A Bayesian Belief Network to assess rate of changes in coral reef ecosystems
Open accessAbstract
It is crucial to identify sources of impacts and degradation to maintain functions and services that the physical structure of coral reef provides. Here, a Bayesian Network approach is used to evaluate effects that anthropogenic and climate change disturbances have on coral reef structure. The network was constructed on knowledge derived from the literature and elicited from experts, and parameterised on independent data. Evaluation of the model was conducted through sensitivity analyses and data integration was fundamental to obtain a balanced dataset. Scenario analyses, conducted to assess the effects of stressors on the reef framework state, suggested that calcifying organisms and carbonate production, rather than bioerosion, had the largest influence on the reef carbonate budgetary state. Despite the overall budget remaining positive, anthropogenic pressures, particularly deterioration of water quality, affected reef carbonate production, representing a warning signal for potential changes in the reef state.
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The study revealed results identifying the factor that contributed most to the carbonate budgetary state of the reef and concluded that the change in the state of the reef was mainly due to the decrease in water quality.
“The study revealed interesting results identifying the factor that contributed the most to the carbonate budgetary state of the reef and concluded that the change in the state of the reef was mainly due to the decrease in water quality (Franco et al., 2016).”
From Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action (2022) · cites this paper as Franco et al., 2016
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Callaghan, D. P., Baldock, T. E., Shabani, B., & Mumby, P. J. (2018) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2018.07.021 · Cited by 13 · see citing works
Bayesian networks for evaluating climate change influence in olive crops in Andalusia, Spain
Ropero, R. F., Rumí, R., & Aguilera, P. A. (2018) · Natural Resource Modeling · doi:10.1111/nrm.12169 · Cited by 11 · see citing works
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Lim, R. B. H., Liew, J. H., Kwik, J. T. B., & Yeo, D. C. J. (2018) · Ecological Modelling · doi:10.1016/j.ecolmodel.2018.06.017 · Cited by 8 · see citing works
Adapting to extreme environments: can coral reefs adapt to climate change?
Crabbe, M. J. C. (2019) · Emerging Topics in Life Sciences · doi:10.1042/etls20180088 · Cited by 8 · see citing works
Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor
Li, D., Wang, H., & Liang, X. (2019) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-019-2047-9 · Cited by 4 · see citing works
Simonetti, F., Ciuccoli, N., Ankan, A., Mancini, M., Castellani, M., Sgroi, M., Fatone, F., & Migliorati, V. (2025) · Water Research · doi:10.1016/j.watres.2025.125133 · Cited by 2 · see citing works
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Impact of learning algorithms on gene expression data set
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Predicting Climate Change Impacts on Agriculture in the Southwest United Kingdom
Jackson, J. A., Stafford, R., Cvitanović, M., & Cantarello, E. (2025) · Sustainability · doi:10.3390/su17093798
- Authors
- Franco, C., Hepburn, L. A., Smith, D. J., Nimrod, S., & Tucker, A.
- Affiliations
- University of Essex (GB)
- St. George's University (GD)
- Brunel University of London (GB)
- Corresponding author
- Tucker, Allan
- Venue
- Environmental Modelling & Software
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 80
- Pages
- 132-142
- ISSN
- 1364-8152 (ISSN-L)
- OpenAlex
- W2290546054
- Cited by
- 25
- References
- 90
- Citations by year
- 2025: 2 · 2023: 1 · 2021: 3 · 2020: 4 · 2019: 9 · 2018: 4 · 2017: 2
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Environmental Modelling & Software (publishedVersion, cc-by-nc-nd)
- Open Access at Essex (University of Essex) (acceptedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by-nc-nd)
- Funders
- St. George's, University of London
- Topics
- Physical Sciences › Environmental Science › Ecology › Coral and Marine Ecosystems Studies
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Coastal and Marine Management
- Physical Sciences › Earth and Planetary Sciences › Earth-Surface Processes › Coastal and Marine Dynamics
- Keywords
- coral reef degradation, coral reef structure, anthropogenic impacts, climate change impacts, carbonate budget, calcifying organisms, bioerosion, water quality degradation, expert elicitation, scenario analysis, ecosystem services
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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