This generated data set contains summaries (daily, monthly, annual) of the eReefs CSIRO hydrodynamic model v2.0 (https://research.csiro.au/ereefs) outputs at both 1km and 4km resolution, generated by the AIMS eReefs Platform (https://ereefs.aims.gov.au/ereefs-aims). These summaries are derived from the original hourly model outputs available via the National Computing Infrastructure (NCI) (https://dapds00.nci.org.au/thredds/catalogs/fx3/catalog.html), and have been re-gridded from the original curvilinear grid used by the eReefs model into a regular grid so that the data files can be easily loaded into standard GIS software. These summaries are updated in near-real time (daily) and are made available via a THREDDS server (http://thredds.ereefs.aims.gov.au/thredds/catalog.html) in NetCDF format. For more information about the eReefs hydrodynamic modelling see https://research.csiro.au/ereefs/models/models-about/models-hydrodynamics/. # Method: A description of the processing, especially aggregation and regridding, is available in the "Technical Guide to Derived Products from CSIRO eReefs Models" document (https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf). # Data Dictionary: The following variables are available: - eta: Surface elevation (sea surface height above sea level) (metres) - salt: Salinity (PSU) - temp: Temperature (degrees C) - wspeed_u: Eastward wind (ms-1) - wspeed_v: Northward wind (ms-1) - u: Eastward sea water velocity (second half of the current vector) (ms-1) - v: Northward sea water velocity (half of the current vector) (ms-1) - mean_wspeed: Mean Wind speed magnitude (ms-1) - mean_cur: Mean sea water velocity (current) magnitude (ms-1) # FAQ: ## Why is the mean_cur not equal to the sqrt(u^2+v^2)? The mean_cur is not equal to the sqrt(u^2 + v^2) due to the aggregation process. The instantaneous (raw hourly) u and v values take both positive and negative values as the current changes direction. When these values are averaged over a day or a month, the positive and negative swings tend to cancel out, removing most of the tidal signal. As a result, the aggregate u and v values only show the net current flow. The mean current is different because the current value corresponds to the magnitude of the current, calculated on the hourly data with sqrt(u^2 + v^2). This is then averaged over time. Since the current magnitude is always a positive number there is no ‘averaging out’ of the tidal currents and thus the average is much higher. The daily u and v values show the net current flow (cancelling out any back and forth movement), the mean_cur shows the average current magnitude, i.e. average strength of the net flows and the tidal flows over the period. # How does the regridding work? What algorithm is used? The raw eReefs model (available via NCI) uses a curvilinear grid to minimise the number of simulation cells. This grid format is incompatible with many GIS applications. The products available from the AIMS eReefs THREDDS server are regridded on to a regular rectangular grid. This regridding is performed using an Inverse Distance Weight from the nearest 4 grid cells. A consequence of this approach is that there are an additional set of interpolated pixels along the coastline. Additional detail on the regridding is available in the "Technical Guide to Derived Products from CSIRO eReefs Models". # Depths: This data set contains a subset of the depths available in the source data sets, which differ slightly between the 1km and 4km models. Depths at 1km resolution: -0.5, -2.35, -5.35, -9.0, -13.0, -18.0, -24.0, -31.0, -39.5, -49.0, -60.0, -73.0, -88.0, -103.0, -120.0, -140.0, Depths are 4km resolution:-0.5, -1.5, -3.0, -5.55, -8.8, -12.75, -17.75, -23.75, -31.0, -39.5, -49.0, -60.0, -73.0, -88.0, -103.0, -120.0, -145.0. Limitations: The wind data is originally from the BOM Access-R weather models. These models capture synoptic winds and some of the features of cyclones, however they do not represent the high speed winds near the eye of cyclones well. For this reason the maximum wind speed aggregations do not capture the peak winds of cyclones. The GBR1 yearly summaries of salinity and temperature have a known problem where the data is WRONG with a sharp boundary appearing at edge of the GBR. Do not use this data. We will investigate and resolve the issue with this data. This dataset is based on a spatial and temporal model and as such is an estimate of the environmental conditions. It is not based on in-water measurements, and thus will have a spatially varying level of error in the modelled values. It is important to consider if the model results are fit for the intended purpose.

The dataset contains the particle size distribution analysis (on a Malvern Mastersizer 3000 laser diffraction) and major and trace element geochemistry data for end of river and flood plume samples from the 2017, 2018 and 2019 Burdekin flood events as well as for the 2017 and 2018 Tully flood events. Additional data are provided from the Ross River, Haughton River and Johnstone River. * This dataset is currently under embargo. The grain size data were analysed following treatment with H2O2 which is designed to remove the organic material from the samples. Hence the data should be considered as a ‘treated grain size’ according to the protocols recommended in the Bainbridge et al. (in review) manuscript. The dataset represents the first time end of river and plume samples have been treated to examine the primary grain size. In addition, these are the first samples analysed from the > 25 PSU zone in the plume and samples > 10 PSU for grain size and geochemistry were also rare. Bainbridge, Z. Lewis, S. Stevens, T. Petus, C. Lazarus, E. Gorman, J. Smithers, S. in review. Measuring sediment grain size across the catchment to reef continuum: Improved methods and environmental insights. Marine Pollution Bulletin. Methods: Our dataset includes end-of-river (EoR) suspended sediment samples from the Burdekin and Tully Rivers captured during high flow events that occurred over the 2016-2017 and 2017-2018 water years, and also the 2018-2019 water year for the Burdekin. Opportunistic, representative samples were also collected from the neighbouring South Johnstone River (Wet Tropics) in 2018, and the Herbert (Wet Tropics), Ross and Haughton (Dry Tropics) Rivers in 2019. In total 15 samples were analysed for the Burdekin, three for the Tully, two for the Ross and one each for the neighbouring South Johnstone, Herbert and Haughton Rivers. The Herbert River sample was a composite of daily sampling across the discharge peak from 4th to 7th February, 2019. Samples were collected off bridges with a 10 L container. Flood plume sampling along the estuarine salinity gradient from the river mouth was conducted immediately following EoR sample collection. Given the larger size and duration of the Burdekin flood plume, sample sites targeted the movement of the plume over a number of days guided by near real-time MODIS satellite imagery. Sites were located along a salinity gradient transect extending from the freshwater reaches of the estuary and sites were selected to coincide with the environmental instrument arrays installed at Orchard Rocks, Havannah Island and Dunk Island. Flood plume samples were collected using the SediPump® high-volume filtration system to ensure adequate samples sizes to complete the analyses. Pump durations at each site were 2-3 hours, which includes the time to collect the depth samples (1-2m above local benthic depth). For further details on the grain size analysis see: Bainbridge, Z. Lewis, S. Stevens, T. Petus, C. Lazarus, E. Gorman, J. Smithers, S. in review. Measuring sediment grain size across the catchment to reef continuum: Improved methods and environmental insights. Marine Pollution Bulletin. Limitations of the data: The sample pre-treatment of the grain size data need to be understood where H2O2 was used to remove the organics from the sample. Format: The data are provided as a Microsoft Excel file. References: Lewis, S., Bainbridge, Z. Stevens, T., Garzon-Garcia, A., Chen, C., Bahadori, M., Burton, J., James, C., Smithers, S. and Olley, J. (2020) What’s really damaging the Reef?: Tracing the origin and fate of the environmentally detrimental sediment and associated bioavailable nutrients. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (250pp.). Bainbridge, Z. Lewis, S. Stevens, T. Petus, C. Lazarus, E. Gorman, J. Smithers, S. in review. Measuring sediment grain size across the catchment to reef continuum: Improved methods and environmental insights. Marine Pollution Bulletin. Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.8_Origin-detrimental-sediment

