This dataset presents the code written for the analysis and modelling for the Jellyfish Forecasting System for NESP TWQ Project 2.2.3. The Jellyfish Forecasting System (JFS) searches for robust statistical relationships between historical sting events (and observations) and local environmental conditions. These relationships are tested using data to quantify the underlying uncertainties. They then form the basis for forecasting risk levels associated with current environmental conditions. The development of the JFS modelling and analysis is supported by the Venomous Jellyfish Database (sting events and specimen samples – November 2018) (NESP 2.2.3, CSIRO) with corresponding analysis of wind fields and tidal heights along the Queensland coastline. The code has been calibrated and tested for the study focus regions including Cairns (Beach, Island, Reef), Townsville (Beach, Island+Reef) and Whitsundays (Beach, Island+Reef). The JFS uses the European Centre for Medium-Range Weather forecasting (ECMWF) wind fields from the ERA Interim, Daily product (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim). This daily product has global coverage at a spatial resolution of approximately 80km. However, only 11 locations off the Queensland coast were extracted covering the period 1-Jan-1985 to 31-Dec-2016. For the modelling, the data has been transformed into CSV files containing date, eastward wind (m/s) and northward wind (m/s), for each of the 11 geographical locations. Hourly tidal height was calculated from tidal harmonics supplied by the Bureau of Meteorology (http://www.bom.gov.au/oceanography/projects/ntc/ntc.shtml) using the XTide software (http://www.flaterco.com/xtide/). Hourly tidal heights have been calculated for 7 sites along the Queensland coast (Albany Island, Cairns, Cardwell, Cooktown, Fife, Grenville, Townsville) for the period 1-Jan-1985 to 31-Dec-2017. Data has been transformed into CSV files, one for each of the 7 sites. Columns correspond to number of days since 1-Jan 1990 and tidal height (m). Irukandji stings were then modelled using a generalised linear model (GLM). A GLM generalises ordinary linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value (McCullagh & Nelder 1989). For each region, we used a GLM with the number of Irukandji stings per day as the response variable. The GLM had a Poisson error structure and a log link function (Crawley 2005). For the Poisson GLMs, we inferred absences when stings were not recorded in the data for a day. We consider that there was reasonably consistent sampling effort in the database since 1985, but very patchy prior to this date. It should be noted that Irukandji are very patchy in time; for example, there was a single sting record in 2017 despite considerable effort trying to find stings in that year. Although the database might miss small and localised Irukandji sting events, we believe it captures larger infestation events. We included six predictors in the models: Month, two wind variables, and three tidal variables. Month was a factor and arranged so that the summer was in the middle of the year (i.e., from June to May). The two wind variables were Speed and Direction. For each day within each region (Cairns, Townsville or Whitsundays), hourly wind-speed and direction was used. We derived cumulative wind Speed and Direction, working backwards from each day, with the current day being Day 1. We calculated cumulative winds from the current day (Day 1) to 14 days previously for every day in every Region and Area. To provide greater weighting for winds on more recent days, we used an inverse weighting for each day, where the weighting was given by 1/i for each day i. Thus, the Cumulative Speed for n days is given by: Cumulative Speed_n=(\sum_(i=1)^n Speed_i/i) / (\sum_(i=1)^n 1/i) For example, calculations for the 3-day cumulative wind speed are: (1/1×Wind Day 1 + 1/2 × Wind Day 2 + 1/3 × Wind Day 3) / (1/1+1/2+1/3) Similarly, we calculated the cumulative weighted wind Direction using the formula: Cumulative Direction_n=(\sum_(i=1)^n Direction_i/i) / (\sum_(i=1)^n 1/i) We used circular statistics in the R Package Circular to calculate the weighted cumulative mean, because direction 0º is the same as 360º. We initially used a smoother for this term in the model, but because of its non-linearity and the lack of winds of all directions, we found that it was better to use wind Direction as a factor with four levels (NW, NE, SE and SW). In some Regions and Areas, not all wind Directions were present. To assign each event to the tidal cycle, we used tidal data from the closest of the seven stations to calculate three tidal variables: (i) the tidal range each day (m); (ii) the tidal height (m); and (iii) whether the tide was incoming or outgoing. To estimate the three tidal variables, the time of day of the event was required. However, the Time of Day was only available for 780 observations, and the 291 missing observations were estimated assuming a random Time of Day, which will not influence the relationship but will keep these rows in the analysis. Tidal range was not significant in any models and will not be considered further. To focus on times when Irukandji were present, months when stings never occurred in an area/region were excluded from the analysis – this is generally the winter months. For model selection, we used Akaike Information Criterion (AIC), which is an estimate of the relative quality of models given the data, to choose the most parsimonious model. We thus do not talk about significant predictors, but important ones, consistent with information theoretic approaches. Limitations: It is important to note that while the presence of Irukandji is more likely on high risk days, the forecasting system should not be interpreted as predicting the presence of Irukandji or that stings will occur. Format: It is a text file with a .r extension, the default code format in R. This code runs on the csv datafile “VJD_records_EXTRACT_20180802_QLD.csv” that has latitude, longitude, date, and time of day for each Irukandji sting on the GBR. A subset of these data have been made publicly available through eAtlas, but not all data could be made publicly available because of permission issues. For more information about data permissions, please contact Dr Lisa Gershwin (lisa.gershwin@stingeradvisor.com). Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2016-18-NESP-TWQ-2\2.2.3_Jellyfish-early-warning\data\ and https://github.com/eatlas/NESP_2.2.3_Jellyfish-early-warning

Five paired Control/Treatment gully sites on commercial grazing properties in the Upper Burdekin and Bowen catchments are being monitored as part of NESP Project 2.1.4 (Demonstration and evaluation of gully remediation on downstream water quality and agricultural production in GBR rangelands) (Bartley et al., 2018). The key question being asked is: "Is there measurable improvement in the erosion and water quality leaving remediated gully sites compared to sites left untreated?" The monitoring approach uses a modified BACI (Before after control impact) design. This record acts as an aggregation point for the datasets produced by NESP TWQ Project 2.1.4. for Gully Remediation at sites Meadowvale, Virginia Park, Strathbogie, Minnievale and Mt Wickham Station. These sites are located in the Burdekin Region of Queensland. The time series established in this project is being extended by NESP TWQ Project 5.9. Updated versions of this dataset will be linked to from this record when they become available. There are two sites in the Upper Burdekin sites (Virginia Park and Meadowvale) which capitalize on previous research investments looking at rangeland management and water quality response. The Bogie (Strathbogie) and Don (Minnievale) sites are Reef Trust 2 partnership projects. Mt Wickham is a new site that is part of the Burdekin Landholders Driving Change (LDC) project. Each contributing datasets has been described in specific dataset records linked as child records. Datasets include Key Localities, Loads Runoff, Site Configuration and survey types, TLS Scans and Analysis, Vegetation Survey, Water Quality survey. The data can be downloaded from each of the child records.

