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    The dataset consists of four data files from a 20-day experiment to investigate different photophysiological responses of two species of coral (Acropora millepora and Pachyseris speciosa) to constant and variable daily light integrals. Methods: Eight partial colonies each of Pachyseris speciosa (from 5 – 8 m depth) and Acropora millepora (from 3 – 5 m depth) were collected from Davies Reef, central Great Barrier Reef, Australia (-18.823390, 147.6518563) in July 2016, and taken to the National Sea Simulator at the Australian Institute of Marine Science (AIMS), Townsville. Experiment consisted of four types of light exposure treatments, wherein nubbins from the partial colonies were either exposed to 20 days of high light (32 mol photons m-2 d-1), low light (6 mol photons m-2 d-1), or alternating variable light treatments. Four sets of photoacclimation and physiological responses were measured and the corresponding data has been placed in four separate csv files; (1) Fluorescence measurements were conducted using a diving pulse-amplitude modulated fluorometer (DPAM). Measurements were taken twice with the DPAM: once at 0.5 h before sunrise, to assess the maximum quantum yield of photosystem II (Fv/Fm), and at noon, after 0.5 h exposure to maximum irradiance, to assess effective quantum yield (PSII). For each nubbin, at least five measurements were taken from different regions on each nubbin and the values averaged. The excitation pressures on PSII, (see Ralph et al. 2016) was assessed to estimate the degree of photoinhibition versus light limitation. Non-photochemical quenching (NPQ), also derived from PAM pre-dawn and noontime measurements based on equations by Genty et al 1987, was measured to assess the amount of excess photon energy dissipated safely as heat. (2) At the end of the experiment, the concentration of chlorophyll a (photosynthetic) and total carotenoids (photosynthetic and photoprotective) of nubbins were compared between treatments. Tissue was removed from the skeleton with an air gun and filtered seawater, and homogenized. The slurry was centrifuged for 6-8 min at 1,500 g and the coral host supernatant was separated from the symbiont pellet. The pellet was then rinsed with filtered seawater and re-centrifuged at 10,000 g for 3 min prior to extraction. Pigments were obtained via a double extraction procedure (1 mL 95% ethanol at 4oC for 20 minutes each, with sonicator), and the absorbance was spectrophotomerically measured at 665, 664, 649 and 470 nm wavelengths. Concentrations of chlorophyll a and total carotenoids (µg/mL) were calculated based on equations by Lichtenthaler (1987) and Ritchie (2008) and standardized to nubbin surface area, which was estimated via a single wax dip protocol (Veal et al 2010). (3) At the end of the experiment, 18 nubbins were selected for respirometry measurements. Their ceramic plugs were carefully cleaned to remove algal growth. Nubbins were individually placed in 634 mL sealed stirred chambers that contained oxygen sensor spots (optodes), and the Firesting hardware/software (Pyroscience, Germany) was used to measure oxygen concentrations within the chambers every minute. Incubations ran for an hour each at ten light levels (0, 15, 40, 80, 120, 200, 300, 500, 700 and 1000 µmol photons m-2 s-1), measured with an upwards facing, calibrated, cosine corrected light sensor (meter LI-250A, sensor LI-192, Li-COR, USA). Water was flushed in the chambers at the beginning of each light level measurement. Rates of oxygen consumption (estimated respiration in the dark) and production (estimated net photosynthesis in the light) were standardized to coral surface area estimates derived from the wax dipping procedure. Photosynthesis to irradiance (P-I) curves were fitted to the data using a hyperbolic tangent fit, as described by Jassby and Platt (1976) using the ‘stats’ package (version 3.6.0) in the statistical platform R (version 3.4.0, R Development Core Team 2017). Parameters for maximum photosynthetic production (Pmax), saturation irradiance (Ik) and dark respiration (Rdark) for each treatment were estimated from fitted models. Net daily oxygen production (Pn) was calculated by predicting production using the P-I curves at actual logged experimental light levels, over a 24 h period. (4) Growth rates of A. millepora were assessed as differences in buoyant weight over time (Davies 1989). Nubbins were individually weighed to the nearest 0.001 g by suspending them on a tray below a semi-micro balance (Shimadzu AUW220D, Japan) in a water bath at ~25 OC. The percent change in buoyant weight between days 8 and 20 was assessed. Literature Cited Lichtenthaler HK. [34] Chlorophylls and carotenoids: Pigments of photosynthetic biomembranes. Methods in Enzymology. 148: Academic Press; 1987. p. 350-82. Ritchie RJ. Universal chlorophyll equations for estimating chlorophylls a, b, c, and d and total chlorophylls in natural assemblages of photosynthetic organisms using acetone, methanol, or ethanol solvents. Photosynthetica. 