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    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

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    This dataset contains the results of the real-time water quality monitoring program (RTWQM) conducted across the Russell-Mulgrave catchment (south of Cairns) for "Project 25". Project 25 spanned two (2) NESP TWQ projects: 2.1.7 (2016 - 2018) and 4.8 (2019 - 2020), with the dataset for Project 4.8 also containing the data for Project 2.1.7. Data is the result of 2-3 hourly in situ logging of stream height (in metres) and nitrate concentrations (mg/L). * This dataset is under an embargo period for 18 months from the completion of the project extension (NESP TWQ 4.8). The broad aim of this study dataset was to characterise the water quality impacts and relative signatures of a range of distinct landuse types found across the Russell-Mulgrave catchment, and quantify the sugarcane industry’s specific role in end-of-catchment water quality. Subcatchment waterway sites were selected to represent the major land uses of the region, and were classed as sugarcane, urban, banana, or natural rainforest land use categories. Sites were also selected based on wet season accessibility to the site and the size of the waterway. A total of 9 sites were selected for the monitoring program through the period 2016-2018. Water quality monitoring for Project 25 is based around integration of relatively traditional monitoring approaches (discrete sample collection for subsequent laboratory analysis) as well as emerging real-time (sensor-based) monitoring approaches. The development of real-time information and feedback on local water quality dynamics is a relatively novel approach to landholder engagement that is yet to be meaningfully explored in natural resource management programs. Project 25 will trial these new technologies from both the perspective of an engagement-extension tool, and also their reliability in water quality monitoring applications across multiple spatial scales (paddock to catchment). This program utilises emerging real time water quality monitoring (RTWQM) technologies including sensor and telemetry technologies that provide continuous measurement of nitrogen water quality concentrations. Noting the inherent limitations associated with traditional grab sampling, such as extended analysis and holding times prior to reporting results, monitoring programs aiming at facilitating management change are increasingly shifting towards continuous measurements using in situ sensors. RTWQM equipment was deployed in three selected sub-catchments in the broader Project 25 monitoring design to provide real time water quality information on parameters such as nutrients (nitrate) back to local industry network. The spatial design aims to link to specific paddock management activities within the monitored catchment sites. This will eventually enable individual decisions making based on real rather than hypothetical average conditions. Localised comparative data will enable growers to compare performance with neighbours. The real time information from these systems provides a solid basis for farmers to adjust strategies at any time in a dynamic and autonomous manner. Methods: Real-time monitoring stations, based closely on those utilised in an earlier BBIFMAC case study (Burton et al., 2014), were installed at three sites identified in discussion with cane industry steering committee personnel, across the Russell-Mulgrave canefarming district. Sensors were current market?ready technologies, in this case TriOS NICO and OPUS optical sensors (https://www.trios.de/en/). Discrete manual sampling for nutrient water quality was also conducted at all sites on an approximate monthly basis during dry-season low flows to ground-truth sensor nitrate readings. Sampling frequency increased to daily (and occasionally several samples a day) during wet season flood events, particularly during early wet season ‘first-flush’ events to capture initial high concentration run-off dynamics from the immediate catchment area. Samples were manually collected by project scientists, or support staff trained individually in the correct sampling and quality assurance procedures developed in conjunction with the TropWATER Water Quality Laboratory. Calibration checks of each sensor were conducted at least every 3 months, using 0, 1 mg/L, 5 mg/L and 10 mg/L nitrate calibration standards provided by the TropWATER Water Quality Laboratory. Station design in 2017 initially involved water being pumped into a flow-through cell with the nitrate sensor housed in the sampling station. Some early power issues and equipment failures saw sites re-designed with the sensor installed instream in a PVC pipe, and subsequent measurements taken in situ. Optical sensors are susceptible to reduced performance from biofouling and