Towed video surveys were carried out in targeted shallow seabed environments within the proposed Oceanic Shoals Commonwealth Marine Reserve (CMR) in the Timor Sea. The survey concentrated on shelf habitats (< 200m) of the western part of the Oceanic Shoals CMR and included potential biodiversity hotspots such as pinnacles, banks and shoals. In total, 52 towed video transects were collected at depths of between 31 and 129 metres. Sampling involved habitat classification, conducted in real-time during the surveys, and taking downward and forward facing photographic still images at 5 second intervals for subsequent analysis. The cameras were towed across the shoals at a speed of 0.5 - 1.5 knots at an altitude of 0.5 - 2 metres above the seabed.\n The Marine Biodiversity Survey of the proposed Oceanic Shoals Commonwealth Marine Reserve (CMR) in the Timor Sea was a research collaboration between the Australian Institute of Marine Science (AIMS), Geoscience Australia (GA), University of Western Australia (UWA) and the Museum & Art Gallery of the Northern Territory (MGNT). The survey was undertaken between 12 September and 6 October 2012 on the AIMS Research Vessel, RV Solander, and formed part of the National Environmental Research Program (NERP) Marine Biodiversity Hub Theme 4: Regional Biodiversity Discovery to Support Marine Bioregional Plans.\n

This dataset contains code used to generate the daily benthic light (bPAR) data product provided at https://eatlas.org.au/data/uuid/356e7b3c-1508-432e-9d85-263ec8a67cef and the bPAR index for water quality in the Great Barrier Reef (GBR). It can also be used to calculate photosynthetically active radiation (PAR) at any specified depth in GBR waters. The amount of light available for photosynthesis (photosynthetically active radiation, or PAR) is an important determinant of ecosystem health. PAR reaching the bottom of the water column is known as benthic PAR (bPAR). Where there is sufficient light reaching the bottom, seagrasses and corals may thrive. bPAR varies seasonally as a function of surface PAR, but also varies function of both water depth and water quality. This dataset contains the code used to generate the bPAR data product and the derived bPAR index for water quality developed through NESP TWQ projects 2.3.1 and 5.3. Methods: Daily benthic light (bPAR) is derived from NASA MODIS Ocean Color data in combination with bathymetric data from Beaman (2010) using the algorithm described by Magno-Canto et al. (2019, 2020). Ocean colour observations are used to estimate inherent optical properties (total absorption and total backscattering coefficients) at each of ten MODIS wavelengths, using the SWIM algorithm of McKinna et al. (2015). These in turn are used to calculate spectrally-resolved light attenuation (Kd). The Beer-Lambert equation is then used to propagate surface PAR using the calculated Kd down to the depth of the bottom of the water column defined by the bathymetry data (i.e. depth map) obtained from Beaman (2010). This yields an estimate of instantaneous bPAR on a nominal 1 km2 grid scale across the study region. Instantaneous bPAR is then used to calculate daily integrated benthic light by considering the path and angle of the sun over the course of each day, assuming constant atmospheric conditions within each day. The C code for generating the bPAR product from MODIS data is in the process of being implemented in NASA’s SeaDAS processing and visualisation software package. Daily bPAR is then is used to derive an index for water quality in the Great Barrier Reef (GBR). Daily bPAR observations at a nominal 1km2 resolution are used to calculate a cumulative seasonal and annual light stress experienced by benthic photosynthesising organisms at each pixel. This light stress is aggregated over each NRM region and waterbody of the GBR and scaled to produce an index with a value between 0 (no light penetration to benthic habitats) and 1 (excellent light penetration to benthic habitats). The R script RS_bPAR_final.R calculates the index and generates figures plotting variations in the value of the index over time as well as showing how this index varies in each region as a function of river load. The full method used to calculate bPAR is described in the following publications: Magno-Canto, M.M., McKinna, L.I., Robson, B.J. and Fabricius, K.E., 2019. Model for deriving benthic irradiance in the Great Barrier Reef from MODIS satellite imagery. Optics express, 27(20), pp.A1350-A1371. Magno-Canto, M.M., McKinna, L.I., Robson, B.J., Fabricius, K.E. and Garcia, R., 2020. Model for deriving benthic irradiance in the Great Barrier Reef from MODIS satellite imagery: erratum. Optics Express, 28(19), pp.27473-27475. The full method used to calculate the bPAR index are described in the manuscript: Canto, M. M., Fabricius, K. E., Logan, M., Lewis, S., McKinna, L. I. W., & Robson, B. J. (2021). A benthic light index of water quality in the Great Barrier Reef, Australia. Marine Pollution Bulletin, 169, 112539. https://doi.org/10.1016/j.marpolbul.2021.112539 Format: The functions needed to calculate benthic PAR or PAR at any specified depth is provided as c code in the file “get_bpar.c”. This is designed