Bleaching and environmental data for global coral reef sites from 1980-2020

This study demonstrates the important role that long term monitoring programs can play in understanding the natural variability and long term trends in the coral reefs of American Samoa. One benefit of this study is that it provides an overview of the condition of the reefs on all the main volcanic islands simultaneously. It also provides a broad scale perspective for understanding the results of the site dedicated monitoring programs in Fagatele Bay National Marine Sanctuary and Pago Pago Harbour (Aua transect: see below), which provide a much longer term perspective on the reefs of Tutuila (85 and 25 years respectively).

This dataset includes the status report (PDF and MS Word) and raw survey data (MS Excel) made in 1996 and 2002. Survey sites include all islands of American Samoa, although remote atolls of Swains and Rose were not made in 2002. Surveys were also made in the country of Western Samoa.

This dataset consists of underwater benthic imagery and measurements of light attenuation taken from Paluma Shoals in the Coral Sea following a 2016 El Niño coral bleaching event. Data were collected between 09 and 11 August 2016. Benthic imagery was captured using a SeaViewer Sea-Drop™ Camera (950 Analog model) on 10 August 2016. Light attenuation measurements were taken using a LiCOR LI-192SA Light Meter deployed at a range of depths below the sea surface. These cruises formed the field component of NERC Discovery Science project "Quantifying ENSO-related bleaching on nearshore, turbid-zone coral reefs grant story”. The data were collected following a major El Niño event which caused mass coral bleaching across the Great Barrier Reef. The event provided opportunity to undertake a rapid assessment of the impacts of bleaching on the turbid-zone reefs in the vicinity of Paluma Shoals (central Halifax Bay). The aim of the project is to ascertain: 1) The total extent of bleaching-induced mortality; 2) The extent to which specific coral species have been impacted; 3) Any immediate impacts on the structural complexity and diversity of the reefs. The Discovery Science project was composed of Standard Grant NE/P007694/1. The grant was held by the University of Exeter, School of Geography and led by Professor Christopher Perry. The funding period ran from 01 July 2016 to 31 March 2017. All data described have been received by BODC from the RRS James Clark Ross and will be processed and made available online in the future. Raw data are available on request. No further data are expected from this project.

This metadata record describes chlorophyll fluorescence and turbidity time-series data collected through in situ monitoring by the Great Barrier Reef Marine Monitoring Program for Inshore Water Quality (MMP WQ). A full description of the MMP WQ and its associated datasets can be found in the parent metadata record linked above.

Continuous in situ chlorophyll fluorescence and turbidity were measured using WET Labs ECO FLNTUSB Combination Fluorometer and Turbidity Sensors. The MMP WQ currently has instruments deployed at 19 sites summarised by Natural Resource Management (NRM) region below. The date ranges of instrument deployments are also shown; some sites were discontinued in 2014 but data are still available for download.

Cape York NRM:

Annan-Endeavour focus region: Forrester Reef (2020-present), Dawson Reef (2020-present)

Wet Tropics NRM:

Barron-Daintree focus region: Snapper Island North (2007-2014)

Russell-Mulgrave focus region: Fitzroy Island West (2007-present), High Island West (2007-present), Russell-Mulgrave River mooring (2015-present), Frankland West (2007-present)

Tully focus region: Dunk Island North (2007-present), Tully River mooring (2015-present)

Burdekin NRM: Pelorus (2007-present), Pandora (2007-present), Geoffrey Bay (2007-present), Burdekin River mooring (2015-present)

Mackay Whitsunday NRM: Double Cone Island (2007-present), Daydream Island (2007-2014), Pine Island (2007-present), Seaforth Island (2015-present), Repulse Island dive mooring (2015-2021), O'Connell River mooring (2021-present)

Fitzroy NRM (monitored 2005-2014 under MMP WQ, 2020-present under Fitzroy Basin program): Pelican Island (2007-2015), Humpy Island (2007-2015, 2021-present), Barren Island (2007-2015, 2021-present), Fitzroy River mouth (2021-present).

Instruments are deployed for approximately 4 months at a time at 5 m below the water's surface. They collect one sample every 10 minutes, where each data point is calculated as the mean of 50 instantaneous burst readings. Pre- and post-deployment checks of each instrument include measurements of the maximum fluorescence response and the dark count (instrument response with no external fluorescence, essentially the ‘zero’ point). Factory servicing and calibration checks are performed at the WET Labs facility in the USA after 12-18 months of in-water deployment time. After retrieval, the instruments are cleaned and data downloaded and converted from raw instrumental records into measurement units (µg L-1 for chlorophyll fluorescence and NTU for turbidity) according to the standard procedures of the manufacturer. Deployment information and all raw and converted instrumental records are stored in an Oracle-based data management system developed by AIMS. Detailed procedures for data handling can be found the MMP WQ's QA/QC Reports (see link below in Related Information).

Instrument data are validated against concurrently-collected water samples. Water samples for analyses of chlorophyll a and total suspended solids are collected three times per year to calibrate logger fluorescence and turbidity to in situ conditions. Diver-operated Niskin bottles are used to sample close to the moored loggers and samples are preserved and analysed in the same manner as ship-based water samples (see link below in Related Information).