2008;46(1):115–26. Veal CJ, Holmes G, Nunez M, Hoegh-Guldberg O, Osborn J. A comparative study of methods for surface area and three-dimensional shape measurement of coral skeletons. Limnology and Oceanography: Methods. 2010;8(5):241-53. doi: 10.4319/lom.2010.8.241. Jassby AD, Platt T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnology and Oceanography. 1976;21(4):540-7. doi: 10.4319/lo.1976.21.4.0540. Davies S. Short-term growth measurements of corals using an accurate buoyant weighing technique. Marine Biology. 1989;101(3):389–95. doi: 10.1007/BF00428135. Ralph PJ, Hill R, Doblin MA, Davy SK (2016) Theory and application of pulse amplitude modulated chlorophyll fluorometry in coral health assessment. In: Diseases of Coral, pp. 506–523. Format: The dataset consists of four separate components, stored as .csv files, pertaining to the different physiological aspects used to understand coral responses to variable light conditions; fluorometry (85KB), pigment analysis (6KB), respirometry (9KB) and growth (1KB). Data Dictionary: pam.csv - Date: Date of sampling (DD/MM/YY) - Group: Which sampling group the individual was placed in. - Treatment: Which treatment the individual was in, where HL is High Light, LL is Low Light, VLH is Variable Light starting High and VLL is Variable Light starting Low. - Tank: Tank number each nubbin resided in - Species: Either Acropora millepora or Pachyseris speciosa - Coral_ID: Colony identification - Fo_mean: minimum fluorescence yield in dark - Fm_mean: maximum fluorescence yield in dark - DA_yield; Refers to the maximum quantum yield, quantifies the maximum potential for photosynethesis through proportion of available photosystems in a dark-adapted state (dimensionless) - F_mean; steady-state fluorescence yield in light - Fm._mean; maximum fluorescence yield in light - LA_yield: refers to the effective quantum yield, quantifying the relative number of open to closed photosystems in a light-adapted state (dimensionless) - Qm; Excitation pressure of photosystem II, which is a relationship between effective and maximum quantum yields and gives an idea of the relative light-limitation or photoinhibitory stress on the coral (dimensionless) pigments.csv - Treatment: Which treatment the individual was in, where HL is High Light, LL is Low Light, VLH is Variable Light starting High and VLL is Variable Light starting Low. - Tank: Tank number - Species: Either Acropora millepora or Pachyseris speciosa - Coral_ID: Colony identification - Individual_ID: Individual identification - Surface: Relevant for P. speciosa only, refers to whether tissue was taken from the top or the bottom of the nubbin - surfaceArea: Surface area in cm2 - ChlA: estimated total of chlorophyll a (micrograms) per individual coral nubbin - Caro: estimated total of total carotenoids (micrograms) per individual coral nubbin - chlaSA: estimate of chlorophyll a by surface area (micrograms per cm2) per individual coral nubbin - caroSA: estimate of total carotenoids by surface area (micrograms per cm2) per individual coral nubbin - Ratio: Ratio of chlorophyll a to carotenoids respirometry.csv - Date: Date of sampling (DD/MM/YY) - Group: Which sampling group the individual was placed in, based solely on order of respirometry run - Pattern: Whether the treatment was constant (HL or LL) or variable (VLH or VLL) - Treatment: Which treatment the individual was in, where HL is High Light, LL is Low Light, VLH is Variable Light starting High and VLL is Variable Light starting Low - Light: Whether light levels were low or high - Irradiance (umolEm-2s-1); Instantaneous irradiance measurement in micromoles photons per m2 per second - Species: Either Acropora millepora or Pachyseris speciosa - ID: Individual ID - Production_mgO2hr-1: Oxygen production based on respirometry in milligrams of oxygen produced per hour weight.csv (only Acropora millepora in this dataset) - Treatment: Which treatment the individual was in, where HL is High Light, LL is Low Light, VLH is Variable Light starting High and VLL is Variable Light starting Low - Tank: Tank number - Coral_ID: Colony and individual ID - Percent_change: percent change in buoyant weight Data Location: This dataset is filed in the eAtlas enduring data repository at: data\2016-18-NESP-TWQ-2\2.3.1_Benthic-light\AU_NESP-TWQ-2.3.1_AIMS_BenthicLight_experiment2

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    As a sub-project of the now discontinued water quality component of the AIMS Long-term Monitoring Project, sediments were examined along cross-shelf transects commencing at the mouths of the Johnstone and Barron Rivers, which drain heavily cultivated areas. Results were compared with sediments from a transect commencing near the Pascoe River, which drains an uncultivated area of Cape York. Observations were made between November 1992 and April 1996. Sampling was conducted in alternate dry and wet seasons 1992-1993 but only in wet (summer) season 1994 and 1995. Inshore stations were located within 1 km of the shore and seaward stations approximately 20km offshore. \n \nStations were located along and across the Great