to be executed as part of NASA’s SeaDAS software. The script to calculate the bPAR index, “RS_bPAR_final.R” can be executed in the R programming language (v 3.4 or later). This script calls on functions defined in a second R script, “WQI_functions.R” and spatial data and region labels provided in the R data files “Polys.rda” and “spatial.csv”, which are also provided. References: Magno-Canto, M.M., McKinna, L.I., Robson, B.J. and Fabricius, K.E., 2019. Model for deriving benthic irradiance in the Great Barrier Reef from MODIS satellite imagery. Optics express, 27(20), pp.A1350-A1371. Magno-Canto, M.M., McKinna, L.I., Robson, B.J., Fabricius, K.E. and Garcia, R., 2020. Model for deriving benthic irradiance in the Great Barrier Reef from MODIS satellite imagery: erratum. Optics Express, 28(19), pp.27473-27475. Canto, M. M., Fabricius, K. E., Logan, M., Lewis, S., McKinna, L. I. W., & Robson, B. J. (2021). A benthic light index of water quality in the Great Barrier Reef, Australia. Marine Pollution Bulletin, 169, 112539. https://doi.org/10.1016/j.marpolbul.2021.112539 Data Location: This dataset is filed in the eAtlas enduring data repository at: data\nesp5\5.3_Benthic-light

This dataset contains the results of the water quality monitoring program 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 discrete ‘grab’ samples analysed at TropWATER JCU laboratory for nutrients and sediments. Samples were collected throughout the year at nine sites that capture a range of different landuse types across the catchment including sugarcane, bananas, natural rainforest and urban influences. * This dataset is under an embargo period for 18 months from the completion of the project extension (NESP TWQ 4.8). The 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. Monitoring focused primarily on nutrient (nitrogen and phosphorus) and sediment water quality parameters, as these are typically identified as the most important management challenges for north Queensland industries, and considered most relevant to Great Barrier Reef water quality issues. Methods: Sampling at all sites was conducted on a monthly or bimonthly basis during dry-season low flows. 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. Wet season sample frequency was extended to approximate weekly collection during larger, more sustained events during later stages of the wet season. 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. Water samples were collected in pre-rinsed 1-L polypropylene bottles using an extendable sampling pole for total suspended solids (suspended sediments), unfiltered nutrient samples were subsampled into 60-mL polypropylene vials (Sarstedt, Germany), with filterable nutrients filtered on-site through pre-rinsed filter modules (MiniSart 0.45 ?m cellulose acetate, Sartorius, Germany) into six 10-mL polypropylene vials (Sarstedt, Australia). Samples were stored on ice in eskies following sampling and on-site processing, for transport to the laboratory for subsequent analysis. Site water samples were analysed for total nitrogen (TN), ammonium nitrogen (AN), oxidised nitrogen (ON: nitrate + nitrite), filterable reactive phosphorus (FRP) and total suspended solids (TSS). Samples for TN were digested in an autoclave using an alkaline persulfate technique (modified from Hosomi and Sudo, 1987) and the resulting solution simultaneously analysed for ON by segmented flow auto-analysis using an OI Analytical (Texas, USA) Flow Solution IV. The analyses of ON, AN and FRP were also conducted using segmented flow auto-analysis techniques following standard methods (APHA, 2005). A specific urea assay was also conducted to quantify the urea component of DON using a segmented flow analyser modification of the procedures developed by Marsh et al. (1965). Samples for TSS analyses were filtered through pre-weighed Whatman (England) GF/C filter membranes (nominally 1.2 mm pore size) and oven-dried at 103–105 °C for 24 h and reweighed to determine the dry TSS weight as described in APHA (2005). Format: Data consists of an excel spreadsheet with all samples collected at each of the nine sites listed sequentially (by date of collection) on separate, named spreadsheet tabs. Additional information related to unique laboratory sample and job-batch numbers is also listed for QA/QC purposes. Duplicate samples were also collected on occasion at some sites for analytical QA/QC purposes. These samples are identified in a separate column. Grey cell fill indicated a particular parameter was not assessed for that sample. References: APHA., 2005. Standard Methods for the Examination of Water and Wastewaters. American Public Health Association, American Waterworks Association and Water Environment Federation: Washington, USA. Hosomi, M., Sudo, R., 1987. Simultaneous determination of total nitrogen and total phosphorus in freshwater samples using persulfate digestion. Internat. J. Environ. Stud. 27, 267-275.Liaw A., Wiener M., 2002. Classification and regression by randomForest. R News 2, 18–22. Marsh, W.H., Fingerhut, B., Miller, H., 1965. Automated and manual direct methods for the determination of blood urea. Clin. Chem. 11, 624–627. 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.