sedimentation of the optical lens (Steven et al., 2013). Optical sensors utilised during Project 25 were initially cleaned utilising an integrated compressed air blast system to automatically clean the optical window. Early observations of optical window cleanliness, and periodic calibration testing of sensors highlighted that at least monthly physical cleaning of lens was also required for satisfactory performance at some sites. Recent development of automated, externally mounted lens wiper technologies by TriOS saw these new cleaning technologies added to some sites towards end of 2018. Other aspects of sampling station design and operation that can improve sensor performance also emerged during early stages of Project 25 sensor deployment and monitoring. The TriOS sensors utilised can operate theoretically with power supplies spanning 12V to 24V (±10%). Frequent initial situations of nitrate-N cycling emerged where system operating voltages approached or fluctuated around the lower 12V threshold (due to issues such as riparian shading of solar panels or sustained cloudy weather reducing battery recharge and voltage drop through cable lengths). Reconfiguring system design so nitrate sensor measurements were always taken at a nominal 24V power output reduced these effects significantly. Format: Data consists of an excel spreadsheet with stream height (m) and nitrate concentrations (mg/L) for each hydrological year of data recorded on separate, named spreadsheet tabs. References: Burton, E., T.J. McShane, and D. Stubbs D. 2014. A Sub Catchment Adaptive Management Approach To Water Quality in Sugarcane. Burdekin Bowen Integrated Floodplain Management Advisory Committee (BBIFMAC). 42pp. Steven, ADL, Hodge, J, Cannard, T, Carlin, G, Franklin, H, McJannet, D, Moeseneder, C, Searle, R, 2014. Continuous Water Quality Monitoring on the Great Barrier Reef. CSIRO Final Report to Great Barrier Reef Foundation, 158pp. Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2016-18-NESP-TWQ-2\2.1.7_Engaging-farmers-WQ and data\custodian\2018-2021-NESP-TWQ-4\4.8_Project25 respectively.

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    Methods: Pygmy blue whales in the South-east Indian Ocean migrate from the southern coast of Australia to Indonesia, with a significant part of their migration route passing through areas subject to oil and gas production. This study aimed at improving our understanding of the spatial extent of the distribution, migration and foraging areas, to better inform impact assessment of anthropogenic activities in these regions. Using a combination of passive acoustic monitoring of the NW Australian coast (46 instruments from 2006 to 2019) and satellite telemetry data (22 tag deployments from 2009 to 2021) we quantified the pygmy blue whale distribution and important areas during their northern and southern migration. We show extensive use of slope habitat off Western Australia and only minimal use of shelf habitat, compared to southern Australia where use of the continental shelf and shelf break predominates. In addition, movement behaviour estimated by a state-space model on satellite tag data showed that in general pygmy blue whales off Western Australia were mostly engaged in migration, interspersed with relatively short periods (median = 28 h, range = 2 – 1080 h) of low move persistence (slow movement with high turning angles), which is indicative of foraging. Using the spatial overlap of time and number of whales in area analysis of the satellite tracking data (top 50% of grid cells) with foraging movement behaviour, we quantified the spatial extent of pygmy blue whale high use areas for foraging and migration. We compared these areas to the previously described areas of importance to foraging and migrating whales (Biologically Important Areas; BIAs). In some cases these had good agreement with the most important areas we calculated from our data, but others had only low (<10%) overlap. Month was the most important variable predicting the number of pygmy blue whale units and number of singers (acting as indices of pygmy blue whale density). Whale density was highest in the southern part of the NW Australian coast and whales were present there between April-June, and November-December, a pattern also confirmed by the satellite tracking data. Available data indicated pygmy blue whales spent up to 124 days in Indonesian waters (34% of annual cycle). Since this area may also be the calving ground for this population, inter-jurisdictional management is necessary to ensure their full protection. Keywords: Migration; Foraging; Satellite telemetry; Passive acoustics; Biologically Important Areas; Ecologically significant areas Format: Shapefiles and ESRI grids Data Dictionary: Shapefiles: - Turtle foraging distribution (yes or no) - Turtle internesting distribution (yes or no) - Whales distribution (yes or no) - Whale shark