This dataset summarises the results of a survey to determine the concentration of trace metals from mine-derived pollution in marine waters and sediments across the Torres Strait during October 2016. Sampling was performed by a CSIRO team between 3 and 16 October 2016 on board the MV Eclipse. Surface water samples were collected from 21 sites using strict sampling protocols that are designed to minimise contamination (USEPA, 1996; Angel et al., 2010b). METHODS: - Sample Collection Clean powder-free vinyl gloves were worn for the handling of all sample bottles and sampling equipment, and the collection of water samples before sediment samples at any given site. Acid washed sampling bottles (0.5, 1, and 5 L), double-bagged in zip-lock bags and stored inside an esky containing ice bricks was transported on the tender to each site. The 0.5 L bottle was used to collect a sample for total mercury analysis. The 1 L bottle was used for collecting a sample for total recoverable metals analyses other than mercury, and the 5 L bottle was used for collecting a sample for filterable (dissolved) and TSS-bound metals analyses other than mercury. At every sampling site a ‘clean hands’, ‘dirty hands’ protocol was used for taking water samples. This involved the ‘clean hands’ person opening the esky, placing gloves on hands, withdrawing the 1 L sample bottle from pre-labelled zip-lock bags, placing it into an attachment on a purpose built Perspex pole sampler, uncapping the bottle and holding onto the cap. The ‘dirty hands’ person then rapidly submerged the bottle in the pole sampler to a depth of approximately 50 cm to take the sample. Each sample bottle was rinsed twice with water from the sample site by filling each bottle, capping, shaking and emptying. The 1 L bottle was used to collect water samples that were decanted into the 0.5 and 5 L bottles until they were full of sample, after which the 1 L bottle was filled a final time. The ‘clean hands’ person capped each bottle once they well filled and replaced them into the zip-lock bags in the esky. The water samples were placed into a fridge on board the MV Eclipse prior to filtration. The samples were filtered within 6 hours of sample collection. For quality control purposes, field blanks were collected at sites M, O and 8 and duplicate samples were collected at sites N, O and A. Field blanks for trace metals analysis were prepared at the designated sites by opening a 1 L bottle to the air for approximately 30 seconds followed by capping and returning to its zip-lock bag. On return to the MV Eclipse, the bottle was then filled with 1 L of deionised water. Salinity and pH were measured using an Orion Star A329 portable meter (Thermo Scientific). Sample pH was measured using a Thermo Scientific Orion Gel-Filled ROSS pH Ultra Triode Electrode (8107UWMMD) that was calibrated using pH 4.00, 7.00 and 10.00 buffers. Salinity was measured using a Thermo Scientific Orion Conductivity Cell (013010MD) that was calibrated using KCl conductivity standards. Sediment samples were collected from each site immediately after the water sampling. A combination of techniques were employed to collect the sediment samples that depended on the local water current conditions and ability of the corer to penetrate the sediment. Firstly, a gravity core sampler was deployed from the Eclipse, which collected up to 12 cm deep sediment within pre-loaded plastic core tubes. If this was unsuccessful divers took hand cores of up to 7 cm depth by diving to the sea bed. The core tubes were capped with plastic stoppers and wherever possible, returned to the surface in an upright position. If the substrate was too hard for hand coring, the divers took a grab of loose sediment samples by hand inside 250 mL polycarbonate vials. The core tubes were withdrawn from the corer on-board the MV Eclipse, placed into zip-lock bags, and placed inside a freezer until frozen. The cores were then sectioned by allowing a core to partially thaw so that the sediment core could be extruded with a plastic plunger, before cutting into sections (typically 1-2 cm length) with a plastic blade. The core sections were placed into zip-lock bags and stored frozen for transport to the Lucas Heights laboratories. The contents of some of the shorter unconsolidated sediment cores became mixed, in which case the entire core was treated as a single sample rather than sub-sectioning. Triplicate cores/sediment grabs were generally taken at each site in order to assess sampling heterogeneity. - Water sample processing Water samples for analysis of trace metals were vacuum filtered through acid-washed 0.45 µm Millipore membrane filters using an acid washed polycarbonate filtration apparatus (Sartorius). The filtration assemblies were further cleaned before processing each sample by first filtering a 100 mL volume of 10% v/v nitric acid solution followed by two 150 mL volumes of deionised water, and finally, a 50 mL volume of sample. For each volume of these solutions the filtration rig was held on an angle and rotated both before and after filtration so that the solutions came into contact with all surfaces of the top and bottom compartments of the apparatus to ensure rigorous rinsing / pre-treatment was achieved. The 50 mL aliquot of sample used to pre-clean the filtration rig was poured into the 1 L acid washed Nalgene filtrate receiving bottle, shaken to pre-treat the bottle, and discarded to waste. The sample was then filtered and the filtrate transferred into the receiving bottle. Between 4-6 L of each sample filtrate was retained for analysis. Filtrates were then preserved by addition of 2 mL/L of concentrated nitric acid (Merck Tracepur). For the field blanks, approximately half of the 1L sample was filtered and preserved. The remaining 500 mL was acidified and retained for subsequent analysis. The difference between the filtered and unfiltered field blanks gave an indication if filtration resulted in contamination. Suspended sediment samples for total suspended sediment (TSS) and TSS-bound metals analyses were acquired by filtering known volumes of water through pre-weighed 0.45 µm membrane filters (Millipore). The filters were rinsed with 10% nitric acid before use and each sample was filtered using the filtration procedure described above. After the sample was filtered and the filtrate removed, the upper compartment of the filtration apparatus and the filter were rinsed with approximately 20 mL of deionised water to remove any salt. The filters were placed into acid-washed plastic Petri slides and stored frozen. The filters were transferred to the CSIRO Lucas Heights laboratories, after which they were oven-dried at 60oC, cooled to room temperature in a desiccator, and weighed. This procedure was repeated three times to ensure the mass was consistent, after which, the filters were stored at room temperature until total recoverable (TR) metals analysis was performed. The TSS concentration (mg/L) of the water samples was calculated using the difference in the mass of the filter before and after filtration divided by the volume of sample filtered. - Analysis of dissolved metals Dissolved Cd, Co, Cu, Ni, Pb and Zn in filtered samples were analysed by complexation and solvent extraction prior followed by quantitation of the pre-concentrated metals by ICPMS. The extraction procedure allowed the pre-concentration of metals by a factor of 25, thus allowing more accurate quantification. A dithiocarbamate complexation/solvent extraction method based on the procedure described by Magnusson and Westerlund (1981) was employed. The major differences were the use of a combined sodium bicarbonate buffer/ammonium pyrrolidine dithiocarbamate reagent (Apte and Gunn, 1987) and 1,1,1-trichloroethane as the extraction solvent in place of Freon. In brief, sample aliquots (250 mL) were buffered to pH 5 by the addition of the combined reagent and extracted into two 10-mL portions of triple-distilled trichloroethane. The extracts were combined and the metals back-extracted into 1 mL of concentrated nitric acid (Merck Tracepur). The back extracts were diluted to a final volume of 10 mL by addition of deionised water and analysed by inductively coupled plasma-mass spectrometry (ICPMS) (Agilent, 7500CE) using the instrument operating