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Suggested Citation:"Appendix B: Bayesian Network File Creation." National Academies of Sciences, Engineering, and Medicine. 2019. A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs. Washington, DC: The National Academies Press. doi: 10.17226/25424.
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Appendix B

Bayesian Network File Creation

To analyze the outputs of the biophysical model in a Bayesian network, numerical outputs of coral cover are translated to likelihood data in three steps.

First, in the MATLAB program, decadal time slices of the output data are taken. In other words, projected coral cover (replicates of 10 data points per line in the intervention table) are captured at years 2020, 2030, 2040, 2050, and 2060. Outputs are then organized according to the design table and exported to a Microsoft (MS) Excel table. Here, output years are stored in column 1; scenarios, conditions, and interventions then become grouping variables in columns 2 to 8 (see Figure B.1). Coral cover (the output variable) becomes the last column. Levels within each grouping variable (e.g., representative concentration pathway [RCP]2.6 versus RCP8.5) are stacked vertically, with the number of replicate model runs (10) for each line in the intervention table (192) and numbers of time slices (5) determining the number of rows in the output table (9,600).

Second, the database is then converted to a tab-delimited text file and imported into the software Netica using the method described by Ni et al. (2011), but see also Nicol and Chadès (2017). Briefly, in preparation for data import into Netica, an empty Bayesian network must be constructed with all independent (parent) nodes completed with titles and level descriptions identical to column headers and row information in the MS Excel file. All parent nodes should be set to discrete, that is, using distinct levels, whereas coral cover is a continuous variable (see Figure B.2).

Suggested Citation:"Appendix B: Bayesian Network File Creation." National Academies of Sciences, Engineering, and Medicine. 2019. A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs. Washington, DC: The National Academies Press. doi: 10.17226/25424.
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Image
FIGURE B.1 Screen capture of Microsoft Excel database of coral cover output data produced by the MATLAB model. The database is subsequently imported into the software Netica for the analysis of conditional likelihoods.

Third, the data file is imported into Netica via a learning algorithm. This process is built into Netica and can be run by importing the data file via the Cases tab in the menu line, then Learn, then Incorporate Case File (see Figure B.3). In the subsequent menu, the user is asked to browse for the data file. Upon import, Netica converts the distributions of coral cover within and among years, scenarios, and interventions to distributions of conditional likelihoods linked in the network. The Bayesian network is then ready to query.

Suggested Citation:"Appendix B: Bayesian Network File Creation." National Academies of Sciences, Engineering, and Medicine. 2019. A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs. Washington, DC: The National Academies Press. doi: 10.17226/25424.
×
Image
FIGURE B.2 Screen captures of the properties of an independent (here using assisted gene flow as an example, top) and the dependent node (coral cover, bottom) used in Netica. Independent nodes are set to discrete and dependent nodes to continuous.
Suggested Citation:"Appendix B: Bayesian Network File Creation." National Academies of Sciences, Engineering, and Medicine. 2019. A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs. Washington, DC: The National Academies Press. doi: 10.17226/25424.
×
Image
FIGURE B.3 Screen capture showing the route for importing the MATLAB-generated model output file into the Netica software.
Suggested Citation:"Appendix B: Bayesian Network File Creation." National Academies of Sciences, Engineering, and Medicine. 2019. A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs. Washington, DC: The National Academies Press. doi: 10.17226/25424.
×
Page 195
Suggested Citation:"Appendix B: Bayesian Network File Creation." National Academies of Sciences, Engineering, and Medicine. 2019. A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs. Washington, DC: The National Academies Press. doi: 10.17226/25424.
×
Page 196
Suggested Citation:"Appendix B: Bayesian Network File Creation." National Academies of Sciences, Engineering, and Medicine. 2019. A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs. Washington, DC: The National Academies Press. doi: 10.17226/25424.
×
Page 197
Suggested Citation:"Appendix B: Bayesian Network File Creation." National Academies of Sciences, Engineering, and Medicine. 2019. A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs. Washington, DC: The National Academies Press. doi: 10.17226/25424.
×
Page 198
A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs Get This Book
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Coral reefs are critical to ocean and human life because they provide food, living area, storm protection, tourism income, and more. However, human-induced stressors, such as overfishing, sediment, pollution, and habitat destruction have threatened ocean ecosystems globally for decades. In the face of climate change, these ecosystems now face an array of unfamiliar challenges due to destructive rises in ocean temperature, acidity and sea level. These factors lead to an increased frequency of bleaching events, hindered growth, and a decreasing rate of calcification. Research on interventions to combat these relatively new stressors and a reevaluation of longstanding interventions is necessary to understand and protect coral reefs in this changing climate. Previous research on these methods prompts further questions regarding the decision making process for site-specific interventions.

A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs builds upon a previous report that reviews the state of research on methods that have been used, tested, or proposed to increase the resilience of coral reefs. This new report aims to help coral managers evaluate the specific needs of their site and navigate the 23 different interventions described in the previous report. A case study of the Caribbean, a region with low coral population plagued by disease, serves as an example for coral intervention decision making. This report provides complex coral management decision making tools, identifies gaps in coral biology and conservation research, and provides examples to help individuals and communities tailor a decision strategy to a local area.

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