distribution (yes or no) Grids: - Spatial distribution across north-western and northern Australia, within Australia’s Exclusive Economic Zone of the cumulative threat of oil spill from high (max 3.58) to absent (0). - Spatial distribution across north-western and northern Australia, within Australia’s Exclusive Economic Zone of the threat of artificial light at night ranging from high (1) to low (0). - Spatial distribution across north-western and northern Australia, within Australia’s Exclusive Economic Zone of the threat of coastal habitat modification ranging from high (3.37) to low (0). - Spatial distribution across north-western and northern Australia, within Australia’s Exclusive Economic Zone of the threat of entanglement ranging from high (2) to low (0). - Spatial distribution across north-western and northern Australia, within Australia’s Exclusive Economic Zone of the threat of bycatch ranging from high (1) to low (0). - Spatial distribution across north-western and northern Australia, within Australia’s Exclusive Economic Zone of the threat of strike ranging from high (2.01) to low (0). - Spatial distribution across north-western and northern Australia, within Australia’s Exclusive Economic Zone of the threat of underwater noise at night ranging from high (3.66) to low (0). - Exposure of marine turtles to cumulative threats during foraging. Values range from high to low. - Exposure of marine turtles to cumulative threats during migration. Values range from high to low. - Exposure of marine turtles to cumulative threats during inter-nesting. Values range from high to low. - Exposure of whales to cumulative threats. Values range from high to low. - Exposure of whale sharks to cumulative threats. Values range from high to low. References: Michele Thums, Luciana C. Ferreira, Curt Jenner, Micheline Jenner, Danielle Harris, Andrew Davenport, Virginia Andrews-Goff, Mike Double, Luciana Möller, Catherine R.M. Attard, Kerstin Bilgmann, Paul G. Thomson, Robert McCauley, Pygmy blue whale movement, distribution and important areas in the Eastern Indian Ocean, Global Ecology and Conservation, Volume 35, 2022, e02054, ISSN 2351-9894, https://doi.org/10.1016/j.gecco.2022.e02054 Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\WA_AIMS_NWS2S-Pygmy-blue-whales_2021

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    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

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    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

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    This generated data set contains exposure products derived from 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 exposure products 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 products 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/. This dataset contains the following exposure products: * FRESH WATER EXPOSURE The freshwater exposure products calculate the level of exposure to salinity levels below a threshold over a calendar month. The standard thresholds used in the freshwater exposure products generated by the AIMS eReefs Platform are 26 and 28 PSU (refer to the attributes attached to the variables in the NetCDF files to confirm). These were chosen as different species have different tolerances to freshwater exposure, and the most appropriate level is not currently known. Method: The freshwater exposure is calculated from the sum of the daily average salinity below the exposure threshold. Salinity levels above the threshold don't contribute at all to the exposure. Salinity levels below the threshold contribute in proportion to the amount below the threshold. As a result with a threshold of 28 PSU and an exposure to 22 PSU for 3 days the freshwater exposure would be (28-22)*3 = 18 PSU days. The same exposure at 25 PSU would take 6 days (28-25)*6 = 18 PSU days. This method of calculating exposure is conceptually very similar to temperature exposure measured in Degree Heating Weeks, where the higher the temperature is above the typical maximum summer temperature the faster the heat stress occurs. For freshwater exposure the stress occurs when the salinity drops below the threshold value. The lower the salinity the faster the exposure accumulates. More research is needed to determine if this is the best method for estimating stress from freshwater. A more detailed description of the processing, especially exposure calculations 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 variables found in the freshwater exposure products document the threshold used to calculate that variable. For example: - fresh_water_exposure_29p5 : threshold = 29.5 - fresh_water_exposure_28 : threshold = 28.0 - fresh_water_exposure_26 : threshold = 26.0 Depths: Depths at 1km resolution: -2.35m Depths are 4km resolution: -1.5m Limitations: 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.