conditions recommended by the manufacturer. For quality control purposes a portion of the certified reference seawater NASS-6 (National Research Council (NRC), Canada) CRM was analysed in every sample batch. Dissolved aluminium and iron concentrations were measured directly on portions of acidified filtered waters by ICP-AES (Varian730 ES) using matrix-matched standards. The concentrations of dissolved chromium were measured directly by ICP-MS (Agilent 7500CE ) following three-fold dilution with deionised water and calibration against matrix-matched standards. The concentration of dissolved arsenic in the filtered samples was measured by hydride generation atomic absorption spectrometry (HG-AAS), using procedures based on the standard methods described by APHA (1998). Samples were first digested by addition of potassium persulfate (1% m/v final concentration) and heating to 120°C for 30 min in an autoclave. Hydrochloric acid, (3 M final concentration) was then added to the samples. Pentavalent arsenic was then pre-reduced to arsenic (III) by addition of potassium iodide (1% (m/v) final concentration) and ascorbic acid (0.2% (m/v) final concentration) and left standing for at least 30 min at room temperature prior to analysis. Arsenic concentrations were then measured by HG-AAS using a Varian VGA system operated under standard conditions recommended by the manufacturer. Arsenic (III) in solution was reduced to arsine by reduction with sodium borohydride, which was stripped from solution with argon gas into a silica tube, electrically heated at 925°C. Heating converted arsine into arsenic vapour, which was quantified by atomic absorption spectrometry. For quality control purposes a portion of the certified reference seawater NASS-6 (National Research Council (NRC), Canada) CRM was analysed in every sample batch. DOC was measured on aliquots of filtered samples collected during the June 2018 survey using a Shimadzu TOC-LCSH Total Organic Carbon Analyser using the procedures recommended by the manufacturer. - Analysis of metals bound to total suspended solids (TSS) and benthic sediment The TSS and benthic sediment was digested in pre-cleaned Teflon digestion vessels using aqua-regia digestions in a microwave-assisted reaction system (MARS). The membrane filters containing the suspended sediments or known quantities of dry benthic sediment were transferred into the MARS digestion vessels and subjected to pressurised digestion. The method involved adding 2.5 mL of concentrated nitric acid (Tracepur, Merck) and 7.5 mL of concentrated hydrochloric acid (Tracepur, Merck) to each digestion vessel and heating at high pressure in a MARS digestion system for 90 minutes. Once cool, the digest vessels were vented followed by dilution of the digest to a final volume of 40 mL using deionised water. The masses of the empty vessel, the vessel plus sample, and the vessel plus sample and acid mixture before and after heating were recorded to allow calculation of a dilution factor used in the determination of metal concentrations in the initial undiluted sample. For quality control purposes, portions of the certified reference sediments ERM-CC018 (IRMM) and PACS-3 (NRC Canada) were analysed in each sample batch. Format: This dataset consists of multiple Comma Separated Value (CSV) tables containing the data provided by the project team. Data Location: This dataset is filed in the eAtlas enduring data repository at: data\NESP-TWQ-2\2.2.2_TS-mine-pollution

This dataset consists of one spreadsheet, which shows the survival, number of polyps, size, Symbiodinium quantity and photosynthetic efficiency (Fv/Fm) of up to four-month-old Acropora millepora coral recruits while being exposed to three different climate scenarios resembling current climate conditions and conditions expected by mid and end of the century. Coral recruit resilience towards one-month-long light attenuation with five intensities was tested by exposing the recruits either one (experiment 1) or two months (experiment 2) following settlement. Additional tabs show temperature and pCO2 measured throughout the experiment. The experiment had 45 tanks made up from 15 climate (3) - light (5) combinations with 3-times replication. The temperature and pCO2 corresponding to the water going into the main lines feeding the 15 tanks in each climate combination were logged and are available in the dataset. The study was conducted at the National Sea Simulator. The aim of this study was to 1) identify the survival of coral recruits under simultaneous exposure to climate stress (temperature and pCO2) and light attenuation and 2) identify survival mechanisms (i.e., number of polyps, Symbiodinium quantities, photosynthetic efficiency). The climate scenarios (average of logged conditions) tested were: - `Current`: temperature: 28.46 deg C, pCO2: 428 ppm - `Medium`: temperature: 28.95 deg C, pCO2: 692 ppm - `High`: temperature: 29.56 deg C, pCO2: 985 ppm The following light levels were tested for each of the climate scenarios : Maximum day time PAR: 3, 15, 31, 62, 124 (umol photons m-2 s-1) and matching Daily Light Integral of 0.1, 0.5, 1, 2, 4 (mol photons m-2 d-1). This data will inform the development of water-quality management guidelines, a key aim of NESP project 5.2. The full research report can be found at: Brunner CA, Ricardo GF, Uthicke S, Negri AP, Hoogenboom MO. 2022. Effects of climate change and light limitation on coral recruits. Marine Ecology Progress Series # Methods: Coral recruits of Acropora millepora, a branching coral species abundant in shallow reefs on the Great Barrier Reef, were raised for 4 months in `current` and realistic `medium` and `high` climate scenarios (increased temperature and acidification), and were exposed to five environmentally relevant light attenuation levels typical of flood plumes and dredging operations. The one-month-long light attenuation events were simulated at different recruit ages: (1) one month following settlement, and (2) two months following settlement. After a four-week recovery phase, survival, polyp numbers, size, Symbiodinium quantities and the photosynthetic efficiency (Fv/Fm) were documented, and the data are presented here. Specific details of the methodology may be found in: Brunner CA, Ricardo GF, Uthicke S, Negri AP, Hoogenboom MO. 2022. Effects of climate change and light limitation on coral recruits. Marine Ecology Progress Series # Limitations: Note that the dataset only includes data for recruits that did not fuse with neighbouring recruits throughout the study. No data gathered where cells are empty. Mortality was observed even in the `Current` (control) climate treatment. # Format: Excel Spreadsheet with 3 tables (`Experiment`: 16 columns x 12332 rows, `Temperature`: 5 columns x 26600 rows, `pCO2`: 5 rows x 14544 rows). # Data Dictionary: - `experiment`: 1: one-month light attenuation one month after settlement, 2 one-month light attenuation two months after settlement - `climate`: climate condition that each tank was subjected to: `Current`, `Medium` and `High` conditions. - `age_month`: age in months following settlement climate = climate treatment based on manipulated temperature and pCO2, see "Temperatures" and "pCO2" tab for details - `light_PAR`: light attenuation in photosynthetic active radiation (umol photons m-2 s-1) during the attenuation period. The PAR specified corresponds to the maximum light intensity during a day. - `light_DLI`: light attenuation as Daily Light Integral (mol photons m-2 d-1) during the attenuation period. This corresponds to the total accumulated light received over the day. During the night light was 0. Between 6 am and 9 am light ramped up to the maximum value (light_PAR). It stayed at this level during the day and ramped down to zero from 3 pm to 6 pm. - `tank_ID`: identification number of climate controllable aquarium - `disc_ID`: identification number of discs where coral recruits settled on - `recruit_ID_on_disc`: identification number of each recruit on each disc - `polyps_alive`: number of alive polyps of each recruit, a dead recruit has 0 alive polyps. Polyps were counted so that we can could standardise the counts of Symbiodinium to Symbiodinium / polyp - `recruit_alive`: 1: coral recruit is alive, 0: coral recruit is dead - `recruitarea_tota`: total recruit size in mm² including dead and alive areas - `recruitarea_alive`: alive recruit area in mm² - `recruitarea_bleached`: bleached recruit area in mm² - `recruitarea_cca`: recruit area in mm² overgrown with crustaceous corallin algae (CCA) - `symbionts_per_polyp`: Symbiodinium cells per alive polyp - `FvFm`: dark adapted photosynthetic efficiency # References: Brunner CA, Ricardo GF, Uthicke S, Negri AP, Hoogenboom MO. 2022. Effects of climate change and light limitation on coral recruits. Marine Ecology Progress Series Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.2_Cumulative-impacts\data\2022-03-23-recruit-climate-light

