50 Reefs Global Coral Ocean Warming, Connectivity and Cyclone Dataset

Abstract

The 50 Reefs analysis (Beyer et al. 2018) was based on metrics associated with five major themes: historical (1985-2017) thermal conditions (13 metrics) (Coral Reef Watch), predicted future thermal condition estimated from general climate model (GCM) projections (8 metrics and 19 GCMs), larval connectivity and settlement (2 metrics) (Wood et al. 2014), cyclone threat (3 metrics) (Carrigan & Puotinen 2011) and recent (previous two summers) thermal conditions (4 metrics). See the attached document for a detailed description and justification of these metrics. The tabular dataset contains values for these 174 metric (columns) for each of the ~54,586 0.05 (approximately 25 km2) degree raster cells (rows) identified as containing coral habitat in the Coral Reef Watch dataset (see attached document for details). These variables were standardised to mean 0 unit variance for the analysis by subtracting the mean and dividing by the standard deviation; the table presents the original non-transformed versions of the data. The table also contains: (1) an ID field that assigns a unique numeric identification number to each row; (2) two fields representing the latitude and longitude of the centre of the cell; and (3) one field ‘score’ representing the aggregate score reported in the 50 Reefs analysis (Beyer et al. 2018). Hence, the table contains 178 fields in total.The geospatial data consists of three shapefiles: (1) CRW_coral_cell_centres.shp: a point dataset representing the centres of the 0.05 degree raster cells that the Coral Reef Watch dataset indicates may contain coral reefs. The key benefit of the point representation is that it facilitates visualisation of the data (each 0.05 is too small to be clearly visualised on a large scale map, but the size of the vector points can be readily adjusted in map-making software). The “ID” field in the attribute table of this shapefile corresponds to the ID values in the tabular dataset above. (2) 50Reefs_BCU_risk_sensitive.shp: a polygon dataset representing the locations of the biolcimatic units (BCUs) identified as the good compromise solution in the 50 Reefs analysis. These are labeled risk-sensitive as they represent a compromise between performance (quantified by exposure to climate change effects and by connectivity) and risk (variation in performance associated with uncertainty in future conditions). The risk-return trade-off was quantified using Modern Portfolio Theory (see Beyer et al. 2018 for details). (3) 50Reefs_BCU_risk_insensitive.shp: a polygon dataset representing the locations of the biolcimatic units (BCUs) identified as the high-performance, high-risk solution in the 50 Reefs analysis. These are labeled risk-insensitive because they do not account for correlations in the variance in uncertainty among BCUs (see Beyer et al. 2018 for details). For most purposes the first and second geospatial datasets will be the useful datasets, while the third geospatial dataset is included only for reference. REVISION: This dataset was revised (15/01/2020) to correct an error in the score metric field in the tabular and geospatial datasets (see PDF documentation for further information).