Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

Pirathiban, R., Williams, K.J., Pettitt, A.N., & Low-Choy, S.J. (2015) Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models. In The International Biometric Society Australasian Region Conference 2015: BIOMETRICS by the Harbour, 30 November - 3 December 2015, Hobart, Tasmania.

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Abstract

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

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ID Code: 91755
Item Type: Conference Item (Presentation)
Refereed: No
Additional URLs:
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Facilities: Science and Engineering Centre
Copyright Owner: Copyright 2015 [please consult the author]
Deposited On: 11 Jan 2016 23:34
Last Modified: 11 Jan 2016 23:34

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