From science to management : using Bayesian networks to learn about Lyngbya

Johnson, Sandra, Abal, Eva, Ahern, Kathleen, & Hamilton, Grant (2013) From science to management : using Bayesian networks to learn about Lyngbya. Statistical Science, 29(1), pp. 36-41.

View at publisher (open access)


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.

Impact and interest:

2 citations in Scopus
Search Google Scholar™
2 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

241 since deposited on 18 Nov 2013
198 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 64596
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Bayesian statistics, Bayesian networks, Lyngbya
DOI: 10.1214/13-STS424
ISSN: 0883-4237
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000)
Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000)
Divisions: Current > Schools > School of Earth, Environmental & Biological Sciences
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Institute of Mathematical Statistics
Deposited On: 18 Nov 2013 23:37
Last Modified: 19 Nov 2014 08:42

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page