Instruments in the Cape York region are deployed at 3 m below the surface and have different processing and servicing procedures than other regions. These procedures are detailed in the files containing instrument data, linked below.

Instrument data can be downloaded in hourly or daily averaged formats (see links below in Data Downloads).

This data record contains data associated with the publication:Tebbett SB, Streit RP, Morais J, Schlaefer JA, Swan S, Bellwood DR (2022) Benthic cyanobacterial mat formation during severe coral bleaching at Lizard Island: the mediating role of water currents. Marine Environmental Research 181:105752. doi: 10.1016/j.marenvres.2022.105752In this study we examined cyanobacterial mat distribution during a severe coral bleaching event at Lizard Island (Great Barrier Reef, Australia). This study was based on the cover of cyanobacterial mats, and other benthic components, from inside 1 m2 photoquadrats (n = 349). The photoquadrats were spread across 19 transects around Lizard Island and photoqaudrats were fixed (i.e. the same areas were repeatedly sampled/photographed). Photoquadrats were repeatedly sampled at six points in time between February 2016 and January 2021, with particularly focused sampling (three times in 2016) around the severe coral bleaching event that affected Lizard Island in 2016. Photoquadrats were analysed to determine benthic cover of major benthic components, under 40 randomly stratified points in each photoquadrat, using the software photoquad V_1_4. The CSV file in this dataset contains the raw benthic cover data analysed in this paper. The numbers under each benthic component in the dataset relate to the number of points that fell on that component in each quadrat.For full methodological details (including details/data on water currents), please see the published manuscript and associated supporting information (outlined above).

The dataset contains information of Acropora aspera coral fecundity in reef flat and reef slope habitats. All data originates from Heron Island corals in the southern Great Barrier Reef (Capricorn Bunker). In November 2021, a total of 40x colonies of Acropora aspera were checked for presence of eggs, tagged, and sampled on the reef flat (<1m depth) and the reef slope (~5m depth) of Heron Island. For each site (x2 sites, distanced by >100m), the sampling was proceeded in the following way: - 10x colonies were tagged on the reef flat and 10x colonies were tagged on the reef slope - 1x fragment was collected from each tagged colony and preserved in 5% formalin and seawater (total of 40x fragments) The fixed samples were then decalcified in 3-10% hydrochloric acid. For each decalcified sample, 30 polyps were randomly selected and inspected for presence/absence of eggs to estimate percentages of reproductive polyps within a branch. Among the polyps inspected for each sample, a minimum of 10 gravid polyps were dissected under the microscope to identify, count, and measure ovaries, spermaries, and eggs. Scaled pictures of each polyp were taken with a camera for measurements. For size measurements, the width and length of each element are recorded. Measurements were performed with ImageJ.

Access: Metadata is fully public. Data files will be made fully public once the research has been published. For special requests, please directly contact the Data Custodian and CC the Project Leader.

Lineage: NA – the dataset is the raw dataset prior to cleaning. It contains all necessary information to conduct cleaning by the user.

This dataset contains Sentinel 2 and Landsat 8 cloud free composite satellite images of the Coral Sea reef areas and some parts of the Great Barrier Reef. It also contains raw depth contours derived from the satellite imagery. This dataset was developed as the base information for mapping the boundaries of reefs and coral cays in the Coral Sea. It is likely that the satellite imagery is useful for numerous other applications. The full source code is available and can be used to apply these techniques to other locations.

This dataset contains two sets of raw satellite derived bathymetry polygons for 5 m, 10 m and 20 m depths based on both the Landsat 8 and Sentinel 2 imagery. These are intended to be post-processed using clipping and manual clean up to provide an estimate of the top structure of reefs. This dataset also contains select scenes on the Great Barrier Reef and Shark bay in Western Australia that were used to calibrate the depth contours. Areas in the GBR were compared with the GA GBR30 2020 (Beaman, 2017) bathymetry dataset and the imagery in Shark bay was used to tune and verify the Satellite Derived Bathymetry algorithm in the handling of dark substrates such as by seagrass meadows. This dataset also contains a couple of small Sentinel 3 images that were used to check the presence of reefs in the Coral Sea outside the bounds of the Sentinel 2 and Landsat 8 imagery.

The Sentinel 2 and Landsat 8 imagery was prepared using the Google Earth Engine, followed by post processing in Python and GDAL. The processing code is available on GitHub (https://github.com/eatlas/CS_AIMS_Coral-Sea-Features_Img).