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    Presence information for sharks and fish fitted with acoustic transmitters on reefs in the Townsville region. Acoustic receivers are deployed on: Bramble (4), Rib (4), Kelso (2), John Brewer (4), Lodestone (4), Helix (4), Keeper (2), Glow (3), Cotton Shoal (1), Arc (1), Grub (4), Yankee (3), Centipede (2), Wheeler (5), Davies (2), Pinnacle (1) and Little Broadhurst (2) reefs. Transmitter life ranges from 12-30 months. Transmitters report depth data to indicate position of the animal in the water column. \n \n To date transmitters have been deployed in: grey reef, blacktip, bull, silvertip, sliteye and hammerhead sharks, common coral trout, bluespot coral trout, redthroat emperor, giant trevally and spanish mackerel in this region.\n To monitor the presence and movement of fish and elasmobranch species within and between reefs in the Townsville reef region.\n These data are collected as part of NERP funded research, but the data will also be contributed to the AATAMS facility within IMOS and as such are part of a broader national framework. Data from this research is also housed in the AATAMS data repository and publicly available on line.\n

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    This research was conducted to understand the relationship between water quality and ecological attributes on coral reef communities, through field surveys on naturally turbid inshore coral reefs that vary in exposure to water bourne sediment, nutrient and chlorophyll concentrations. Reefs in two regions were surveyed for biodiversity of algae, hard corals, octocorals and fish. Region PC: Princess Charlotte Bay (in the Claremont Isles, north of PC Bay) and and Region WT: Wet Tropics (between Tully and Port Douglas). SOme reefs were surveyed for the all four groups of taxa, while others were surveyed for some groups of the biodiversity. \n Four groups of taxa were surveyed using rapid ecological assessments based on standardized scuba-swims by experts:\n 1 -macroalgae, mostly identified to genus level\n 2- hard corals , idenified to species level\n 3- octocorals, identified to genus level\n 4 -fish, identified to species or species groups\n Abundances of the three benthic groups were rated on a 6-point scale as 0 = ‘absent’, 1 = ‘rare’, 2 = ‘uncommon’, 3 = ‘common’, 4 = ‘abundant’, and 5 = ‘dominant’.\n Abundances of some of the fish species were estimated on a log (base 5) scale (Williams, 1982), whereas less abundant fish species were fully enumerated.\n Water Quality data was collected between December 2000 and April 2002. Surface water samples were taken at each reef for analysis of 12 water quality variables: particulate nitrogen and phosphorus (PN, PP), nitrate, nitrite, ammonium, total dissolved nitrogen and phosphorus (TDN, TDP), silicate (Sil), chlorophyll (Chl), phaeopigments (Phae), salinity (Sal) and suspended solids (SS).\n Analysies included: Redundancy analyses, Permutation tests, Log-linear regression models and Model averaging of models based on the Bayesian Information Criterion was used.\n

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    This dataset contains processed satellite imageries of the Golf of Papua - Torres Strait (GP-TS) region. It includes: - 12-year (mid 2008-mid 2019) of daily MODIS water type images (Wet season colour scale), and summaries (seasonal, annual, long term, difference composite maps) - 1 year (2019) of weekly Sentinel-3 water type images (Forel-Ule colour scale) ** This dataset is currently under embargo until 31/01/2022. These outputs have been produced though the remote sensing components of the NESP Project 2.2.1 and NESP Project 5.14: Identifying the water quality and ecosystem health threats to the high diversity Torres Strait and Far Northern GBR from runoff from the Fly River (Waterhouse et al., 2018, in review and Petus et al., in prep.) These studies used different sources and long-term databases of freely available satellite data to describe large-scale turbidity patterns around the GP-TS region, map the Fly River plume and to identify instances and areas with likely plume intrusion into the Torres Strait protected zone. Multi-year datasets of medium-resolution satellite images (MODIS-Aqua and Sentinel-3) of the study area have been downloaded and processed. Medium-resolution satellite data have been processed into daily colour class and water type maps of the study area using two respective colour classification scales. Several spatial summaries have been produced (median, frequency, difference composite maps) at different time scales (seasonal, annual, long term). These spatial summaries provides a large scale