This data set contains high frequency logging data to measure water depth, water temperature and electrical conductivity in project wetland sites. Coastal wetlands adjacent to the Great Barrier Reef (GBR) have incredible environmental, cultural and economic value. Despite this, many floodplains in the GBR catchments have been modified, impacted or lost entirely because of continuing land use change (such as agricultural, aquaculture, peri-urban/urban, and industrial expansion). Of the floodplains and their wetlands remaining many now provide severely reduced aquatic and avian habitat, due to alien weed infestation and poor water quality. A large number of coastal wetlands have also lost their connectivity with estuaries that flow into the GBR lagoon (e.g. due to earth bunding), which can impact marine and freshwater aquatic (diadromous) species that have a critical estuary lifecycle phase, and rely on this connectivity between the reef and shallow tidal and freshwater wetlands. The overall project objective is to evaluate existing and future coastal wetland system repair investments, covering a combination of project sites across Great Barrier Reef catchment area, to explicitly evaluate how these projects achieve biodiversity improvements, water quality benefits and connectivity with downstream marine coastal habitats. Methods: High frequency water depth logging Water depth, temperature and electrical conductivity were monitored by loggers (CTD-Diver, Eijkelkamp Soil & Water, Netherlands) located in the wetland. The loggers captured data from the bottom of the water column (~ 10 cm above the soil surface) every 20 minutes, and were downloaded as part of routine maintenance visits. Each logger was installed inside a PVC pipe (3m height, 90mm diameter) that was attached to a steel star picket. Loggers were attached to a stainless steel wire cord that was attached to the top of the PVC pipe for easy retrieval (downloading the data and maintenance). The loggers were downloaded every few months. For more details see: Canning, A., Adame F., Waltham, N. J., (2020) Evaluating the services provided by ponded pasture wetlands in Great Barrier Reef catchments – Tedlands case study. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (23pp.). Waltham, N. J. & Canning, A. (2020) Exploring the potential of watercourse repair on an agricultural floodplain. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (75pp.). Format: All data are in Excel format. References: Canning, A., Adame F., Waltham, N. J., (2020) Evaluating the services provided by ponded pasture wetlands in Great Barrier Reef catchments – Tedlands case study. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (23pp.). Waltham, N. J. & Canning, A. (2020) Exploring the potential of watercourse repair on an agricultural floodplain. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (75pp.). Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.13_Coastal-wetland-systems-repair

This record provides an overview of the NESP Marine and Coastal Hub small-scale study - "Scoping Study: Indigenous Participation and Research Needs". This scoping study builds on the engagement undertaken during the development of the NESP proposal for the Marine and Coastal Hub, where 42 Traditional Owners and Native Title holders across mostly northern Australia provided support for our bid. This period of engagement identified the need to consult widely with Indigenous leaders and groups before committing to substantive research projects. This scoping study provides the resources to begin such a consultation with Indigenous partners on their aspirations for the Marine and Coastal Hub. It will also provide a vehicle for the Hub to actively engage the NESP Indigenous Facilitators Network. As part of the Australian Government policy on Closing the Gap and consistent with Part 6 of the Northern Australia Indigenous Development Accord, this scoping study will explore how best to execute increased Indigenous ownership over participatory research and how such a program can deliver on the Department’s stated aspirations of meaningful and substantive engagement of Traditional Owners. This scoping study will bring together key Indigenous leaders and groups to plan, prioritise and evaluate potential research directions. Specifically, this scoping study will engage with Indigenous Australians to develop a cohesive set of prospective projects for investment under future Hub research plans, that deliver up to large-scale, broader picture goals for Indigenous people. This will require marshalling of Indigenous knowledge and development and enhancement of Indigenous relationships. Consultation will involve both Indigenous only and Indigenous-researcher meetings and workshops. We are seeking ethical Indigenous engagement, equitable participation and co-design and co-delivery of national environmental research priorities, while substantially increasing the outcomes for social, economic and cultural terms for Indigenous people. Planned Outputs • Final technical report with analysed data and a short summary of recommendations for policy makers of key findings [written]

