Statistical Science

From Science to Management: Using Bayesian Networks to Learn about Lyngbya

Sandra Johnson, Eva Abal, Kathleen Ahern, and Grant Hamilton

Full-text: Open access

Abstract

Toxic blooms of Lyngbya majuscula occur in coastal areas worldwide and have major ecological, health and economic consequences. The exact causes and combinations of factors which lead to these blooms are not clearly understood. Lyngbya experts and stakeholders are a particularly diverse group, including ecologists, scientists, state and local government representatives, community organisations, catchment industry groups and local fishermen. An integrated Bayesian network approach was developed to better understand and model this complex environmental problem, identify knowledge gaps, prioritise future research and evaluate management options.

Article information

Source
Statist. Sci., Volume 29, Number 1 (2014), 36-41.

Dates
First available in Project Euclid: 9 May 2014

Permanent link to this document
https://projecteuclid.org/euclid.ss/1399645726

Digital Object Identifier
doi:10.1214/13-STS424

Mathematical Reviews number (MathSciNet)
MR3201844

Zentralblatt MATH identifier
1332.62421

Keywords
Bayesian statistics Bayesian networks Lyngbya

Citation

Johnson, Sandra; Abal, Eva; Ahern, Kathleen; Hamilton, Grant. From Science to Management: Using Bayesian Networks to Learn about Lyngbya. Statist. Sci. 29 (2014), no. 1, 36--41. doi:10.1214/13-STS424. https://projecteuclid.org/euclid.ss/1399645726