baseline of the composition of coastal waters around the GP-TS region, as well as a description of seasonal trends. This baseline is particularly important as field water quality data are scarce and challenging to collect due to the remoteness of the study area, They provide a reference against which to compare future changes, as well as spatially explicit information for when and where the influence from Fly River discharge is likely to occur and which TS ecosystems are likely to be the most exposed. In making this data publicly available for management, the authors from the TropWATER Catchment to Reef Research Group request being contacted and involved in decision making processes that incorporate this data, to ensure its methodology and limitations are fully understood. Methods: MODIS-Aqua water type maps Twelve years of water type maps (mid-2008 to mid-2019) were produced using daily MODIS-Aqua (MA) true colour satellite imagery reclassified to 6 distinct ocean colour classes. The ocean colour is the result of interactions between sunlight and materials in the water. It is co-determined by the absorption and scattering of various optically active water quality components: the suspended sediment: SS, the coloured dissolved organic matter: CDOM and the chlorophyll-a: Chl-a. The ocean colour is a simple indicator available to study the composition of our ocean and distinguish different surface water bodies and their associated water quality characteristics (e.g., Petus et al., 2019, in prep.).The six colour classes (CC) were defined by their colour properties across an Intensity-Hue-Saturation gradient (Alvarez-Romero et al., 2012) and were regrouped into three optical water types: Primary (CC1-4), Secondary (CC5) and Tertiary (CC6). They were produced using the WSC scale classification toolbox (Petus et al., 2019). The WSC scale classification toolbox is a semi-automated toolbox using a suit of R and Python (ArcGIS) scripts that has been developed originally for the Great Barrier Reef (GBR) through Marine Monitoring Program (MMP) funding (Alvarez-Romero et al., 2013). The toolbox spectrally enhance (Red-Green-Blue, RGB to Intensity-Hue-Saturation, IHS) MODIS true colour imagery and cluster the MODIS pixels into “cloud” (from the RGB image), “ambiant water” and six Wet Season Colour classes (from the IHS image) through a supervised classification using typical apparent surface colour signatures of flood waters in the GBR (Alvarez-Romero et al., 2013, Figure 1, right and Figure 2). Discrimination of colour classes has been based on the GBR flood plume typology as defined originally in e.g., Devlin et al. (2011). It has been calibrated and validated with satellite and in-situ water quality data, respectively (Alvarez-Romero et al., 2013; Devlin et al., 2015, Petus et al., 2016). Technical details about the WSC scale classification have been published in e.g. Alvarez-Romero et al., 2013, Devlin et al., 2015; Petus et al., 2016, 2019 and GBRMPA, 2020 and Waterhouse et al., in prep. In the GBR WSC scale, the brownish to brownish-green turbid water masses (colour classes 1 to 4, or primary water type) are typical for inshore regions of GBR river plumes or nearshore marine areas with high concentrations of resuspended sediments found during the wet season. These water bodies in flood waters typically contain high nutrient and phytoplankton concentrations, but are also enriched in sediment and dissolved organic matter resulting in reduced light levels. The greenish-to-greenish-blue turbid water masses (colour class 5, or Secondary water type) is typical of coastal waters rich in algae and also containing dissolved matter and fine sediment. This water body is found in the GBR open coastal waters as well as in the mid-water plumes where relatively high nutrient availability and increased light levels due to sedimentation favour coastal productivity. Finally, the greenish-blue water mass (colour class 6 or Tertiary water type) correspond to waters with above ambient water quality concentrations. This water body is typical for areas towards the open sea or offshore regions of river flood plumes (e.g. Petus et al., 2019). Sentinel-3 OLCI water type maps One year (2019) of water type maps was also produced using daily Sentinel-3 Ocean and Land Color Instrument (S3 OLCI) Level-2 (hereafter S3) satellite data reclassified to 21 distinct ocean colour classes. The 21 colour classes (CC) were defined by their colour properties across a Hue gradient and were produced using the Forel-Ule colour (FU) scale classification toolbox. The FU classification toolbox is a semi-automated toolbox using a suit of Python, .bat and xml scripts that have been developed