This record provides an overview of the NESP Marine and Coastal Hub small-scale study - NESP MaC Project 1.29 a: Great Reef Census - a case study to integrate citizen science data into research output for marine habitat management". For specific data outputs from this project, please see child records associated with this metadata. To maximise our understanding of our marine and coastal environment, we need to take advantage of emerging technologies and approaches. This includes citizen science, community monitoring and Indigenous Rangers. Technology has greatly reduced the gap between mainstream science and community science to the point they may become almost identical in some integrated programs, especially when involving collection of in-field information. The challenge for science is to integrate with the vast opportunities afforded by this congruence. The Great Reef Census (GRC) is an established citizen science innovation project, designed to pilot new ways of capturing reconnaissance citizen science data. By using citizen scientists to both collect and analyse reef images, as well as a team of professional scientists to ensure program rigour, the project is an innovative approach to assessing Great Barrier Reef health that complements and enhance existing monitoring programs. The aim of this project is to demonstrate a citizen science approach can effectively fill gaps in knowledge when assessing marine habitats to improve management outcomes. As a case study, it will demonstrate how citizen science data can be integrated into the monitoring programs across Australia’s marine and coastal environments using new digital technology platforms. The project will also complement ongoing GBR-based research and provide critical knowledge gaps through end-user engagement with GBRMPA’s CoTS Control Program and Australia’s reporting on the health of the GBR. Our study will 1) scrutinise, validate and synthesize expert versus citizen scientist analyses of geo-referenced images collected during the Great Reef Census Year 1 field campaign and using the analysis platform, and 2) explore a re-structured online analysis platform that integrates machine learning and citizen science to extract more output from a growing image library collected during field efforts. The end product will provide a case-study evaluation of the benefits and capability of citizen-science programs as well as assisting decision-making capacity based on real-time broad spatial scale information on the Great Barrier Reef. The output will provide a demonstrated case study of meaningful citizen science application to assess marine habitats which can be applied more broadly to tropical marine habitats. Planned Outputs • Synthesis R data package • Final technical report with analysed data and a short summary of recommendations for policy makers of key findings [written]

This generated data set contains summaries (daily, monthly, annual) of the eReefs CSIRO biogeochemistry model v3.1 (https://research.csiro.au/ereefs) outputs at 4km resolution, generated by the AIMS eReefs Platform (https://ereefs.aims.gov.au/ereefs-aims). These summaries are derived from the original daily 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 (https://thredds.ereefs.aims.gov.au/thredds/ ) in NetCDF format. The GBR4 BioGeoChemical (BGC) model builds on the GBR4 hydrodynamic model by modelling the water quality (nutrients and suspended sediment) and key ecological processes (coral, seagrass, plankton) that drive the water chemistry. This model allows us to better understand how water quality is affected by land runoff. Detailed information about the model can be found in the paper: CSIRO Environmental Modelling Suite (EMS): Scientific description of the optical and biogeochemical models (vB3p0). The original model output data set contains three (3) scenarios, each of which have an equivalent set of summaries in this data set: Baseline (GBR4_H2p0_B3p1_Cq3b_Dhnd): Paddock to Reef SOURCE Catchments with 2019 catchment condition from Dec 1, 2010 – Jun 30, 2018 (used for GBR Report Card 8 published in 2019), Empirical SOURCE with 2019 catchment condition, Jul 1, 2018 – April 30, 2019. This scenario most closely corresponds to historic BioGeoChemical conditions of the reef (see limitation). Pre-industrial (GBR4_H2p0_B3p1_Cq3P_Dhnd): Paddock to Reef SOURCE Catchments with Pre-Industrial catchment condition from Dec 1, 2010 – Jun 30, 2018 (used for GBR Report Card 8 published in 2019), Empirical SOURCE with Pre-Industrial catchment, Jul 1, 2018 – April 30, 2019. Reduced (GBR4_H2p0_B3p1_Cq3R_Dhnd): SOURCE Catchments with 2019 catchment condition (q3b) with anthropogenic loads (q3b – q3p) reduced according to the percentage reductions of DIN, PN, PP and TSS specified in the Reef 2050 Water Quality Improvement Plan (WQIP) 2017-2022 as calculated in Brodie et al., (2017). Further, the reductions are adjusted to account for the cumulative reductions already achieved between 2014 and 2019 that will be reflected in the 2019 catchment condition used in the Baseline scenario (q3b). For more information about the biogeochemical model naming protocol, see https://research.csiro.au/ereefs/models/models-about/biogeochemical-simulation-naming-protocol/ 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://nextcloud.eatlas.org.au/apps/sharealias/a/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf). Data Dictionary: This dataset contains a subset of the original BGC model variables. This subset was chosen based on those variables that are most likely to have utility. Additional information about these variables can be found using the OPeNDAP browser via the AIMS eReefs THREDDS server (https://thredds.ereefs.aims.gov.au/thredds/ ). alk: [mmol m-3] Total alkalinity BOD: [mg O m-3] Biochemical Oxygen Demand CH_N: [g N m-2] Coral host N Chl_a_sum: [mg Chl m-3] Total Chlorophyll CO32: [mmol m-3] Carbonate CS_bleach: [d-1] Coral