Barrier Reef shelf from 3 river mouths (Barron, Johnstone and Pascoe) out to individual reefs: Pascoe River mouth, off Portland Roads, Dolphin Reef, Bourke Reef; Barron River mouth, Port Douglas, Low Isles, Green Island, Thetford Reef, Fitzroy Island; Flying Fish Point (at the mouth of the North Johnstone River), Russell Heads, North Barnard Island, Flora Reef, Feather Reef, Ellison Reef. \n \nNutrient flux samples were examined for dissolved inorganic nutrients (ammonium, nitrite, nitrate, phosphate) using standard automated techniques. Solid-phase nutrients were measured in bulk sediment samples for total organic carbon, total carbon, total nitrogen and total phosphorus. Total carbonate was estimated by the difference between total carbon and total organic carbon concentrations multiplied by 8.33.\n This research was undertaken to determine the extent of temporal and spatial variability of nutrient regeneration rates and nutrient concentrations in surface sediments of the far northern GBR shelf by: \n1. assess the role of river run-off in delivering nutrient and sediment loads in the GBR by monitoring changes in the quantities of nutrients (and related variables) in the interstitial porewaters and bulk sediments. \n2. measure the rates of nutrient flux across the sediment-water interface in order to determine the flux nutrients between the sediments and the overlying water column.\n Parameters measured: Carbon/Nitrogen Total Bulk Sediment, Particulate Organic Carbon (POC), Sedimentary Organic Carbon, Total Dissolved Carbon, Dissolved Inorganic Nitrogen, Total Dissolved Nitrogen, Total Dissolved Phosphorus, Salinity. \n \nThe water quality component of the AIMS Long-term Monitoring Project has a separate metadata record.\n

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    This dataset summarises benthic surveys of seagrass for Dugong and Turtle habitats at Orman Reefs, Torres Strait. The site data describes seagrass coverage estimations at 279 intertidal sites; while the meadow data groups sites into six (6) individual meadows. Data captured includes information on visual estimates for seagrass species, substrate, biomass, diversity, percent cover for benthic macro-invertebrates and algae. Methods: A helicopter was used for the intertidal surveys following TropWATER’s methods to assess areas at high risk from shipping accidents in Torres Strait (Carter et al. 2013). At each site the helicopter enters a low hover position while seagrass was ranked and species composition estimated from three 0.25 m2 quadrats placed randomly within a 10m2 circular area. Seagrass above-ground biomass was determined using the “visual estimates of biomass” technique (Mellors 1991) using trained observers. This involves ranking seagrass biomass while referring to a series of quadrat photographs of similar seagrass habitats for which the above-ground biomass has been previously measured. Three separate biomass scales are used: low biomass, high biomass, and Enhalus biomass. The percent contribution of each seagrass species to total above-ground biomass within each quadrat is also recorded. At the completion of sampling each observer ranks a series of calibration quadrats. A linear regression is then calculated for the relationship between the observer ranks and the harvested values. This regression is used to calibrate above-ground biomass estimates for all ranks made by that observer during the survey. Biomass ranks are then converted to above-ground biomass in grams dry weight per square metre (gdw m-2). Format: All survey data were entered into a Geographic Information System (GIS) developed for Torres Strait using ArcGIS 10.4. Rectified colour satellite imagery of Orman Reefs (Source: ESRI, Landsat 2018), field notes and aerial photographs taken from the helicopter during surveys were used to identify geographical features, such as reef tops, channels and deep-water drop-offs, to assist in determining seagrass meadow boundaries. Three (3) GIS layers were created to describe spatial features of the region: a site layer, seagrass meadow layer, and seagrass biomass interpolation layer. eAtlas Processing: The original data was provided as ArcMap Layer Packages which were converted to open formats (Shapefile, CSV and GeoTiff) for use in the eAtlas mapping system and as part of the dataset download. These conversion were performed with no modifications to the underlying data. Site layer This layer contains data collected at each site, including: - Temporal details: survey date and time. - Spatial details: latitude/longitude, dbMSL. - Habitat information: sediment type; seagrass information including presence/absence and above-ground biomass (total and for each species); percent cover of seagrass, algae, BMI and open substrate; percent contribution of algae functional groups and BMI categories. - Sampling method and any relevant comments. Seagrass meadow layer Seagrass presence/absence site data was used to construct the meadow (polygon) layer. The meadow layer provides summary information for all sites within the meadow, including: 1. Habitat information – seagrass species present, meadow community type, meadow density, mean meadow biomass ± standard error (s.e.), meadow area ± reliability estimate (R), and number of sites within the meadow. 