originally for the GBR through MMP funding. It allow processing multi-year databases of satellite images using the FU classification algorithm recently developed through the European Citclops project and implemented in the Science Toolbox Exploitation Platform (SNAP) (http://www.citclops.eu/home, Van der Woerd and Wernand, 2015, 2018). Technical details about the WSC scale classification have been published in e.g., Petus et al., 2019, in prep. and the Appendix B of Gruber et al., 2019. The FU satellite algorithm converts satellite normalised multi-band reflectance information into a discrete set of FU numbers using uniform colourimetric functions (Wernand et al., 2012). The derivation of the colour of natural waters is based on the calculation of Tristimulus values of the three primaries (X, Y, Z) that specify the colour stimulus of the human eye. The algorithm is validated by a set of hyperspectral measurements from inland, coastal and marine waters (Van der Woerd and Wernand 2018) and is applicable to global aquatic environments (lake, estuaries, coastal, offshore). Technical details about FU satellite algorithm, including detailed mathematical descriptions, are presented in e.g., Van der Woerd and Wernand (2015, 2016), Van der Woerd and Wernand (2018) and Wernand et al. (2013). A first comparative study in the GBR suggested that FU4-5, FU6-9 and FU ? 10 are similar to the Primary, Secondary and Tertiary water types in the WS colour scale, respectively (Petus et al., 2019). Both satellites and colour scales provides qualitative estimation of water composition and spatial datasets that can used in conjunction with in-situ field measurements, satellite estimations and/or hydrodynamic modelling assessments of water quality concentrations (if available). By itself, they are particularly interesting in remote areas where in-situ water quality and optical data are scarce to inexistent as they both relies only on the apparent colour of the ocean. This datasets have been used in Petus et al., in prep. and Waterhouse et al., in review to: (i) map optical water masses in the study area; including the turbid Fly River plume, (ii) document long-term turbidity trends in the Gulf of Papua – Torres Strait region, (iii) determine seasonal changes in turbidity and seasonal plume patterns, and; (iii) assess the presence of ecosystems likely exposed to the Fly River plume, as well as their frequency of exposure. These datasets does not allow assessing the trace metals contaminants of the Fly River discharge or assessing the ecological impact of Fly River discharges on TS ecosystems. Outputs: Daily datasets: MODIS-Aqua true colour images and six colour class maps: Database name: Daily_MAWS.gdb, Data format: a2008141 = year 2008, Julian day 141 Twelve years (mid-2008 to mid-2019) of daily MODIS true colour images of the GPTS region were downloaded from the NASA Rapid Response and EOSDIS worldview websites. The true colour images were spectrally enhanced (from red-green-blue to hue-saturation-intensity colour systems), and clustered into six colour class maps using methods described above (Álvarez-Romero et al., 2013) and post-processed in ArcGIS 10.3. Weekly datasets: Sentinel-3 Forel Ule (21) colour class maps: Database name: Weekly_S3FU.gdb, Data format: T2019w01 = week 1 of 2019 One year (2019) of daily S3 OLCI imagery of the study area was downloaded on the EUMETSAT Copernicus Online Data Access website (https://coda.eumetsat.int/#/home). S3 data were atmospherically corrected and were processed with SNAP (Van der Woerd et al., 2016, Van der Woerd and Wernand 2018) and clustered into 21 FUC class maps using methods described above. Processed daily images were combined into weekly maximum FUC composites. Weekly composites were produced to minimise the amount of area without data per image due to masking of dense cloud cover which is very common in the study area. Summaries: median, mean, Standard Deviation, difference and frequency maps Median, mean and standard deviation composite maps (2008-2016, produced as part of NESP TWQ 2.2.1) were created from the MODIS daily colour class maps. These composites were obtained by overlaying the daily MODIS colour class maps and calculating mean and standard deviation and/or the median colour class category per pixel over seasonal, annual (2008 to 2016) and long-term time periods (8 years: 2008-2016). The seasons were defined in this study as: monsoon season: January – April, trade wind season: May to October and transition period: November and December. *Note that in these summaries produced through NESP 2.2.1, data from the 2nd of Feb 2011 to 8th of May 2012 were missing from the NASA websites due to