Data is from a series of instruments, including temperature loggers, weather stations and turbidity / salinity loggers deployed at four sites in the Torres Strait (Thursday Island – logger + weather station), Masig Island (logger + weather station), Bramble Cay (Maizab Kaur – logger + weather station) and the northern Warrior Reefs (turbidity and salinity) as part of a larger project to investigate the impact of Fly River discharge waters on the Torres Strait. Weather station data provides wind and atmospheric data to drive oceanographic models, temperature data identifies periods of thermal anomalies that may be related to coral bleaching and ecosystem health and the turbidity / salinity loggers identifies times where low salinity / high turbidity waters encroach on the northern part of the Torres Strait. This work represents a two-year extension to a previous NESP project with the extension, for this part of the project, looking to deploy instruments at the Northern Warrior Reefs for turbidity and salinity monitoring and to maintain the rest of the instruments to support the modelling work and remote sensing analysis. The overall project was to identify the fate of waters from the Fly River in Papua New Guinea and their potential impact on the Torres Strait region. This component of the project looked to provide real time weather information to drive the oceanographic models that model sediment transport and water movement. The Temperature loggers form an extension of previous work around identifying times of temperatures anomalies that may be linked to coral bleaching events. The work has identified a cool region in the east of the Torres Straits that may be important for longer term coral health. Finally, turbidity and salinity instruments were deployed at the Northern Warrior Reefs following concerns that this area was impacted by seagrass die-back and so may be showing signs of impact from PNG coastal waters. Two drone flights were also done over Bramble Cay, one in November 2019 and the second in October 2020. GeoTIFF images from the Drone-Deploy software are included in the data set. Methods: Weather Stations: Standard AIMS weather stations based on a Vaisala WTX-530 weather station along with a Li-Cor LI-192 PAR light sensor were deployed previously at Tuesday Islet near Thursday Island, at Masig (Yorke) Island in the central Torres Strait and at Bramble Cay (Maizab Kaur) in the north-east of the Torres Strait. Data collected includes: - Wind speed and direction averaged over 10 and 30 minutes - Relative Humidity - Barometric Pressure - Rainfall - Air Temperature - Light as PAR (Photosynthetically Active Radiation) As well as the above water sensors the stations had below water sensors, typically Seabird SBE39 temperature and depth sensors. Temperature Loggers: Small self-contained temperature loggers were deployed at a series of sites across the Torres Strait, due to restriction on retrieving these this data only includes loggers from the weather station sites. The loggers are Vemco Minilog-T II loggers (no longer produced) and record temperature every 10 minutes. Turbidity / Salinity Loggers: The instruments deployed on the Northern Warrior Reefs consisted of a Wet-Labs NTUS turbidity sensor and a SeaBird SBE-37 CTD instrument. The instruments were deployed in early 2020 and recovered in late 2020 for about a year’s worth of data. The data was downloaded and with the manufacturers software converted to standard units including accounting for calibration data. The data was topped and tailed to remove data when the instruments were not in the water (using the time stamps and depth sensor). Finally, the data was visually inspected and any spikes and other anomalous values removed. Drone Images: Drone surveys were done of Bramble Cay to get data on cay movement and stability. A DJI Phantom-3 drone was used to collect images, the images were then stitched into an ortho-mosaic using the Drone-Deploy software system. Drone Deploy is a web service where you program in your area to service. It then controls the Drone using an app that flies it in a pattern to cover the area, taking photos along the way. These photos are then uploaded to the Drone Deploy servers where they are stitched together using Photogrammetry into an ortho-mosaic (looking straight down from a long way away). Limitations of the data: Due to technical issues, there are data gaps in the data sets. It is difficult to get to the stations to do repairs and the presence of COVID-19 restrictions added to this. Format: Weather Station Data: This can be obtained from the AIMS web site at: Maritime Weather and Oceanographic Observations (https://weather.aims.gov.au/#/overview). The data included in this report is downloaded 10 minute raw data and daily averaged data as excel spreadsheets. Logger Data: The logger data can also be accessed from the AIMS web site at: Spatial Maps - Research Data - Australian Institute of Marine Science (aims.gov.au). The data included in this report is the downloaded raw 10 minute data as Excel files. Turbidity and Salinity Data: The data is included as the raw data files from the instruments (no quality control) and then as Excel files that have been quality controlled. Data is 10 minute readings converted to either PSU for salinity or NTU for turbidity. Dates are shown as local times (UTC+10) and identified as such by the column titles. Drone Images: The exported files from Drone Deploy are included, these are Zip files with a KML file that refences the included GeoTIFF file. These files are large and so may not load on older computers. References: Waterhouse, J., Petus, C., Brodie, J., Bainbridge, S., Wolanski, E., Dafforn, K.A., Birrer, S.C., Lough, J., Tracey, D., Johnson, J.E., Chariton, A.C., Johnston, E.L., Li, Y., Martins, F., O’Brien, D. (2018) Identifying water quality and ecosystem health threats to the Torres Strait and Far Northern GBR from runoff of the Fly River. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (162pp.). Bainbridge, S.J., Berkelmans, R., Sweatman, H and Weeks, S. (2015). Monitoring the health of Torres Strait Reefs – Final Report. Report to the National Environmental Research Program. Reef and Rainforest Research Centre Limited, Cairns (pp 74). Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.14_TS-water-quality

This dataset shows the results of mapping the connectivity of key values (natural heritage, indigenous heritage, social and historic and economic) of the Great Barrier Reef with its neighbouring regions (Torres Strait, Coral Sea and Great Sandy Strait). The purpose of this mapping process was to identify values that need joint management across multiple regions. It contains a spreadsheet containing the connection information obtained from expert elicitation, all maps derived from this information and all GIS files needed to recreate these maps. This dataset contains the connection strength for 59 attributes of the values between 7 regions (GBR Far Northern, GBR Cairns-Cooktown, GBR Whitsunday-Townsville, GBR Mackay-Capricorn, Torres Strait, Coral Sea and Great Sandy Strait) based on expert opinion. Each connection is assessed based on its strength, mechanism and confidence. Where a connection was known to not exist between two regions then this was also explicitly recorded. A video tutorial on this dataset and its maps is available from https://vimeo.com/335053846. Methods: The information for the connectivity maps was gathered from experts (~30) during a 3-day workshop in August 2017. Experts were provided with a template containing a map of Queensland and the neighbouring seas, with an overlay of the regions of interest to assess the connectivity. These were Torres Strait, GBR:Far North Queensland, GBR:Cairns to Cooktown, GBC: Townsville to Whitsundays, GBR: Mackay to Capricorn Bunkers and Great Sandy Strait (which includes Hervey bay). A range of reference maps showing locations of the values were provided, where this information could be obtained. As well as the map the template provided 7x7 