2. Sampling methods and any relevant comments. Meadow community type was determined according to seagrass species composition within each meadow. Species composition was based on the percent each species’ biomass contributed to mean meadow biomass. A standard nomenclature system was used to categorise each meadow (Table 1). This nomenclature also included a measure of meadow density categories (light, moderate, dense) determined by mean biomass of the dominant species within the meadow (Table 2). Mapping precision estimates (in metres) were based on the mapping method used for that meadow (Table 3). Mapping precision estimates ranged from 10-20m for intertidal seagrass meadows and up to 100m for meadow mapping precision estimates based on the distance between sites with and without seagrass. Mapping precision estimate was used to calculate an error buffer around each meadow; the area of this buffer is expressed as a meadow reliability estimate (R) in hectares. Table 1. Nomenclature for seagrass community types. Community type (Species composition) Species A (Species A is 90-100% of composition) Species A with Species B (Species A is 60-90% of composition) Species A with Species B/Species C (Species A is 50% of composition) Species A/Species B (Species A is 40-60% of composition) Table 2. Density categories and mean above-ground biomass ranges for each species used in determining seagrass community density. Species: H. ovalis Categories: Light (<1), Moderate (1 - 5), Dense (>5) Species: C. serrulata, C. rotundata, S. isoetifolium, T. hemprichii Categories: Light (<5), Moderate (5 - 25), Dense (>25) Species: E. acoroides, T. ciliatum Categories: Light (<40), Moderate (40 - 100), Dense (>100) Table 3. Mapping precision and methods for seagrass meadows. Mapping precision: Mapping method 10-20 m: - Meadow boundaries mapped in detail by GPS from helicopter - Intertidal meadows completely exposed or visible at low tide - Relatively high density of mapping and survey sites - Recent aerial photography and satellite imagery aided in mapping 50-100 m: - Meadow boundaries determined from helicopter and camera - Inshore boundaries mapped from helicopter - Offshore boundaries interpreted from survey sites and satellite imagery - Relatively high density of mapping and survey sites Seagrass biomass interpolation layer An inverse distance weighted (IDW) interpolation was applied to seagrass site data to describe spatial variation in seagrass biomass across Orman Reefs meadows. The interpolation was conducted in ArcMap 10.4. Further information can be found in this publication: Carter AB and Rasheed MA (2018), “Torres Strait Seagrass Long-term Monitoring: Dugong Sanctuary, Dungeness Reef and Orman Reefs”, JCU Publication, Report no. 18/17, Centre for Tropical Water & Aquatic Ecosystem Research, Cairns Data Dictionary: Site layer 1. Temporal survey details: - SITE: unique identifier within the Site Layer representing a single sample site. - DATE 2. Spatial survey details - LOCATION - LATITUDE - LONGITUDE 3. Substrate information - SUBSTRATE: tags identifying the types of substrates at the sample site. Possible tags are: Sand, Shell, Reef, Mud, Rubble, Rock 4. Seagrass information - PRESENCE: the presence and absense of seagrass, 0 as absence and 1 as presence - EA: estimated biomass of "Enhalus acoroides" at sample site. Unit is gdw m-2. - TH: estimated biomass of "Thalassia hemprichii" at sample site. Unit is gdw m-2. - CR: estimated biomass of "Cymodocea rotundata" at sample site. Unit is gdw m-2. - CS: estimated biomass of "Cymodocea serrulata" at sample site. Unit is gdw m-2. - TC: estimated biomass of "Thalassodendron ciliatum" at sample site. Unit is gdw m-2. - SI: estimated biomass of "Syringodium isoetifolium" at sample site. Unit is gdw m-2. - HO: estimated biomass of "Halophila ovalis" at sample site. Unit is gdw m-2. - BIOMASS: estimated total biomass for sample site calculated from the mean of the 3 replicate quadrats. Process is to estimate total biomass for the site, then estimate percentage of each seagrass species at the site, then attribute the biomass to each species, which is then recorded in the corresponding species column above. For example: site 154 has biomass of 0.69004, estimate 1/3 EA (0.24119), 2/3 TH (0.44885). Unit is gdw m-2. - SE: standard error of biomass at sample site calculated from the 3 replicate quadrats used to estimate biomass at a sample site 4. Sample method - VESSEL: Indicating the type of vessel used to collect the sample - HELICOPTER: Helicopter utilised (1) or not (0) at sample site. - CAMERA: Camera utilised (1) or not (0) at sample site. - GRAB: van Veen grab utilised (1) or not (0) at sample site. - WALKING: Walking access to site utilised (1) or not (0). - DIVER: dive access to site utilised (1) or not (0). 