a hardware failure of a NASA disk array that contained the MODIS images. Missing image were made available later, downloaded and used to produce the new summaries below. Summaries: Long-term median, long-term seasonal median and long-term monthly median (decadal: 2009-2018, produced as part of NESP TWQ 5.14) Database name: Summaries_MAWS_medianLT.gdb, Data format: Med_0918_Mo = long-term median monsoonal composite, Med0918TW = long-term median trade wind composite, Month0918m = long-term median monthly composite (for example: Aug0918m) These summaries were created from the daily MODIS colour class maps. These composites were obtained by overlaying the daily MODIS colour class maps and calculating the median colour class category for each pixel of our study area using all daily data from 2009 to 2018, or using daily data collected in each monsoon and trade wind seasons or in each month of the 2009 – 2018 period. The seasons were defined in this study as: monsoon season: January – April, trade wind season: May-December. Long-term (i) seasonal and (ii) monthly difference maps Database name: Summaries_MAWS_difference_,maps.gdb Data format: Diff_Seasonal0918m = seasonal difference map, Diff_Month0918m = monthly difference maps (for example, Diff_Aug0918m) These summaries were created to illustrate areas with an increase (positive anomaly) or decrease (negative anomaly) in turbidity: (i) in the trade wind season against the monsoonal trends; or (ii) in each month against long-term trends. The seasonal difference map was calculated by subtracting the long-term median monsoonal and trade wind maps. The monthly difference maps was calculated by subtracting each long-term monthly map and the decadal median maps Frequency maps (2009-2018): Annual frequency maps were generated from the daily colour class maps for each of the colour classes 1 to 6, as well as 1-4 combined (primary water type), 1-5 (primary plus secondary water types) and 1-6 combined. The water type frequency was defined as the total number of days per year exposed to a given water type divided by the number of data days (non-cloud) recorded per year, resulting in a normalised frequency on a scale from 0 – 1. An annual ‘data quality’ (dq) raster was also produced, calculated as the number of non-cloud days per pixel divided by the total number of daily rasters available for the year. Processing was performed using Python 2.7.3 and ArcGIS 10.7 (ESRI, 2019). Mean multi-annual frequency map (2009-2018): For each of the colour class combinations described above, a corresponding multiannual average was generated using cell statistics: mean (Spatial Analyst), performed using Python 2.7.3 and ArcGIS 10.7 (ESRI, 2019). Limitations of the data: In the absence of significant ground-truthing measurements, this study assumed that turbidity levels in the Torres Strait region decreased from the Primary to the Tertiary waters types, as observed in the GBR (e.g., Petus et al., 2019). The assumption supported by preliminary match-ups undertaken between available field and MODIS satellite data, as well as match-ups between MODIS water type maps and Sentinel-3 Forel-Ule water type maps (Petus et al., in prep; Waterhouse et al., in review). More in-situ water quality measurements should, however, be collected to fully validate this assumption. The dense cloud cover in the study area, allowed to capture satellite information about 50% of the time and it is likely that some large sediment plumes associated with rough weather may have been missed. Also, it is impossible to fully separate direct riverine plume influence from sediment resuspension or the influence of calcareous sediments in the satellite maps. Finally this project also assumes that the RGB and IHS signals recorded over turbid, sediment-dominated waters such as the GBR flood waters mainly results from the water contribution and that fully accurate atmospheric corrections of MODIS-Aqua data are not crucial for this type of turbid waters and assessment. Format: NetCDF files References: 1) Alvarez-Romero, J., Devlin, M., da Silva, E., Petus, C., Ban, N., Pressey, R., Kool, J., Roberts, J., Cerdeira-Estrada, S., Wenger, A., Brodie, J., 2013. A novel approach to model exposure of coastal-marine ecosystems to riverine flood plumes based on remote sensing techniques. Journal of Environmental Management, 119. pp. 194-207 2) Devlin, M., Schroeder, T., McKinna, L., Brodie, J., Brando, V., & Dekker, A., 2011. Monitoring and mapping of flood plumes in the Great Barrier Reef based on in situ and remote sensing observations. Advances in Environmental Remote Sensing to Monitor Global Changes, 147-191. 