Export citation

References

  • Ahern, K. S., Ahern, C. R. and Udy, J. W. (2008). In situ field experiment shows Lyngbya majuscula (cyanobacterium) growth stimulated by added iron, phosphorus and nitrogen. Harmful Algae 7 389–404.
  • Albert, S., O’Neil, J. M., Udy, J. W., Ahern, K. S., O’Sullivan, C. M. and Dennison, W. C. (2005). Blooms of the cyanobacterium Lyngbya majuscula in coastal Queensland, Australia: Disparate sites, common factors. Mar. Pollut. Bull. 51 428–437.
  • Angeli, D., De Leenheer, P. and Sontag, E. D. (2007). A Petri net approach to the study of persistence in chemical reaction networks. Math. Biosci. 210 598–618.
  • Arquitt, S. and Johnstone, R. (2004). A scoping and consensus building model of a toxic blue-green algae bloom. System Dynamics Review 20 179–198.
  • Arthur, K. E., Limpus, C. J., Roelfsema, C. M., Udy, J. W. and Shaw, G. R. (2006). A bloom of Lyngbya majuscula in Shoalwater Bay, Queensland, Australia: An important feeding ground for the green turtle (Chelonia mydas). Harmful Algae 5 251–265.
  • Arthur, K., Limpus, C., Balazs, G., Capper, A., Udy, J., Shaw, G., Keuper-Bennett, U. and Bennett, P. (2008). The exposure of green turtles (Chelonia mydas) to tumour promoting compounds produced by the cyanobacterium Lyngbya majuscula and their potential role in the aetiology of fibropapillomatosis. Harmful Algae 7 114–125.
  • Baesens, B., Verstraeten, G., Van den Poel, D., Egmont-Petersen, M., Van Kenhove, P. and Vanthienen, J. (2004). Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers. European J. Oper. Res. 156 508–523.
  • Bromley, J., Jackson, N. A., Clymer, O. J., Giacomello, A. M. and Jensen, F. V. (2005). The use of Hugin to develop Bayesian networks as an aid to integrated water resource planning. Environmental Modelling Software 20 231–242.
  • Dennison, W., Abal, E. G., Rogers, J., Collier, C., Gaus, C. and South East Queensland Regional Water Quality Management Strategy (1999a). Moreton Bay Study: A Scientific Basis for the Healthy Waterways Campaign. South East Queensland Regional Water Quality Management Strategy, Brisbane, QLD.
  • Dennison, W. C., O’Neil, J. M., Duffy, E. J., Oliver, P. E. and Shaw, G. R. (1999b). Blooms of the cyanobacterium Lyngbya majuscula in coastal waters of Queensland, Australia. Bulletin de l’Institut Oceanographique, Monaco 19 501–506.
  • Hamilton, G., McVinish, R. and Mengersen, K. (2009). Bayesian model averaging for harmful algal bloom prediction. Ecol. Appl. 19 1805–1814.
  • Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N. J., Chung, S., Emili, A., Snyder, M., Greenblatt, J. F. and Gerstein, M. (2003). A Bayesian networks approach for predicting protein–protein interactions from genomic data. Science 302 449–453.
  • Janssens, D., Wets, G., Brijs, T., Vanhoof, K., Arentze, T. and Timmermans, H. (2006). Integrating Bayesian networks and decision trees in a sequential rule-based transportation model. European J. Oper. Res. 175 16–34.
  • Jensen, F. V. and Nielsen, T. D. (2007). Bayesian Networks and Decision Graphs, 2nd ed. Springer, New York.
  • Johnson, S. and Mengersen, K. (2009). A Bayesian network approach to modelling temporal behaviour of Lyngbya majuscula bloom initiation. In 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation (R. S. Anderssen, R. D. Braddock and L. T. H. Newham, eds.). Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, Cairns, Queensland, Australia.
  • Johnson, S. and Mengersen, K. (2012). Integrated Bayesian network framework for modeling complex ecological issues. Integr. Environ. Assess. Manag. 8 480–490.
  • Johnson, S., Fielding, F., Hamilton, G. and Mengersen, K. (2010). An Integrated Bayesian Network approach to Lyngbya majuscula bloom initiation. Mar. Environ. Res. 69 27–37.
  • Johnson, S., Abal, E., Ahern, K. and Hamilton, G. (2014). Supplement to “From science to management: Using Bayesian networks to learn about Lyngbya.” DOI:10.1214/13-STS424SUPP.
  • Kehoe, M., O’Brien, K., Grinham, A., Rissik, D., Ahern, K. S. and Maxwell, P. (2012). Random forest algorithm yields accurate quantitative prediction models of benthic light at intertidal sites affected by toxic Lyngbya majuscula blooms. Harmful Algae 19 46–52.
  • McCann, R. K., Marcot, B. G. and Ellis, R. (2006). Bayesian belief networks: Applications in ecology and natural resource management. Canadian Journal of Forest Research 36 3053.
  • Mengersen, K. and Whittle, P. (2011). Improving accuracy and intelligibility of decisions. Journal für Verbraucherschutz und Lebensmittelsicherheit 6 15–19.
  • Osborne, N. J., Shaw, G. R. and Webb, P. M. (2007). Health effects of recreational exposure to Moreton Bay, Australia waters during a Lyngbya majuscula bloom. Environ. Int. 33 309–314.
  • Osborne, N. J. T., Webb, P. M. and Shaw, G. R. (2001). The toxins of Lyngbya majuscula and their human and ecological health effects. Environ. Int. 27 381–392.
  • Pearl, J. (1985). Bayesian networks: A model of self-activated memory for evidential reasoning. In Seventh Annual Conference of the Cognitive Science Society, Irvine, CA. Univ. California, Irvine.
  • Pittman, S. J. and Pittman, K. M. (2005). Short-term consequences of a benthic cyanobacterial bloom (Lyngbya majuscula Gomont) for fish and penaeid prawns in Moreton Bay (Queensland, Australia). Estuarine, Coastal and Shelf Science 63 619–632.
  • Pointon, S. M., Ahern, K. S., Ahern, C. R., Vowles, C. M., Eldershaw, V. J. and Preda, M. (2008). Modelling land based nutrients relating to Lyngbya majuscula (Cyanobacteria) growth in Moreton Bay, southeast Queensland, Australia. In Thirteenth International Marine Biological Workshop, The Marine Fauna and Flora of Moreton Bay, Queensland 54 (P. J. F. Davie and J. A. Phillips, eds.) 377–390. Memoirs of the Queensland Museum Nature, Brisbane.
  • Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling 203 312–318.
  • Watkinson, A. J., O’Neil, J. M. and Dennison, W. C. (2005). Ecophysiology of the marine cyanobacterium, Lyngbya majuscula (Oscillatoriaceae) in Moreton Bay, Australia. Harmful Algae 4 697–715.

Supplemental materials

  • Supplementary material: Supplementary Figures. Diagrams for the Lyngbya management network and the Lyngbya science Bayesian network are included in the supplemental article to this paper.