5. Percentage of substrate coverage. Note: ALGAE_COVE + BENTHOS_CO + SEAGRASS_C = 100% - ALGAE_COVE: Estimated percentage of algae cover at sample site. eg: Site 155 has 10% algae cover. - BENTHOS_CO: Estimated percentage of benthos cover at sample site. eg: Site 155 has 63% benthos cover - SEAGRASS_C: Estimated percentage of seagrass cover at sample site. eg: Site 155 has 27% seagrass cover. 6. Presence of Dugong feeding trails - DFTS_PRESE: identifies presence (1) or absence (0) of evidence that sample site is part of a Dugong feeding trail. 7. Data custodian and date of update - AUTHOR - UPDATED 8. Meadow Survey. Distribution of Algae cover (ALGAE_COVE) across the following functional groups: - TURF (Turf mat) - ERECT_MACR (Erect macrophyte) - ENCRUSTING (Erect macrophyte) - ERECT_CALC (Erect Calcareous) - FILAMENTOU (Filamentous) Note: TURF + ERECT_MACR + ENCRUSTING + ERECT_CALC + FILAMENTOU = 100 Distribution of Benthic macro-invertebrates cover across the following broad taxonomic groups: - OPEN_SUBST: open substrate, no Benthic macro-invertebrates. - HARD_CORAL - SOFT_CORAL - SPONGE - OTHER_BMI Note: OPEN_SUBST + HARD_CORAL + SORF_CORAL + SPONGE + OTHER_BMI = BENTHOS_CO Meadow Layer 1. Layer Identification - "Id": unique identifier for the seagrass meadow. This value is referenced by the "MEADOW" field of the Site Layer. 2. Spatial survey mapping precision and reliability estimates - "R_m": estimated mapping precision based on mapping method. All meadows of this dataset have been mapped via helicopter and therefore have a corresponding mapping precision of "50-100m". - "R_Ha": meadow reliability estimate (unit: hectares) expressing the error buffer around each meadow as calculated from the mapping precision estimate. 3. Area Cover Seagrass - "Area__ha_": estimated meadow size (unit: hectares). - "Type": meadow community type, determined according to seagrass species composition within the meadow. - "Species": seagrass species found within the meadow. - "Biomass": estimated biomass of the meadow (unit: gdw m-2) along with estimated error. The error is a calculation of standard error of biomass. Mean biomass and SE calculated from all sites within an individual meadow. - "Sites": the number of sample sites within the meadow. References: Mellors, J. E. 1991. An evaluation of a rapid visual technique for estimating seagrass biomass. Aquatic Botany, 42: 67-73 Data Location: This dataset is filed in the eAtlas enduring data repository at: data/TSRA_2018-22/TS_JCU_Orman-Reefs_2017 Change Log: This section will document changes to the dataset as subsequent versions are released. This section will allow you to tell if you have the latest copy of the dataset. - Version 1 (2018-09-05): Initial release of the dataset. The filenames for this release were: TS_JCU_Orman-Reefs_2017_Seagrass_Site-surveys and TS_JCU_Orman-Reefs-2017_Seagrass_Community-type.

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    This dataset contains annual and seasonal values of the benthic light (bPAR index) for water quality on the Great Barrier Reef as calculated for each NRM Region (Cape York, Wet Tropics, Dry Tropics, Fitzroy and Mary-Burnett) and waterbody (Closed Coastal, Open Coastal, Midshelf and Offshore) for each water year between 2003 and 2019, as reported in NESP TWQ Project 5.3. Code used to produce these values is also available through eAtlas, as is the daily bPAR data. 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 annual and seasonal values of the bPAR index developed through NESP TWQ Project 5.3. Methods: Daily benthic photosynthetically active radiation (bPAR) derived from remote sensing ocean colour observations 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). Boundaries of NRM Regions and water bodies as used here are available as part of the “code” dataset also associated with NESP TWQ Project 5.3 in eAtlas. Spatial information (geographic outlines of GBR NRM regions and waterbodies) is provided as a SpatialPolygons variable “Polys” in the R data file Polys.rda The full method used to calculate the index is 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: This dataset consists of a comma separated variable (CSV) file containing the annual and seasonal values of the bPAR index for each water year from 2003 through 2019. Columns contain: A) Water Year. Each water year is defined as the period from 1 October in the preceding calendar year through to 30 September of the year in question, so for example the 2003 water year runs from 1 October 2002 through to 30 September 2003, inclusive. B) NRM Region. Name of NRM management region (Cape York, Wet Tropics, Dry Tropics, Fitzroy or Burnett-Mary). C) Water Body. Name of water body (Closed Coastal, Open Coastal, Midshelf, or Offshore). D) Annual bPAR index. Value of the bPAR index calculated over a full water year for the NRM region and waterbody in question. Values range between 0 (very poor light penetration to benthic habitats) to 1 (excellent light penetration to benthic habitats). E) Wet season bPAR index. Value of the bPAR index calculated over the wet season of the water year in question (i.e. 1 October of the preceding calendar year to 30 April). F) Dry season bPAR index. Value of the bPAR index calculated over the dry season of the water year in question (i.e. 1 May to 30 September). References: 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