bleach rate CS_Chl: [mg Chl m-2] Coral symbiont Chl CS_N: [mg N m-2] Coral symbiont N DIC: [mg C m-3] Dissolved Inorganic Carbon DIN: [mg N m-3] Dissolved Inorganic Nitrogen DIP: [mg P m-3] Dissolved Inorganic Phosphorus DOR_C: [mg C m-3] Dissolved Organic Carbon DOR_N: [mg N m-3] Dissolved Organic Nitrogen DOR_P: [mg P m-3] Dissolved Organic Phosphorus Dust: [kg m-3] Dust EFI: [kg m-3] Ecology Fine Inorganics EpiPAR_sg: [mol photon m-2 d-1] Light intensity above seagrass eta: [metre] Surface Elevation FineSed: [kg m-3] FineSed Fluorescence: [mg chla m-3] Simulated Fluorescence HCO3: [mmol m-3] Bicarbonate Kd_490: [m-1] Vert. att. at 490 nm MA_N: [g N m-2] Macroalgae N MA_N_pr: [mg N m-2 d-1] Macroalgae net production month_EpiPAR_sg: [mol photon m-2] Monthly dose light above seagrass MPB_Chl: [mg Chl m-3 ] Microphytobenthos chlorophyll MPB_N: [mg N m-3] Microphytobenthos N Mud-carbonate: [kg m-3] Mud-carbonate Mud-mineral: [kg m-3] Mud-mineral Nfix: [mg N m-3 s-1] N2 fixation NH4: [mg N m-3] Ammonia NO3: [mg N m-3] Nitrate omega_ar: [nil] Aragonite saturation state Oxy_sat: [%] Oxygen saturation percent Oxygen: [mg O m-3] Dissolved Oxygen P_Prod: [mg C m-3 d-1] Phytoplankton total productivity PAR: [mol photon m-2 s-1] Av. PAR in layer PAR_z: [mol photon m-2 s-1] Downwelling PAR at top of layer pco2surf: [ppmv] oceanic pCO2 (ppmv) PH: [log(mM)] PH PhyL_Chl: [mg Chl m-3 ] Large Phytoplankton chlorophyll PhyL_N: [mg N m-3] Large Phytoplankton N PhyS_Chl: [mg Chl m-3 ] Small Phytoplankton chlorophyll PhyS_N: [mg N m-3] Small Phytoplankton N PhyS_NR: [mg N m-3] Small Phytoplankton N reserve PIP: [mg P m-3] Particulate Inorganic Phosphorus R_400: [sr-1] Remote-sensing reflectance @ 400 nm R_410: [sr-1] Remote-sensing reflectance @ 410 nm R_412: [sr-1] Remote-sensing reflectance @ 412 nm R_443: [sr-1] Remote-sensing reflectance @ 443 nm R_470: [sr-1] Remote-sensing reflectance @ 470 nm R_486: [sr-1] Remote-sensing reflectance @ 486 nm R_488: [sr-1] Remote-sensing reflectance @ 488 nm R_490: [sr-1] Remote-sensing reflectance @ 490 nm R_510: [sr-1] Remote-sensing reflectance @ 510 nm R_531: [sr-1] Remote-sensing reflectance @ 531 nm R_547: [sr-1] Remote-sensing reflectance @ 547 nm R_551: [sr-1] Remote-sensing reflectance @ 551 nm R_555: [sr-1] Remote-sensing reflectance @ 555 nm R_560: [sr-1] Remote-sensing reflectance @ 560 nm R_590: [sr-1] Remote-sensing reflectance @ 590 nm R_620: [sr-1] Remote-sensing reflectance @ 620 nm R_640: [sr-1] Remote-sensing reflectance @ 640 nm R_645: [sr-1] Remote-sensing reflectance @ 645 nm R_665: [sr-1] Remote-sensing reflectance @ 665 nm R_667: [sr-1] Remote-sensing reflectance @ 667 nm R_671: [sr-1] Remote-sensing reflectance @ 671 nm R_673: [sr-1] Remote-sensing reflectance @ 673 nm R_678: [sr-1] Remote-sensing reflectance @ 678 nm R_681: [sr-1] Remote-sensing reflectance @ 681 nm R_709: [sr-1] Remote-sensing reflectance @ 709 nm R_745: [sr-1] Remote-sensing reflectance @ 745 nm R_748: [sr-1] Remote-sensing reflectance @ 748 nm R_754: [sr-1] Remote-sensing reflectance @ 754 nm R_761: [sr-1] Remote-sensing reflectance @ 761 nm R_764: [sr-1] Remote-sensing reflectance @ 764 nm R_767: [sr-1] Remote-sensing reflectance @ 767 nm R_778: [sr-1] Remote-sensing reflectance @ 778 nm salt: [PSU] Salinity Secchi: [m] Secchi from 488 nm SG_N: [g N m-2] Seagrass N SG_N_pr: [mg N m-2 d-1] Seagrass net production SG_shear_mort: [g N m-2 d-1] Seagrass shear stress mort SGD_N: [g N m-2] Deep seagrass N SGD_N_pr: [mg N m-2 d-1] Deep seagrass net production SGD_shear_mort: [g N m-2 d-1] Deep seagrass shear stress mort SGH_N: [g N m-2] Halophila N SGH_N_pr: [mg N m-2 d-1] Halophila net production SGHROOT_N: [g N m-2] Halophila root N SGROOT_N: [g N m-2] Seagrass root N TC: [mg C m-3] Total C temp: [degrees C] Temperature TN: [mg N m-3] Total N TP: [mg P m-3] Total P Tricho_Chl: [mg Chl m-3] Trichodesmium chlorophyll Tricho_N: [mg N m-3] Trichodesmium Nitrogen TSSM: [g TSS m-3] TSS from 645 nm (Petus et al., 2014) Z_grazing: [mg C m-3 d-1] Zooplankton total grazing Zenith2D: [rad] Solar zenith ZooL_N: [mg N m-3] Large Zooplankton N ZooS_N: [mg N m-3] Small Zooplankton N z: [m] Z coordinate (depth) time: [days since 1990-01-01 00:00:00 +10] Time latitude: [degrees_north] Latitude (geographic projection) longitude: [degrees_east] Longitude (geographic projection) Depths: This data set contains a subset of the depths available in the source data set. The depth is represented by the 'k' dimension. The following table shows the depths associated with each 'k' value. k, depth 16, -0.5, 15, -1.5, 14, -3.0, 13, -5.55, 12, -8.8, 11, -12.75, 10, -17.75, 9, -23.75, 8, -31.0, 7, -39.5, 6, -49.0, 5, -60.0, 4, -73.0, 3, -88.0, 2, -103.0, 1, -120.0, 0, -145.0. 