table for filling in the connectivity strength and connection type between all combinations of these regions. The experts self-organised into groups to discuss and complete the template for each attribute to be mapped. Each expert was asked to estimate the strength of connection between each region as well as the connection mechanism and their confidence in the information. Due to the limited workshop time the experts were asked to focus on initially recording the connections between the GBR and its neighbouring regions and not to worry about the internal connections in the GBR, or long-distance connections along the Queensland coast. In the second half of the workshop the experts were asked to review the maps created and expand on the connections to include those internal to the GBR. After the workshop an initial set of maps were produced and reviewed by the project team and a range of issues were identified and resolved. Additional connectivity maps for some attributes were prepared after the workshop by the subject experts within the project team. The data gathered from these templates was translated into a spreadsheet, then processing into the graphic maps using QGIS to present the connectivity information. The following are the value attributes where their connectivity was mapped: Seagrass meadows: pan-regional species (e.g. Halophila spp. and Halodule spp.) Seagrass meadows: tropical/sub-tropical (Cymodocea serrulata, Syringodium isoetifolium) Seagrass meadows: tropical (Thalassia, Cymodocea, Thalassodendron, Enhalus, Rotundata) Seagrass meadows: Zostera muelleri Mangroves & saltmarsh Hard corals Crustose coralline algae Macroalgae Crown of thorns starfish larval flow Acropora larval flow Casuarina equisetifolia & Pandanus tectorius Argusia argentia Pisonia grandis: cay vegetation Inter-reef gardens (sponges + gorgonians) (Incomplete) Halimeda Upwellings Pelagic foraging seabirds Inshore and offshore foraging seabirds Migratory shorebirds Ornate rock lobster Yellowfin tuna Black marlin Spanish mackerel Tiger shark Grey nurse shark Humpback whales Dugongs Green turtles Hawksbill turtles Loggerhead turtles Flatback turtles Longfin & Shortfin Eels Red-spot king prawn Brown tiger prawn Eastern king prawns Great White Shark Sandfish (H. scabra) Black teatfish (H. whitmaei) Location of sea country Tangible cultural resources Location of place attachment Location of historic shipwrecks Location of places of social significance Location of commercial fishing activity Location of recreational use Location of tourism destinations Australian blacktip shark (C. tilstoni) Barramundi Common black tip shark (C. limbatus) Dogtooth tuna Grey mackerel Mud crab Coral trout (Plectropomus laevis) Coral trout (Plectropomus leopardus) Red throat emperor Reef manta Saucer scallop (Ylistrum balloti) Bull shark Grey reef shark Limitations of the data: The connectivity information in this dataset is only rough in nature, capturing the interconnections between 7 regions. The connectivity data is based on expert elicitation and so is limited by the knowledge of the experts that were available for the workshop. In most cases the experts had sufficient knowledge to create robust maps. There were however some cases where the knowledge of the participants was limited, or the available scientific knowledge on the topic was limited (particularly for the ‘inter-reefal gardens’ attribute) or the exact meaning of the value attribute was poorly understood or could not be agreed up on (particularly for the social and indigenous heritage maps). This information was noted with the maps. These connectivity maps should be considered as an initial assessment of the connections between each of the regions and should not be used as authoritative maps without consulting with additional sources of information. Each of the connectivity links between regions was recorded with a level of confidence, however these were self-reported, and each assessment was performed relatively quickly, with little time for reflection or review of all the available evidence. It is likely that in many cases the experts tended to have a bias to mark links with strong confidence. During subsequent revisions of some maps there were substantial corrections and adjustments even for connections with a strong confidence, indicating that there could be significant errors in the maps where the experts were not available for subsequent revisions. Each of the maps were reviewed by several project team members with broad general knowledge. Not all connection combinations were captured in this process due to the limited expert time available. A focus was made on capturing the connections between the GBR and its neighbouring regions. Where additional time was available the connections within 4 regions in the GBR was also captured. The connectivity maps only show connections between immediately neighbouring regions, not far connections such as between Torres Strait and Great Sandy Strait. In some cases the connection information for longer distances was recorded from the experts but not used in the mapping process. The coastline polygon and the region boundaries in the maps are not spatially accurate. They were simplified to make the maps more diagrammatic. This was done to reduce the chance of misinterpreting the connection arrows on the map as being spatially explicit. Format: This dataset is made up of a spreadsheet that contains all the connectivity information recorded from the expert elicitation and all the GIS files needed to recreate the generated maps. original/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_Master_v2018-09-05.xlsx: ‘Values connectivity’: This sheet contains the raw connectivity codes transcribed from the templates produced prepared by the subject experts. This is the master copy of the connection information. Subsequent sheets in the spreadsheet are derived using formulas from this table. 1-Vertical-data: This is a transformation of the ‘Values connectivity’ sheet so that each source and destination connection is represented as a single row. This also has the connection mechanism codes split into individual columns to allow easier processing in the map generation. This sheet pulls in the spatial information for the arrows on the maps (‘LinkGeom’ attribute) or crosses that represent no connections (‘NoLinkGeom’) using lookup tables from the ‘Arrow-Geom-LUT’ and ‘NoConnection-Geom-LUT’ sheets. 2.Point-extract: This contains all the ‘no connection’ points from the ‘Values connectivity’ dataset. This was saved as working/ GBR_NESP-TWQ-3-3-3_Seascape-connectivity_no-con-pt.csv and used by the QGIS maps to draw all the crosses on the maps. This table is created by copy and pasting (values only) the ‘1-Vertical-data’ sheet when the ‘NoLinkGeom’ attribute is used to filter out all line features, by unchecking blank rows in the ‘NoLinkGeom’ filter. 2.Line-extract: This contains all the ‘connections’ between regions from the ‘Values connectivity’ dataset. This was saved as working/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_arrows.csv and used by the QGIS maps to draw all the arrows on the maps. This table is created by copy and pasting (values only) the ‘1-Vertical-data’ sheet when the ‘LinkGeom’ attribute is used to filter out all point features, by unchecking blank rows in the ‘LinkGeom’ filter. Map-Atlas-Settings: This contains the metadata for each of the maps generated by QGIS. This sheet was exported as working/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_map-atlas-settings.csv and used by QGIS to drive its Atlas feature to generate one map per row of this table. The AttribID is used to enable and disable the appropriate connections on the map being generated. The WKT attribute (Well Known Text) determines the bounding box of the map to be generated and the other attributes are used to display text on the map. map-image-metadata: This table contains metadata descriptions for each of the value attribute maps. This metadata was exported as a CSV and saved into the final generated JPEG maps using the eAtlas Image Metadata Editor Application (https://eatlas.org.au/tools/image-metadata-editor). Seascape-connectivity-maps.qgs: This is a Quantum GIS (https://www.qgis.org) file used to generate all the connectivity maps. To view all the maps use: Project / Layout manager, select Template and ‘Show’ then ‘Preview Atlas’. See the video tutorial for more details (https://vimeo.com/335053846). Data Dictionary: Each connection between regions was marked with codes that represented the connection strength, mechanism and confidence. Confidence 1: Low (assumptions only) 2: Medium (expert judgement) 3: High (observational data, experimental evidence, published data) Connection strength: W: Weak connection S: Strong connection N: No connection Larval or Juvenile connection mechanism from breeding site: AD: Activity dispersal (for example: something that swims) PD: Passive dispersal (planktonic larvae that drift with ocean currents) Adult connection mechanism: DT: Daily travel (movement for feeding) M: Migration (movement driven by seasons) B: Breeding (going to breed) Human Values key (type of connectivity) VR: Visit for resource use (fishers or tourism operators crossing jurisdictions for the purposes of using natural resources) VO: Other Visit (residents, researchers, government, tourists or others visiting across jurisdictions) M: Migration (individuals choosing to migrate to the other jurisdiction) Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2017-2019-NESP-TWQ-3\3.3.3_northeast-seascape-connectivity