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    This dataset reflects the external boundaries of native title applications which have been combined, discontinued, dismissed, struck out or withdrawn. These include claimant, non-claimant and compensation applications. The dataset depicts the geospatial boundaries of applications as at the time the application became inactive. The NTDA Historical dataset provides a spatial representation of application areas as they appeared at the time that the application was ended. Note that in some cases this may be different from the area at the time the application was lodged. The NTDA Transaction dataset shows spatial and aspatial amendments to application records from the time of lodgement to the current representation. The NTDA Historical dataset is a subset of these records. Note: This metadata record is a local copy maintained for the eAtlas and is not authoritative. Please see the link to the 'Original metadata record for this dataset' for the full metadata and latest update. Data Location: This dataset is filed in the eAtlas enduring data repository at: data\non-custodian\ongoing\AU_NNTT\2020-11-05\NTDA_Historical_Nat_shp.zip

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    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

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    This record provides an overview of the NESP Marine and Coastal Hub small-scale study - Project 3.8 – Carbon abatement and biodiversity enhancements through controlling feral ungulate disturbance in wetlands. For specific data outputs from this project, please see child records associated with this metadata. Feral ungulates (e.g., cattle, pigs, buffalo) damage wetlands, reducing biodiversity, water quality and cultural heritage values, but funding for management has been inadequate. Feral ungulates may contribute to greenhouse gas emissions (GHG) through their disturbance of soils and vegetation, but the levels of carbon abatement achieved with their control is not well characterised across Australia. This project will work with Traditional Owners, academics and governments to characterise the benefits of feral ungulate control in wetlands, providing science that will underpin development of an Emission Reduction Fund method, where payments for carbon credits and biodiversity enhancements would fund management of feral ungulates on Country. Planned Outputs • Environmental habitat surveys [tabular dataset] • Greenhouse gas and soil carbon data [tabular data] • ungulate damage assessments [tabular data] • Remote sensing vegetation condition [scripts] • Wetland topology assessments [spatial] • Final technical report with analysed data and a short summary of recommendations for policy makers of key findings [written]

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    The Register of Native Title Claims is kept by the Native Title Registrar pursuant to s 185 of the NTA to hold information on claimant applications that have satisfied the registration requirements set out in ss 190B and 190C of the NTA. Native title claimants whose applications have been accepted for registration are afforded a number of procedural rights in relation to future acts that affect native title including the right to negotiate, the right to be notified, the right to comment, the right to request consultation, or the right to object as prescribed under the NTA. Note: This metadata record is a local copy maintained for the eAtlas and is not authoritative. Please see the link to the 'Original metadata record for this dataset' for the full metadata and latest update. Data Location: This dataset is filed in the eAtlas enduring data repository at: \\pearl\e-atlas\data\non-custodian\ongoing\AU_NNTT\2023-09-29\original\NTDA_Register_Nat.zip

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    P2R.data.reader.v041220 with Header.R This is a block of R code written for NESP Tropical Water Quality Hub Project 3.1.6 The software uses data supplied from the Queensland Government’s Paddock to Reef (P2R) modelling system, and other publicly available data, to generate plots of supply curves for water quality credits in dissolved inorganic nitrogen (DIN) for the main catchments in the Wet Tropics. The aim of Project 3.1.6 was to compare the cost of supplying DIN credits from various sources with the prices that potential credit buyers would be willing to pay for those credits. This R-code estimates the minimum cost of supplying DIN credits via fertiliser practice change in sugarcane production in the main Wet Tropics catchments (Daintree, Mossman, Barron, Mulgrave-Russell, Johnstone, Tully, Murray and Herbert). Key data inputs on reductions in cane yield and reductions in DIN loss from 563 individual cane land management units[1] in the Wet Tropics are obtained from the Queensland Government’s Paddock to Reef (P2R) modelling