Meta-models as a straightforward approach to the sensitivity analysis of complex models

Coutts, S. R. and Yokomizo, H. (2014) Meta-models as a straightforward approach to the sensitivity analysis of complex models. Population Ecology, 56 1: 7-19. doi:10.1007/s10144-013-0422-1

Author Coutts, S. R.
Yokomizo, H.
Title Meta-models as a straightforward approach to the sensitivity analysis of complex models
Journal name Population Ecology   Check publisher's open access policy
ISSN 1438-3896
Publication date 2014-01-01
Year available 2013
Sub-type Article (original research)
DOI 10.1007/s10144-013-0422-1
Open Access Status Not yet assessed
Volume 56
Issue 1
Start page 7
End page 19
Total pages 13
Place of publication Tokyo, Japan
Publisher Springer
Language eng
Subject 1105 Ecology, Evolution, Behavior and Systematics
Abstract Complex simulation models are important tools in applied ecological and conservation research. However sensitivity analysis of this important class of models can be difficult to conduct. High level interactions and non-linear responses are common in complex simulations, and this necessitates a global sensitivity analysis, where each parameter is tested at a range of values, and in combination with changes in many other parameters. We reviewed the literature, searching for population viability analyses that used simulation models. We found only 9 out of the 122 simulation population viability analysis used global sensitivity analysis. This result is typical of other simulation models in applied ecology, where global sensitivity analysis is rare. We then demonstrate how to conduct a meta-modeling sensitivity analysis, where a simpler statistically fit function (the meta-model, also known as the surrogate model or emulator) is used to approximate the behavior of the complicated simulation. This simpler meta-model is interrogated to inform on the behavior of simulation model. We fit two example meta-models, a generalized linear model and a boosted regression tree, to exemplify the approach. Our hope is that by going through these techniques thoroughly they will become more widely adopted.
Keyword Boosted regression trees
Generalized linear models
Population viability analyses
Simulation model
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 5 December 2013.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Biological Sciences Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 14 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 07 Jan 2014, 10:27:58 EST by System User on behalf of School of Biological Sciences