The last stream within the NESP 5.5 project was related to the conduct of an online survey to get aesthetic ratings of additional 3500 images downloaded from Flickr to improve the Artificial Intelligence (AI)-based system recognising and assessing the beauty of natural scenes, which had been developed in the previous NESP 3.2.3 project. Despite some earlier investment into this research area, there is still a need to improve the tools we use to measure the aesthetic beauty of marine landscapes. This research drew on images publicly available on the Internet (in particular through the photo sharing site Flickr) to build a large dataset of GBR images for the assessment of aesthetic value. Building on earlier work in NESP TWQ Hub Project 3.2.3, we conducted a survey focused on collecting beauty scores of an additional large number of GBR images (n = 3500). This dataset consists of one dataset report, two word files and one excel file demonstrating the aesthetic ratings collected used to improve the accuracy of the aesthetic monitoring AI system. Methods: The third research stream was conducted on the basis of an online survey to collect aesthetic ratings of 1585 Australians to rate the aesthetic beauty of 3500 GBR underwater pictures downloaded and selected from Flickr. Flickr is an image hosting service and one of the main sources of images for our project. As per our requirement, we downloaded all images and their metadata (including coordinates where available) based on keyword filter such as “Great Barrier Reef”. The Flickr API is available for non-commercial (but commercial use is possible by prior arrangement) use by outside developers. To ensure a much larger and diverse supply of photographs, we have developed a python-based application using Flickr API that allowed us to download Flickr images by keyword (e.g. “Great Barrier Reef” available at https://www.flickr.com). The focus of this research was on under-water images, which had to be filtered from the downloaded Flickr photos. From the collected images we identified an additional number of 3020 relevant images with coral and fish contents out of a total of approximately 55,000 downloaded images. Matt Curnock, CSIRO expert, also provide 100 images from his private images taken at the GBR and consent to use these images for our research. In total, 3120 images were selected and renamed to be rated in a survey by Australian participants (see two file “Image modification” and “Matt image rename” in the AI folder for further details). The survey was created on Qualtrics website and launched in in April 2020 using Qualtrics survey service. After giving the consent to participating in the online survey, each respondent was randomly exposed to 50 images of the GBR and rate the aesthetic of the GBR scenery on a 10 point scale (1-Very ugly/unpleasant – Very beautiful/pleasant). In total, 1585 complete and valid questionnaires were recorded. Aesthetic rating results was exported to an Excel file and used for improving the accuracy of the computer algorithm recognising and assessing the beauty of natural scenes which had been developed in the previous NESP 3.2.3 project. Further information can be found here: Stantic, B. and Mandal, R. (2020) Aesthetic Assessment of the Great Barrier Reef using Deep Learning. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (30pp.). Available at https://nesptropical.edu.au/wp-content/uploads/2020/11/NESP-TWQ-Project-5.5-Technical-Report-3.pdf Format: The AI DATASET has one dataset report, one excel file showing aesthetic ratings of all images and two Word files showing how images downloaded from Flickr website and provided by Matt Curnock (CSIRO) were renamed and used for aesthetic ratings and AI development. The aesthetic rating results were later used to improve the accuracy of the AI aesthetic monitoring system for the GBR. Further information can be found here: Stantic, B. and Mandal, R. (2020) Aesthetic Assessment of the Great Barrier Reef using Deep Learning. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (30pp.). Available at https://nesptropical.edu.au/wp-content/uploads/2020/11/NESP-TWQ-Project-5.5-Technical-Report-3.pdf References: Murray, N., Marchesotti, M. & Perronnin, F (2012). AVA: A Large-Scale Database for Aesthetic Visual Analysis. Available (09/10/17) http://refbase.cvc.uab.es/files/MMP2012a.pdf Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.5_Measuring-aesthetics

The dataset represents the code base developed for the generation of water quality metrics from various data sources (eReefs Biogeochemical models, MODIS Satellite imaging and AIMS in situ sampling). The water quality metric used underpinning previous Report Cards (until 2015) presented a number of significant shortcomings: - It was solely based on remote sensing-derived data. Concerns were raised about the appropriateness of exclusively relying on remote sensing to evaluate inshore water quality, considering well-documented challenges in obtaining accurate estimates from optically complex waters and the fact that only limited valid satellite observations are available in the wet season due to cloud cover; - It was limited to reporting on two indicators and did not incorporate other water quality data and indicators collected through the Marine Monitoring Program (MMP) and the Integrated Marine Observing System (IMOS); - It appeared relatively insensitive to large terrestrial inputs into the GBR lagoon during large rainfall and runoff events, most likely due to the binary assessment of compliance relative to the water quality guidelines and aggregation and averaging over large spatial and temporal scales; In 2016, based on the limitations described above, the Reef Plan Independent Science Panel (ISP) expressed a lack of confidence in the water quality metric that underpinned Report Cards (prior to 2015) and recommended that a new approach be identified for the Report Card 2016 and future Report Cards. The ISP also acknowledged substantial advancements in modelling water quality through the eReefs biogeochemical models and the fact that recent research and method development had improved our ability to construct report card metrics. Methods: - Index scoring strategies were systematically assessed to meet key objectives of sensitivity and representativeness, to allow data aggregation and to enable the integration of additional water quality measures when these become available in the future. A preferred method was identified as the scaled modified amplitude method with fixed caps sets at half and twice the threshold values, which was tested both theoretically and using historical datasets. - Aggregation strategies were reviewed and a hierarchical aggregation scheme was developed to allow multiple measures and sub-indicators to be combined into a single metric and to allow spatial and temporal aggregation. The process was designed to maintain the richness of information and allow the propagation of uncertainty, which were key project objectives. - The resulting water quality metric calculation process and parameters were applied to the development of the marine water quality metric component of Reef Report Card 2016, covering the reporting period 1 October 2015 to 30 September 2016. Format: The data are in the form of R code. Note, the code cannot be run as a standalone entity as it relies on non-public input data sources.

