Bayesian model averaging for harmful algal bloom prediction

Hamilton, Grant, McVinish, Ross and Mengersen, Kerrie (2009) Bayesian model averaging for harmful algal bloom prediction. Ecological Applications, 19 7: 1805-1814. doi:10.1890/08-1843.1

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Author Hamilton, Grant
McVinish, Ross
Mengersen, Kerrie
Title Bayesian model averaging for harmful algal bloom prediction
Journal name Ecological Applications   Check publisher's open access policy
ISSN 1051-0761
Publication date 2009-10
Sub-type Article (original research)
DOI 10.1890/08-1843.1
Open Access Status File (Publisher version)
Volume 19
Issue 7
Start page 1805
End page 1814
Total pages 10
Editor David S. Schimel
J. David Baldwin
Place of publication Washington, DC, United States
Publisher Ecological Society of America
Collection year 2010
Language eng
Formatted abstract
Harmful algal blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations, and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been developed, and in common with most predictive models in ecology, those that have been developed do not fully account for uncertainties in parameters and model structure. This makes management decisions based on these predictions more risky than might be supposed. We used a probit time series model and Bayesian model averaging (BMA) to predict occurrences of blooms of Lyngbya majuscula, a toxic cyanophyte, in Deception Bay, Queensland, Australia. We found a suite of useful predictors for HAB occurrence, with temperature figuring prominently in models with the majority of posterior support, and a model consisting of the single covariate, average monthly minimum temperature, showed by far the greatest posterior support. A comparison of alternative model averaging strategies was made with one strategy using the full posterior distribution and a simpler approach that utilized the majority of the posterior distribution for predictions but with vastly fewer models. Both BMA approaches showed excellent predictive performance with little difference in their predictive capacity. Applications of BMA are still rare in ecology, particularly in management settings. This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty.
Keyword Bayesian model averaging
Deception Bay, Queensland, Australia
harmful algal bloom (HAB)
Lyngbya majuscula
Occam's window
predictive model
receiver operator characteristic (ROC) curve
reversible jump Markov chain Monte Carlo (RJMCMC)
coastal waters
Moreton Bay
Q-Index Code C1
Q-Index Status Confirmed Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
2010 Higher Education Research Data Collection
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Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 17 times in Scopus Article | Citations
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Created: Fri, 19 Feb 2010, 15:59:41 EST by Kay Mackie on behalf of School of Mathematics & Physics