Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference

CF Dormann; JM Calabrese; G Guillera-Arroita; E Matechou; V Bahn; K Bartoń; CM Beale; S Ciuti; J Elith; K Gerstner; J Guelat; P Keil; JJ Lahoz-Monfort; LJ Pollock; B Reineking; DR Roberts; B Schröder; W Thuiller; DI Warton; BA Wintle; SN Wood; RO Wüest; F Hartig

Journal article

Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference

CF Dormann, JM Calabrese, G Guillera-Arroita, E Matechou, V Bahn, K Bartoń, CM Beale, S Ciuti, J Elith, K Gerstner, J Guelat, P Keil, JJ Lahoz-Monfort, LJ Pollock, B Reineking, DR Roberts, B Schröder, W Thuiller, DI Warton, BA Wintle Show all

Ecological Monographs | WILEY | Published : 2018

DOI: 10.1002/ecm.1309

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Abstract

In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along..

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University of Melbourne Researchers

Gurutzeta Guillera-Arroita Author

Jane Elith Author

Jose Lahoz-Monfort Author

Brendan Wintle Author

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Awarded by European Commission

Funding Acknowledgements

We are grateful to five meticulous reviewers and the editor for providing substantial and constructive feedback, which greatly improved the previous versions of this manuscript. We would like to thank the German Science Foundation (DFG) for funding the workshop "Model averaging in Ecology," held in Freiburg 2-6 March 2015 (DO 786/9-1). Part of this work was carried out during a research stay of C. F. Dormann at the University of Melbourne, co-funded by the DFG (DO 786/10-1). B. Schroder is supported by the DFG (SCHR1000/6-2 and SCHR1000/8-2). D. I. Warton is supported by an Australian Research Council (ARC) Future Fellowship (grant number FT120100501). D. R. Roberts is supported by the Alexander von Humboldt Foundation through the German Federal Ministry of Education and Research. G. Guillera-Arroita is supported by an ARC Discovery Early Career Research Award (project DE160100904). J. Elith is supported by ARC's FT0991640 and DP160101003. J. J. Lahoz-Monfort is supported by Australia's National Environmental Research Program (NERP) Environmental Decisions Hub and ARC DP160101003. K. Barton is supported by a grant from the National Science Center (DEC-2015/16/S/NZ8/00158). K. Gerstner is supported by the German Federal Ministry of Education and Research (BMBF 01LL0901A). K. Gerstner and P. Keil acknowledge funding of iDiv by the DFG (FZT 118). W. Thuiller received funding from the European Research Council under the European Community's FP7/2007-2013 Grant Agreement no. 281422 (TEEMBIO).