This dataset shows the effects of the insecticide imidacloprid on larval development of the hermit crab Coenobita variabilis. Experiments were conducted in 2017. The aim of this project was to apply a standard ecotoxicology protocol to determine the effects of the insecticide imidacloprid (that has been detected in the Great Barrier Reef catchment area (O'Brien et al., 2016)), on larval development (6-d exposures) of the hermit crab Coenobita variabilis. These toxicity data will enable improved assessment of the risks posed to marine crustaceans for both regulatory purposes and for comparison with other taxa. Methods: Imidacloprid (CAS 138261-41-3) stock solutions were prepared using PESTANAL (Merck) analytical grade product (purity greater than or equal to 99.9%). Stock solutions (100 – 1,000 mg L-1) were prepared by dissolving aliquots of the pure compound in ultrapure water using clean, acid-washed (5% nitric acid) glass screw-top containers. Acetone was used to dissolve the imidacloprid (less than or equal to 0.01 % (v/v) in exposure solutions). Stock solutions were stored refrigerated and in the dark. Tests were conducted as previously described (in van Dam et al, 2018). Broodstock crabs were collected from the Nightcliff seashore (Darwin, Australia – 12°23'8.70"S, 130°50'34.59"E) and maintained in custom-built, flat-bottomed enclosures. Spawning was left to occur naturally and toxicity tests initiated immediately following collection of larvae. Transparent polystyrene cell culture plates (Nunc; Thermo Scientific) were employed as test chambers. Each replicate plate contained six wells with a volume of 13 mL each. Exposures were conducted in a high-precision environmental chamber maintained at 29 ± 1°C, under 80 – 100 µmol quanta m-2s-1 PAR irradiance and a 12h:12h diurnal light:dark cycle. Zoeae were exposed to increasing concentrations of imidacloprid and tested against control (no toxicant) larvae. Zoeae were allocated individually to a well as larvae became cannibalistic once transitioned to megalopae. Five wells within a discrete plate contained analogous treatment solutions. Per test, a total of 18 plates were employed for 5 treatment concentrations and a control group, allowing for 3 replicate plates per treatment. Ten mL of exposure media was added to individual wells before the tests were started by randomly placing a larva from the common pool into each well. Larvae were transferred every 48 h to fresh exposure solutions in clean plates. After 6 d exposure, tests were terminated and individuals scored under a stereo microscope. Quality control criteria (> 70% survival in control group) for test acceptability were met for each test. Treatment effects were quantified by the percentage successful transition to megalopae in treatment groups relative to controls. Following prescribed statistical procedures (OECD, 2006), the R package DRC (R project., 2015, Ritz and Stribig., 2005) was used to model the test data and calculate toxicity estimates. Regression models evaluated included log-logistic and Weibull models of different levels of parametrisation. Model comparisons were conducted using the Akaike Information Criterion (AIC) and models that best described the data were applied to approximate pesticide concentrations eliciting 10 and 50% inhibition of successful transition relative to control animals (EC10 and EC50, respectively). The associated 95% confidence limits were estimated using the delta method. Format: The dataset is summarised in one file named ‘Coenobita variabilis pesticide toxicity data_eAtlas.xlsx’ Data Dictionary: The excel spreadsheet has one tab for each pesticide. The last tab of the dataset shows the measured (start and end of test) water quality (WQ) parameters (pH, salinity, dissolved oxygen (DO), and temperature) for each test. For the ‘Imidacloprid_Development tab: Nominal (µg/L) = nominal herbicide concentrations used in the bioassays Measured (µg/L) = measured concentrations analysed by The University of Queensland Rep = replicate notation is 1-3 No. of stage 1 zoea larvae at start = number of larvae per replicate at start of test No. of megalopae larvae day 6 = number of megalopae observed per replicate at end of test References: O’Brien, D. et al. Spatial and temporal variability in pesticide exposure downstream of a heavily irrigated cropping area: application of different monitoring techniques. J. Agric. Food Chem. 64, 3975-3989 (2016). van Dam, J. W. et al. Assessing chronic toxicity of aluminium, gallium and molybdenum in tropical marine waters using a novel bioassay for larvae of the hermit crab Coenobita variabilis. Ecotoxicol Environ Saf 165, 349-356, doi:https://doi.org/10.1016/j.ecoenv.2018.09.025 (2018). OECD. Current Approaches in the Statistical Analysis of Ecotoxicity Data., (OECD Publishing, 2006). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. (2015). Ritz, C. & Streibig, J. C. Bioassay analysis using R. Journal of Statistical Software 12, 1-22 (2005). Data Location: This dataset is filed in the eAtlas enduring data repository at: data\nesp3\3.1.5_Pesticide-guidelines-GBR

This dataset shows the effects of hexazinone (detected in the Great Barrier Reef catchments) on growth (as bell surface area), photosynthesis (effective quantum yield), zooxanthellae density (cells mm-2) and statolith number on the upside-down jellyfish Cassiopea maremetens during laboratory experiments conducted in 2017. The aims of this project were to develop and apply standard ecotoxicology protocols to determine the effects of the Photosystem II (PSII) herbicide hexazinone on the host jellyfish (growth) and endosymbiont photosynthetic efficiency (effective quantum yield and zooxanthellae density) and statolith number. A bioassay was performed over an exposure period of 14 days using hexazinone, a herbicide that has been detected in the Great Barrier Reef catchment area (O’Brien et al. 2016). The toxicity data will enable improved assessment of the risks posed by hexazinone to cnidarians and their symbionts for both regulatory purposes and for comparison with other taxa. Methods: Cassiopea maremetens