The dataset consist consists of two (A-B) csv files and one excel file (E). File A shows 1) the absolute percentage total cover and that of specific coral growth forms: Acropora tabular, Acropora branching, other branching species, and massive/submassive species surveyed at 5-9 m depth on 122 reefs (335 reef sites) by the AIMS Long-Term Monitoring Program (LTMP) and Marine Monitoring Program (MMP) between 1992 and 2017 and 2) the long-term average exposure of each reef to variable conditions in water quality (e.g. nutrients, light, salinity, suspended sediments) as well as yearly local (Crown of Thorns Starfish outbreaks-CoTS) and global (heat stress, Cyclones) pressures. File B shows the same cover data mentioned above but only between 2011 and 2017 with the yearly average exposure of each reef to the same environmental conditions mentioned above. The last file E contains the description of the metadata (ie. Data dictionary) for all files (A-D).
Methods:
We used total coral cover and that of four specific coral groupings: Acropora tabular, Acropora branching, other (non-Acropora) branching, and non-Acropora massive/submassive and encrusting species (MSE) to assess the impact of multiple stressors on the Central and Southern GBR regions. Coral cover data for 122 reefs (335 reef sites) were obtained from permanent photo- and video belt transects at 5-9 m depth by the AIMS Long-Term Monitoring Program (LTMP) and Marine Monitoring Program (MMP) between 1992 and 2017.
For the multiple stressors, we collated variables describing the exposure of individual GBR reefs to variable conditions using 1) previous published data (modelled cyclones and heat stress-Degree Heating Weeks (DHW) data from Mathews et al 2019, light at 8m depth from Magno-Canto et al 2019), 2) extracted data from eReefs models specifically for the wet-season (i.e. Dec-April) and at 5m depth (for salinity, chlorophyll a, suspended sediments and Dissolved Inorganic Nitrogen (DIN) https://ereefs.org.au/ereefs), 3) used the AIMS LTMP/MMP datasets (for Crown of Thorns Starfish outbreaks-CoTS) and 4) created new variables based on previous studies (River DIN (DINriv)), from Wolff et al 2018).
Salinity was represented as a cumulative index based on the number of days of exposure to low (<30) salinity (based on Berkelmans et al 2012, by multiplying No. days by the number of salinity units below 30). River DIN, reflects the total river DIN load that enters the system in runoff and reach each reef based on circulation models. To create this metric we considered the yearly (wet-season) volume of discharge (from 1990 to 2018) that influence each reef and the estimated DIN concentrations at each river mouth following Wolff et al. (2018).
Data of CoTS densities from transects (MMP) and manta-tow surveys (LTMP) was combined by standardising transect CoTS densities to manta tow area and correcting for sampling bias due to higher detectability (by a factor of 9.3) on transects compared to manta tows. To predict CoTS on reefs and years with no survey data, we used spatio-temporal Inverse Distance Weighting analysis with the function idwST from the R-package geosptdb (Melo and Melo 2015). Frequency of outbreaks was determined by counting an outbreak whenever CoTS densities were above defined “incipient outbreak” levels of 0.22 individuals per tow.
Frequency of storms and bleaching-risk events (as presence/absence data) were derived from the modelled cyclone and DHW data and complemented with observations from the LTMP and MMP surveys to better represent true occurrence of disturbances. Frequency of storm therefore includes all cyclones captured in models plus the observation of smaller storms as agents of coral mortality. Frequency of bleaching or bleaching-risk mortality events was considered for DHW above 3 and complemented by field data whenever bleaching was recorded as agent of coral mortality during surveys even when DHW levels were below 3.
Exposure to acute disturbances was assigned to each reef and year considering the date of benthic surveys. If the disturbance occurred post-survey for a given year, the disturbance of the previous year was assigned. The frequency of each acute disturbance and its cumulative lagged effects were evaluated using 1, 3 and 5 years periods. These time periods were chosen given the average temporal gap between consecutive surveys for a given reef and the availability of historical data for testing lagged effects. Cumulative effects of all disturbances combined (i.e. CoTS outbreaks + storms + bleaching) were also evaluated. We also categorised reefs based on their management status through the time-series based on their statude pre/post re-zoning in 2004, as always open for fishing (OO), Open-Closed (OC), Closed-Open (CO), or Always closed (CC), to reflect those changes.
Limitations of the data:
The data set only contains information of coral cover at the taxonomic resolution that was of interest for this particular study up until 2017. It does not show coral cover per species/genus. The stressors data is shown only for the selected reefs (not the entire GBR) and represent mostly long-term wet-season averages from the periods described for each variable in the metadata (except River DIN). Most data represent modelled data simulated from regional-wide models and extracted values relate only to a specific depth (i.e. 5 - 8m). Therefore, extracted values can differ if models have been updated since collation of data. For example, eReefs data was extracted from the 4km model v 2.0 and recently updated versions may provide different values.
Format:
This data set consists of 2.csv files and one excel file with reef-site level and yearly data for the coral cover and stress exposure data. The data contains spatial reference (Latitude and Longitude reef-level coordinates and relative position along (Region: Central - South) and across (Shelf position) the GBR. The data set also contains a spreadsheet with the description of the metadata (i.e. data dictionary)
References:
Matthews, S. A., C. Mellin, A. MacNeil, S. F. Heron, W. Skirving, M. Puotinen, M. J. Devlin and M. Pratchett (2019). "High-resolution characterization of the abiotic environment and disturbance regimes on the Great Barrier Reef, 1985–2017." Ecology 100(2): e02574.
Magno-Canto, M. M., L. I. McKinna, B. J. Robson and K. E. Fabricius (2019). "Model for deriving benthic irradiance in the Great Barrier Reef from MODIS satellite imagery." Optics express 27(20): A1350-A1371.
Steven, A. D., M. E. Baird, R. Brinkman, N. J. Car, S. J. Cox, M. Herzfeld, J. Hodge, E. Jones, E. King, N. Margvelashvili, C. Robillot, B. Robson, T. Schroeder, J. Skerratt, S. Tickell, N. Tuteja, K. Wild-Allen and Y. J. (2019). "eReefs: An operational information system for managing the Great Barrier Reef." Journal of Operational Oceanography 12: 1-17
Berkelmans, R., A. M. Jones and B. Schaffelke (2012). "Salinity thresholds of Acropora spp. on the Great Barrier Reef." Coral Reefs 31(4): 1103-1110.
Wolff, N. H., E. T. Da Silva, M. Devlin, K. R. N. Anthony, S. Lewis, H. Tonin, R. Brinkman and P. J. Mumby (2018). "Contribution of individual rivers to Great Barrier Reef nitrogen exposure with implications for management prioritization." Marine pollution bulletin 133: 30-43.
Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.2_Cumulative-impacts\