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. A technical assessment of the skill level of the BGC version 3.1 model (see links) shows that the absolute accuracy of the BGC model varies significantly with variable and location. As a result care should be taken to ensure the model is fit-for-purpose and in general BGC results should used in combination with second sources of information for making recommendations. The modelled scenarios run for version 3.1 of the BGC model were developed for the purpose of comparing catchment run off effect comparison. As such they were driven with historic weather and river flow boundary conditions, but the sediment and nutrient loads were based on the results of the 2019 Source Catchment modelling. In this catchment modelling the land use is static over the simulation run. This means that for the 'Baseline' scenario this uses estimated land use from 2019 applied over all years (2010 - 2019). As a result improvements in land practices are effectively back dated to start of the simulation (2010). This results in early years in the simulation having slightly lower nutrient and sediment loads then actually happened. The BGC modelling team indicated this approach is likely to introduce small additional errors in places where the land practices have improved significantly, but are likely to be smaller than the inherent errors in the model. These errors only apply if the Baseline model data is interpreted as an estimate of historic conditions, rather than the original intended purpose of the scenario comparison. References: Reef 2050 Water Quality Improvement Plan (WQIP) 2017-2022. https://www.reefplan.qld.gov.au/__data/assets/pdf_file/0017/46115/reef-2050-water-quality-improvement-plan-2017-22.pdf

This dataset consists of one data file (spreadsheet) from a 1 year large tank herbicide degradation experiment in seawater, containing 4 different light and sediment treatments. Each tab contains concentration data at each time point for a single herbicide under each of the four light and sediment conditions. \n \nThe aim of this study was to conduct a year-long degradation experiment using concentrations of commonly detected herbicides in a series of replicate open tanks. We included different light conditions and natural sediments in treatments to improve the ecological relevance and applicability for inclusion by regulators and resource managers in future risk assessments. \n \n \nMethods: \n \nThis study describes a series of outdoor open tank experiments to measure the degradation of herbicides under conditions more natural than those applied in standard flask tests. These tests were conducted in large open tanks with water circulation over the course of a year under both fully dark and light conditions (partially shaded, natural diurnal cycle) and in the presence and absence of natural sediments. Intertidal sediments, containing no detectable concentrations of herbicides (see Results) were collected from the intertidal zone of low tide from Cockle Bay, Magnetic Island, Queensland (19°10' S, 146° 49' E). Each of the sediment treatments contained 3.8 kg sediment following the removal of large sediments > 2 mm by sieving. To allow for periodic sediment sampling without disruption of sediment communities, the sediments were distributed into a single large and 11 small dishes in each tank which could be removed without disturbing the majority of the sediment. The large dish (25 cm x 22 cm 5 cm) were filled with 3.0 kg of sediment (wet weight) and the small ceramic dishes (6.5 cm diameter) 70 g sediment. Physical and chemical information on the seawater and sediments used the open tank experiment may be found in Mercurio et al. (2016). \n \nThe open fibreglass tanks (120 l) were situated in an outdoor glasshouse in two stacked rows of 10 (20 in the top rows and 20 in the bottom rows). The top 20 tanks were partially shaded (70%) and exposed to a natural diurnal cycle (maximum of ~700 µE photons m-2s-1) over the course of the experiment (Li-250A light meter, Li-Cor, Lincoln, USA). The bottom row was fully shaded (no light penetration) at all times. Evaporation was minimised with loose-fitting clear acrylic lids on the top row and fully opaque foam on the bottom row and water continuously circulated in each tank using Turbelle Nanostream pumps. After every sampling period evaporation losses were replenished with equal volumes of MilliQ freshwater. Logged temperatures averaged 28°C (range 21-37°C) in the light and 26°C (21-32°C) in the dark. \n \nHerbicides included: \nAtrazine, Diuron, Hexazinone, Tebuthiuron, Metolachlor, 2,4-D, \nWater samples were taken periodically and analysed by high performance liquid chromatography-mass spectrometry (HPLC-MS/MS) as per Mercurio et al. Herbicide persistence in seawater simulation experiments. PLoS ONE 10(8): e0136391. \nUncertainty in the analytical method for repeated injections into the LC-MS results in a concentration uncertainty of approximately ± 0.2 µg/L. Only concentrations greater than the limit of reporting (LOR) were used to calculate half-lives (t1/2). The limit or reporting was designated as 5 x the detection limit = 1 µg/L to maximise accuracy of the estimations. \nReductions in the concentration of herbicides were plotted to predict the persistence of each herbicide (its 'half-life'). Half-lives for zero order kinetics were obtained by plotting the concentration vs time: t1/2 = 0.5 C0/k0, where C0 is the initial concentration and k0 is the slope. \nThe emergence of the three herbicide breakdown products were also quantified: Desisopropyl Atrazine, Desethyl Atrazine from Atrazine and 3,4-dichloroaniline from Diuron. \n \n \nFormat: \n \n- Herbicide_persistence_open_tank_10052016.xlsx (1.6 MB): Excel spreadsheet containing 8 sheets, one sheet for each herbicide and one for temperature logger measurements. \n \n \nData Dictionary: \n \nCommon: \n \n- Time (days): Time in days from the start of the experiment \n- Sample replicate: Up to four replicate tanks were used containing herbicides and these were incubated under different light and sediment conditions \n \nAtrazine: \n \n- Dark no sediment Atrazine: Concentration of Atrazine remaining in open tanks under dark conditions without sediment \n- Dark no sediment desethyl atrazine (DEA): Concentration of desethyl atrazine (DEA) remaining in