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Appendix B Bayesian Network File Creation In order 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 ten 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 an MS Excel table. Here, output years are stored in column 1; scenarios, conditions and interventions then become grouping variables in column 2 to 8 (Figure B.1). Coral cover (the output variable) becomes the last column. Levels within each grouping variable (e.g. RCP 2.6 vs. RCP 8.5) 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) amounting determining the number of rows in the output table (9,600). Figure B.1 Screen capture of MS 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. Second, the database is then converted to a tab-delimited text file and imported into the software Netica (www.norsys.com) using the method described by Ni et al. (2011), but see also Nicol and Chades (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, i.e. using distinct levels, whereas coral cover is a continuous variable (Figure B.2). PREPUBLICATION COPY 153
154 A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs Figure B.2 Screen captures of the properties of an independent (here using AGF as an example) and the dependent node (coral cover) used in Netica. Independent nodes are set to discrete and dependent nodes to continuous. Thirdly, 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 (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 liked in the network. The Bayesian network is then ready to query. Figure B.3 Screen capture showing the route for importing the MATLAB-generated model output file into the Netica software.