program. Data on fertiliser cost, sugar prices, sugar content in cane, equipment cost and growers’ ‘transaction cost’ in engaging in water quality credit trading are obtained from public sources and the literature. Results are produced in the form of estimated supply curves for DIN credits from practice change in cane for the main Wet Tropics catchments. [1] The Queensland Government’s Paddock to Reef (P2R) modelling and monitoring program (The Australian and Queensland Governments, 2018) defines unique combinations of soil type, soil permeability, climate zone and sub-catchment in sugarcane land in the Wet Tropics catchments as separate cane ‘management units’. Methods: The Queensland Government’s Paddock to Reef (P2R) modelling and monitoring program (The Australian and Queensland Governments, 2018) defines unique combinations of soil type, soil permeability, climate zone and sub-catchment in sugarcane land in the Wet Tropics catchments as separate cane ‘management units’. P2R’s mapping produces 563 separate cane management units for which full cane yield and DIN loss predictions are available over the time period 1987-2013. In Project 3.1.6, the minimum cost of supplying DIN credits from practice change in cane in the Wet Tropics at management unit resolution is calculated as the sum of the following four components: 1. Opportunity cost: the reduction in average annual farm gross margin that follows from a reduction in fertiliser application rate. Average farm gross margin is derived from average P2R-simulated cane yields over the period 1987-2013 (Fraser et al., 2013). Opportunity cost is calculated on an annual basis. 2. Compensation for increased exposure to risk of reduced yield: the average reduction in farm gross margin from years containing the top 10% of P2R-simulated yields over the period 1987-2013 that follows from a reduction in fertiliser application rate (Fraser et al., 2013). Compensation for increased exposure to risk is calculated on an annual basis. 3. Transition cost: the annualised cost of equipment purchases required to implement each step improvement in fertiliser practice. Transition cost data are drawn from van Grieken et al., (2019; Table 2, p.4), inflated to 2018AUD using the Reserve Bank of Australia’s inflation calculator (https://www.rba.gov.au/calculator/ ).Transition cost is annualised over the 6-year cane cycle at a discount rate of 7% per annum, to produce an annualised equivalent transition cost that is compatible with the annual timeframe over which opportunity cost and the compensation required for increased exposure to risk are calculated. 4. Transaction cost: the annualised cost of the resources, including time, that a landholder has to commit to learn about and engage with water quality credit trading. Transaction cost data are drawn from (Coggan et al., 2014; Table 5, p.512), inflated to 2018AUD using the Reserve Bank of Australia’s inflation calculator (https://www.rba.gov.au/calculator/ ).Transaction cost is annualised over a 6-year cane cycle at a discount rate of 7% per annum to produce an annualised equivalent transition cost that is compatible with the annual timeframe over which opportunity cost and the compensation required for increased exposure to risk are calculated. Each element of cost is calculated for each step change in fertiliser management practice in line with the Sugarcane Water Quality Risk Framework 2017-2022 (The Australian and Queensland Governments, n.d.)(https://www.reefplan.qld.gov.au/__data/assets/pdf_file/0036/78867/sugarcane-water-quality-risk-framework-2017-2022.pdf ) i.e. for successive step improvements in fertiliser management practice on the Sugarcane Water Quality Risk Framework – Practice Level D to Practice Level C, Practice Level C to Practice Level B etc. Estimates of transition cost and transaction cost vary depending on the size of the farm within which a management unit is located. These data were not available to Project 3.1.6, so management units are stochastically allocated to small, medium or large farms across multiple simulation loops. Within each stochastic allocation, the total land area allocated to the different size classes of farms is matched to within 1% of that reported by Sing and Barron (2014). The initial level of fertiliser practice is also allocated stochastically across management units to match the proportion of catchment land area managed under the different levels of fertiliser practice as reported in the 2017 and 2018 Reef Report Cards (Commonwealth of Australia and Queensland Government, 2018). For each step change in fertiliser management practice on each management unit, the cost incurred (the sum of opportunity cost, compensation required for exposure to increased risk, transition cost and transaction cost) and the reduction in DIN loss achieved (at the cane field and at End-of-Catchment) are recorded. (Management unit-specific DIN transport coefficients for DIN lost via the surface water pathway are provided by P2R modelling. A uniform DIN transport coefficient from the literature is used for DIN lost via deep drainage (after