were sourced from Lake Magellan, Sunshine Coast, Queensland. Jellyfish were held in 10-20 L plastic tanks partially filled with natural 0.5 µm-filtered seawater in the Marine and Aquaculture Research Facilities Unit (MARFU) at James Cook University, Townsville, Queensland. Herbicide stock solutions (100 mg L-1) were prepared using PESTANAL (Sigma-Aldrich) analytical grade hexazinone (CAS 51235-04-2). Hexazinone was dissolved in the carrier solvent acetone (final concentration 0.01% v/v), and the stock solution was prepared in Milli-Q® water. A control (no herbicide) and solvent control treatments were added to support the validity of the test protocols and to monitor continued performance of the assay. Herbicide analysis was performed by The University of Queensland, Queensland Alliance for Environmental Health Sciences (QAEHS), Woolloongabba, 4102, Australia. Individual C. maremetens medusae of similar size were placed in 250 mL plastic tanks and subjected to designated control/exposure treatments for 14 days. The treatments included two control treatments and five herbicide exposure treatments. Individuals were maintained at 25 ± 1 °C media temperature on a 12:12 h light:dark cycle under light intensity of 146 ± 15 µmol m-2 s-1. Medusae were fed 24-hr old Artemia nauplii every other day with complete solution replacement 2-5 hrs after feeding. Bell surface area (mm2) was determined using photographs taken every five days and analysed using the software ImageJ (Reuden et al.2017). Zooxanthellae density was performed following an adapted protocol of tissue digestion used in Zamoum and Furla (2012) to extract the zooxanthellae. Immediately following zooxanthellae extraction, zooxanthellae counts were conducted per individual using a Neubauer Improved haemocytometer. Zooxanthellae density (cells mm-2) was standardized to the bell surface area (mm2) of digested tissue volume. Effects of hexazinone on the photosynthetic efficiency of C. maremetens was measured by chlorophyll fluorescence as the effective quantum yield (Delta F/Fm') using pulse amplitude modulation fluorometry (mini-PAM, Walz, Germany). Light adapted minimum fluorescence (F) and maximum fluorescence (Fm') were determined and effective quantum yield was calculated for each treatment as per equation (1)(Schreiber et al. 2002). Delta F/Fm' = Fm' – F/Fm' Statoliths were extracted and counted following the Hopf and Kingsford (2013) protocol. Mean percent inhibition in bell surface area, Delta F/Fm' and statolith count of each treatment relative to the control treatment was calculated as per equation (2)(OECD 2011), where Xcontrol is the average bell surface area, Delta F/Fm' or statolith count of control and Xtreatment is the average bell surface area, Delta F/Fm' or statolith count of single treatments. % Inhibition = [(Xcontrol - Xtreatment) / Xcontrol] x 100 Effect concentrations, EC10 and EC50, for the bell surface area, photosynthetic efficiency Delta F/Fm', and statolith count were obtained relative to the control treatment. Format: Cassiopea maremetens herbicide toxicity data_eAtlas.xlsx Data Dictionary: There are five tabs for hexazinone in the spreadsheet. The first tab corresponds to the bell surface area data; the second tab is the pulse amplitude modulation (PAM) fluorometry data; the third tab is the zooxanthellae density data; the fourth tab is the statolith data; and the fifth tab shows the measured water quality (WQ) parameters (pH, salinity, and temperature) of the test. For each Hex_ ‘XXX’ tab: Nominal (µg/L) = nominal herbicide concentrations used in the bioassays; SC denotes solvent control which is no herbicide and contains less than 0.01% v/v solvent carrier Measured (µg/L) = measured concentrations analysed by The University of Queensland Rep = Replicate: for Bell and Zoox, notation is 1-5; for PAM and Stat data, notation is A, B, C, etc. Hex – Hexazinone - = no data For the ‘Hex_Bell’ tab: (includes information for each Hex_ ‘XXX’ tab as well as…) T14_SA (mm2) = Bell Surface Area at Day 14 (mm2) For the ‘Hex_PAM’ tab: (includes information for each Hex_ ‘XXX’ tab as well as…) PAM = pulse amplitude modulation fluorometry to calculate effective quantum yield (light adapted) No = number of measurements taken per individual Cassiopea maremetens. Delta F/Fm' = effective quantum (light adapted) yield measured by a Pulse Amplitude Modulation (PAM) fluorometer For the ‘Hex_Zoox’ tab: (includes information for each Hex_ ‘XXX’ tab as well as…) T14_CellsPer_mm2 = zooxanthellae density at day 14 (cells per mm2) For the ‘Hex_Stat’ tab: (includes information for each Hex_ ‘XXX’ tab as well as…) No = number of measurements taken per individual C. maremetens. T14_StatolithsperStatocyst = number of statoliths contained within a statocyst References: Hopf, J.K., Kingsford, M.J., 2013. The utility of statoliths and bell size to elucidate age and condition of a scyphomedusa (Cassiopea sp.). Marine Biology 160, 951-960 O’Brien, D., Lewis, S., Davis, A., Gallen, C., Smith, R., Turner, R., Warne, M., Turner, S., Caswell, S. and Mueller, J.F. (2016) Spatial and temporal variability in pesticide exposure downstream of a heavily irrigated cropping area: application of different monitoring techniques. Journal of Agricultural and Food Chemistry 64(20), 3975-3989. OECD (2011) OECD guidelines for the testing of chemicals: freshwater alga and cyanobacteria, growth inhibition test, Test No. 201. https://search.oecd.org/env/test-no-201-alga-growth-inhibition-test-9789264069923-en.htm Rueden, C. T.; Schindelin, J. & Hiner, M. C. et al. (2017), "ImageJ2: ImageJ for the next generation of scientific image data", BMC Bioinformatics 18:529, PMID 29187165, doi:10.1186/s12859-017-1934-z Schreiber, U., Müller, J.F., Haugg, A. and Gademann, R. (2002) New type of dual-channel PAM chlorophyll fluorometer for highly sensitive water toxicity biotests. Photosynthesis Research 74(3), 317-330. Zamoum, T., Furla, P., 2012. Symbiodinium isolation by NaOH treatment. J Exp Biol 215, 3875-3880. Data Location: This dataset is filed in the eAtlas enduring data repository at: data\nesp3\3.1.5_Pesticide-guidelines-GBR