3) Devlin, M., Petus, C., Teixeira da Silva, E., Tracey. D., Wolff, N., Waterhouse, J., Brodie, J., 2015. Water Quality and River Plume monitoring in the Great Barrier Reef: An Overview of Methods Based on Ocean Colour Satellite Data. Remote Sensing 7, 12909-12941; doi:10.3390/rs71012909 4) Great Barrier Reef Marine Park Authority 2020, Marine Monitoring Program quality assurance and quality control manual 2018-19, Great Barrier Reef Marine Park Authority, Townsville 5) O'Brien, D., Mellors, J., Petus, C., Martins, F., Wolanski, E. and Brodie, J. (2015) Monitoring and assessment of water quality threats to Torres Strait marine waters and ecosystems. Progress Report August 2015 Report No. 15/43. Townsville, James Cook University. 6) Petus, C., Devlin, M., Thompson, A., McKenzie, L., Collier, C., Teixeira da Silva, E., Tracey, D. Estimating the Exposure of Coral Reefs and Seagrass Meadows to Land-Sourced Contaminants in River Flood Plumes of the Great Barrier Reef: Validating a Simple Satellite Risk Framework with Environmental Data, 2016. Remote Sensing, 8(3), 210; doi:10.3390/rs8030210 7) Petus et al., in prep. Monitoring sediment distribution and potentially polluted riverine runoff in a data-limited environment: insights from satellite images in the remote Torres Strait. 8) Van der Woerd, H.J., Wernand, M.R., 2015. True colour classification of natural waters with medium-spectral resolution satellites: SeaWiFS, MODIS, MERIS and OLCI. 2015. Sensors 15, 25663–25680. https://doi.org/10.3339/s151025663 . 9) Van der Woerd, J.H., Wernand, R.M., 2018. Hue-angle product for low to medium spatial resolution optical satellite sensors. Remote Sensing 10, 180. 10) 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.). 11) Waterhouse, J., Apte, S., Petus, C., Bainbridge, S., Wolanski, E., Tracey, D., Angel, B.M., Jarolimek, C, V., Brodie Jon., in review. NESP Project 5.14. Identifying water quality and ecosystem health threats to the Torres Strait from Fly River runoff. Report to the National Environmental Science Programme. Reef and Rainforest Research Centre Limited, Cairns (162pp.). 12) Waterhouse et al., in prep. Waterhouse, J., Gruber, R., Logan, M., Petus, C., Howley, C., Lewis, S., Tracey, D., James, J., Mellors, J., Tonin, H., Skuza, M., Costello, P., Davidson, J., Gunn, K., Lefevre, C., Moran, D., Robson, B., Shanahan, M., Zagorskis, I., Shellberg, J., 2021. Marine Monitoring Program: Annual Report for Inshore Water Quality Monitoring 2019-20. Report for the Great Barrier Reef Marine Park Authority, Great Barrier Reef Marine Park Authority, Townsville. 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

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    This dataset is a complete state-wide digital land use map of Queensland. The dataset is a product of the Queensland Land Use Mapping Program (QLUMP) and was produced by the Queensland Government. It presents the most current mapping of land use features for Queensland, including the land use mapping products from 1999, 2006 and 2009, in a single feature layer. This dataset was last updated July 2012. The dataset comprises an ESRI vector geodatabase at a nominal scale of 1:50,000 in coastal regions and 1:100 000 in Western Queensland. The layer is a polygon dataset with each class having attributes describing land use. Land use is classified according to the Australian Land Use and Management Classification (ALUMC) Version 7, May 2010. Five primary classes are identified in order of increasing levels of intervention or potential impact on the natural landscape. Water is included separately as a sixth primary class. Under the three-level hierarchical structure, the minimum attribution level for land use mapping in Queensland is secondary land use. Primary and secondary levels relate to land use (i.e. the principal use of the land in terms of the objectives of the land manager). The tertiary level includes data on commodities or vegetation, (e.g. crops such as cereals and oil seeds). Where required* and possible, attribution is performed to tertiary level. * QLUMP maps the land use classes of sugar and cotton to tertiary level. Each polygon has been attributed with "Year", denoting the time at which the mapping is current at. A map illustrating the currency of land use is available at www.derm.qld.gov.au/science/lump/background.html A representation is available for users to apply a symbology to the land use data, by secondary ALUMC. Some land uses that fall under the minimum mapping unit of 2 ha are not explicitly mapped but aggregated into the surrounding land use classess, for example cropping - sugar and grazing native vegetation, whereby tracks and farm infrastructure, road reserves and drainage lines are included.