Webster et al., (2012), as cited in van Grieken et al., 2019; Sec 3.3, p.4). The supply curve for supply of DIN credits from a catchment is constructed by ordering practice change steps from management units in that catchment from the most cost-effective (i.e. lowest $/kgDIN @ EoC) to the least cost-effective (i.e. highest $/kgDIN @ EoC), and then plotting cumulative DIN reduction (kgDIN reduced @ EoC) vs cost-effectiveness ($/kgDIN @ EoC). See Figure 5.5 to 5.9 in the final report ( https://nesptropical.edu.au/wp-content/uploads/2021/01/NESP-TWQ-Project-3.1.6-Final-Report.pdf ). Format: This data delivery is presented as a block of R-code. The first few lines of the tab-delimited input data files are also included in the data delivery to illustrate the format and content of the input data. Datafiles containing P2R data cannot be included in the eAtlas archive because Project 3.1.6 does not hold the rights to these data. Data Dictionary: Data delivery comprises: R-code to generate DIN credit supply curves, plus input data files R-code file: P2R.data.reader.v041220 with Header.R Input data files: as tab-delimited text files Data from Paddock to Reef (P2R): file header only included P2R_combined_Avg_file_to_read = "P2R_Avg_1987_2013_wide.txt" P2R_DINLoss_file_to_read = "P2R_WetTropics_Av_of_DIN_Loss_trimmed_max_to_min.txt" P2R_Herbert_Avg_file_to_read = "P2R_Averaged_data_in_Herbert_only.txt" P2R_FWCane_file_to_read = "P2R_WetTropics_Av_of_CaneFW_trimmed_max_to_min.txt" CVaR_file_to_read = "P2R_CVaR_by_DMU_Best_Worst_10_percent_1987_2013.txt" Publicly available data, compiled by NESP Project 3.1.6 Urea_prices_to_read = "Urea_Prices_in_2018_19_AUD.txt" Sugar_prices_to_read = "Sugar_Prices_in_2018_19_AUD.txt" Transition_costs_to_read = "Transition_Costs_between_Cane_Mgmt_Practices_v040520.txt" Transaction_costs_to_read = "Transaction_Cost_Data_Coggan_2014.txt" References: Coggan, A., van Grieken, M., Boullier, A., Jardi, X., 2014. Private transaction costs of participation in water quality improvement programs for Australia’s Great Barrier Reef: Extent, causes and policy implications. Aust. J. Agric. Resour. Econ. 59, 499–517. https://doi.org/10.1111/1467-8489.12077 Commonwealth of Australia and Queensland Government, 2018. Agricultural Land Management Practice Adoption Results: Reef Water Quality Report Card 2017 and 2018. Fraser, G., Shaw, M., Silburn, M., 2013. Paddock to Reef 2: Cane Paddock Scale Modelling – Wet Tropics Region. Sing, N., Barron, F., 2014. Management practice synthesis for the Wet Tropics region: A report prepared for the Wet Tropics Water Quality Improvement Plan. Terrain NRM, Innisfail. Terrain NRM, Innisfail. Queensland. The Australian and Queensland Governments, 2018. Paddock to Reef Integrated Monitoring, Modelling and Reporting Program 2017-2022: Summary. The Australian and Queensland Governments, n.d. Sugarcane water quality risk framework 2017-2022. van Grieken, M.E., Roebeling, P.C., Bohnet, I.C., Whitten, S.M., Webster, A.J., Poggio, M., Pannell, D., 2019. Adoption of agricultural management for Great Barrier Reef water quality improvement in heterogeneous farming communities. Agric. Syst. 170, 1–8. https://doi.org/10.1016/j.agsy.2018.12.003 Webster, A.J., Bartley, R., Armour, J.D., Brodie, J.E., Thorburn, P.J., 2012. Reducing dissolved inorganic nitrogen in surface runoff water from sugarcane production systems. Mar. Pollut. Bull. 65, 128–135. https://doi.org/10.1016/J.MARPOLBUL.2012.02.023 Data Location: This dataset is filed in the eAtlas enduring data repository at: data\nesp3\3.1.6_Exploring-WQ-trading\

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    This record provides an overview of the NESP Marine and Coastal Hub study - Project 3.5 – Supporting regional planning in northern Australia: Building knowledge, skills and partnerships for understanding seagrass distribution. For specific data outputs from this project, please see child records associated with this metadata. Northern Australia has vast development opportunities but limited knowledge of the environment to inform decision making. This region has globally significant seagrass habitat, supporting dugong, green turtle, and commercially important fish and prawns. Key to managing impacts to species in these habitats is reliable data on seagrass distribution and how this changes over time. Achieving this requires large-scale mapping and a ranger-led monitoring network in remote communities. This project will map seagrass habitats across northern Australia through targeted mapping expeditions in data deficient regions. It will strengthen relationships with coastal communities, build-on existing knowledges and skills, co-design training resources with rangers to undertake monitoring, trial new technologies for monitoring, and synthesise historical and new seagrass data into an open access resource. Planned Outputs • Spatial GIS datasets [seagrass surveys] • Synthesised historical compilation [spatial dataset] • Final technical report with analysed data and a short summary of recommendations for policy makers of key findings [written]