open tanks under dark conditions without sediment \n- Dark no sediment desisopropyl (DIA): Concentration of desisopropyl (DIA) remaining in open tanks under dark conditions without sediment \n- Dark with sediment Atrazine: Concentration of Atrazine remaining in open tanks under dark conditions with sediment \n- Dark with sediment desethyl atrazine (DEA): Concentration of desethyl atrazine (DEA) remaining in open tanks under dark conditions with sediment \n- Dark with sediment desisopropyl (DIA): Concentration of desisopropyl (DIA) remaining in open tanks under dark conditions with sediment \n- Light no sediment Atrazine: Concentration of Atrazine remaining in open tanks under light conditions without sediment \n- Light no sediment desethyl atrazine (DEA): Concentration of desethyl atrazine (DEA) remaining in open tanks under light conditions without sediment \n- Light no sediment desisopropyl (DIA): Concentration of desisopropyl (DIA) remaining in open tanks under light conditions without sediment \n- Light with sediment Atrazine: Concentration of Atrazine remaining in open tanks under light conditions with sediment \n- Sample lost: missing data because the sample was lost \n- Light with sediment desethyl atrazine (DEA): Concentration of desethyl atrazine (DEA) remaining in open tanks under light conditions with sediment \n- Sample lost: missing data because the sample was lost \n- Light with sediment desisopropyl (DIA): Concentration of desisopropyl (DIA) remaining in open tanks under light conditions with sediment \n- Sample lost: missing data because the sample was lost \n \nDiuron: \n \n- Dark no sediment Diuron: Concentration of Diuron remaining in open tanks under dark conditions without sediment \n- Dark no sediment 3,4-dichloroaniline: Concentration of 3,4-dichloroaniline remaining in open tanks under dark conditions without sediment \n- Dark with sediment Diuron: Concentration of Diuron remaining in open tanks under dark conditions with sediment \n- Dark with sediment 3,4-dichloroaniline: Concentration of 3,4-dichloroaniline remaining in open tanks under dark conditions with sediment \n- Light no sediment Diuron: Concentration of Diuron remaining in open tanks under light conditions without sediment \n- Light no sediment 3,4-dichloroaniline: Concentration of 3,4-dichloroaniline remaining in open tanks under light conditions without sediment \n- Light with sediment Diuron: Concentration of Diuron remaining in open tanks under light conditions with sediment \n- Sample lost: missing data because the sample was lost \n- Light with sediment 3,4-dichloroaniline: Concentration of 3,4-dichloroaniline remaining in open tanks under light conditions with sediment \n- Sample lost: missing data because the sample was lost \n \nHexazinone: \n \n- Dark no sediment Hexazinone: Concentration of Hexazinone remaining in open tanks under dark conditions without sediment \n- Dark with sediment Hexazinone: Concentration of Hexazinone remaining in open tanks under dark conditions with sediment \n- Light no sediment Hexazinone: Concentration of Hexazinone remaining in open tanks under light conditions without sediment \n- Light with sediment Hexazinone: Concentration of Hexazinone remaining in open tanks under light conditions with sediment \n- Sample lost: missing data because the sample was lost \n \nTebuthiuron: \n \n- Dark no sediment Tebuthiuron: Concentration of Tebuthiuron remaining in open tanks under dark conditions without sediment \n- Dark with sediment Tebuthiuron: Concentration of Tebuthiuron remaining in open tanks under dark conditions with sediment \n- Light no sediment Tebuthiuron: Concentration of Tebuthiuron remaining in open tanks under light conditions without sediment \n- Light with sediment Tebuthiuron: Concentration of Tebuthiuron remaining in open tanks under light conditions with sediment \n- Sample lost: missing data because the sample was lost \n \nMetolachlor: \n \n- Dark no sediment Metolachlor: Concentration of Metolachlor remaining in open tanks under dark conditions without sediment \n- Dark with sediment Metolachlor: Concentration of Metolachlor remaining in open tanks under dark conditions with sediment \n- Light no sediment Metolachlor: Concentration of Metolachlor remaining in open tanks under light conditions without sediment \n- Light with sediment Metolachlor: Concentration of Metolachlor remaining in open tanks under light conditions with sediment \n \n24D: \n \n- Dark no sediment 24D: Concentration of 2,4-D remaining in open tanks under dark conditions without sediment \n- Dark with sediment 24D: Concentration of 2,4-D remaining in open tanks under dark conditions with sediment \n- Light no sediment 24D: Concentration of 2,4-D remaining in open tanks under light conditions without sediment \n- Light with sediment 24D: Concentration of 2,4-D remaining in open tanks under light conditions with sediment \n \nTemperatures: \n \n- Date Time, GMT+10:00: data and time when temperature was logged \n- Logger: Temperature logger number \n- Temp, °C: temperature logged \n- Minimum: minimum temperature for each logger \n- Maximum: maximum temperature for each logger \n- Mean: mean temperature for each logger \n \n \nReferences: \n \nMercurio P, Mueller JF, Eaglesham G, O'Brien J, Flores F, Negri AP. 2016. Degradation of herbicides in the tropical marine environment: Influence of light and sediment. PLoS ONE in press. \n \n \nData Location: \n \nThe original data is saved in the eAtlas enduring data repository: data\NERP-TE\4.2_Herbicide-effects\\n