This collection contains composite imagery for Sentinel 2 tiles (59 in Coral Sea, 8 in GBR) and Landsat 8 tiles (12 in Coral Sea, 4 in GBR and 1 in WA). For each Sentinel tile there are 3 different colour and contrast enhancement styles intended to highlight different features. These include: - TrueColour - Bands: B2 (blue), B3 (green), B4 (red): True colour imagery. This is useful to identifying shallow features are and in mapping the vegetation on cays. - DeepFalse - Bands: B1 (ultraviolet), B2 (blue), B3 (green): False colour image that shows deep marine features to 50 - 60 m depth. This imagery exploits the clear waters of the Coral Sea to allow the ultraviolet band to provide a much deeper view of coral reefs than is typically achievable with true colour imagery. This imagery has a high level of contrast enhancement applied to the imagery and so it appears more noisy (in particular showing artefact from clouds) than the TrueColour styling. - Shallow - Bands: B5 (red edge), B8 (Near Infrared) , B11 (Short Wave infrared): This false colour imagery focuses on identifying very shallow and dry regions in the imagery. It exploits the property that the longer wavelength bands progressively penetrate the water less. B5 penetrates the water approximately 3 - 5 m, B8 approximately 0.5 m and B11 < 0.1 m. Features less than a couple of metres appear dark blue, dry areas are white. This imagery is intended to help identify coral cay boundaries.

For Landsat 8 imagery only the TrueColour and DeepFalse stylings were rendered.

All Sentinel 2 and Landsat 8 imagery has Satellite Derived Bathymetry (SDB) depth contours. - Depth5m - This corresponds to an estimate of the area above 5 m depth (Mean Sea Level). - Depth10m - This corresponds to an estimate of the area above 10 m depth (Mean Sea Level). - Depth20m - This corresponds to an estimate of the area above 20 m depth (Mean Sea Level).

For most Sentinel and some Landsat tiles there are two versions of the DeepFalse imagery based on different collections (dates). The R1 imagery are composites made up from the best available imagery while the R2 imagery uses the next best set of imagery. This splitting of the imagery is to allow two composites to be created from the pool of available imagery. This allows any mapped features to be checked against two images. Typically the R2 imagery will have more artefacts from clouds. In one Sentinel 2 tile a third image was created to help with mapping the reef platform boundary.

The satellite imagery was processed in tiles (approximately 100 x 100 km for Sentinel 2 and 200 x 200 km for Landsat 8) to keep each final image small enough to manage. These tiles were not merged into a single mosaic as it allowed better individual image contrast enhancement when mapping deep features. The dataset only covers the portion of the Coral Sea where there are shallow coral reefs and where their might have been potential new reef platforms indicated by existing bathymetry datasets and the AHO Marine Charts. The extent of the imagery was limited by those available through the Google Earth Engine.

Methods:

The Sentinel 2 imagery was created using the Google Earth Engine. The core algorithm was: 1. For each Sentinel 2 tile, images from 2015 – 2021 were reviewed manually after first filtering to remove cloudy scenes. The allowable cloud cover was adjusted so that at least the 50 least cloud free images were reviewed. The typical cloud cover threshold was 1%. Where very few images were available the cloud cover filter threshold was raised to 100% and all images were reviewed. The Google Earth Engine image IDs of the best images were recorded, along with notes to help sort the images based on those with the clearest water, lowest waves, lowest cloud, and lowest sun glint. Images where there were no or few clouds over the known coral reefs were preferred. No consideration of tides was used in the image selection process. The collection of usable images were grouped into two sets that would be combined together into composite images. The best were added to the R1 composite, and the next best images into the R2 composite. Consideration was made as to whether each image would improve the resultant composite or make it worse. Adding clear images to the collection reduces the visual noise in the image allowing deeper features to be observed. Adding images with clouds introduces small artefacts to the images, which are magnified due to the high contrast stretching applied to the imagery. Where there were few images all available imagery was typically used. 2. Sunglint was removed from the imagery using estimates of the sunglint using two of the infrared bands (described in detail in the section on Sun glint removal and atmospheric correction). 3. A composite image was created from the best images by taking the statistical median of the stack of images selected in the previous stage, after masking out clouds and their shadows (described in detail later). 4. The brightness of the composite image was normalised so that all tiles would have a similar average brightness for deep water areas. This correction was applied to allow more consistent contrast enhancement. Note: this brightness adjustment was applied as a single offset across all pixels in the tile and so this does not correct for finer spatial brightness variations. 5. The contrast of the images was enhanced to create a series of products for different uses. The TrueColour colour image retained the full range of tones visible, so that bright sand cays still retain detail. The DeepFalse style was optimised to see features at depth and the Shallow style provides access to far red and infrared bands for assessing shallow features, such as cays and island. 6. The various contrast enhanced composite images were exported from Google Earth Engine and optimised using Python and GDAL. This optimisation added internal tiling and overviews to the imagery. The depth polygons from each tile were merged into shapefiles covering the whole for each depth.

Cloud Masking

Prior to combining the best images each image was processed to mask out clouds and their shadows.

The cloud masking uses the COPERNICUS/S2_CLOUD_PROBABILITY dataset developed by SentinelHub (Google, n.d.; Zupanc, 2017). The mask includes the cloud areas, plus a mask to remove cloud shadows. The cloud shadows were estimated by projecting the cloud mask in the direction opposite the angle to the sun. The shadow distance was estimated in two parts.

A low cloud mask was created based on the assumption that small clouds have a small shadow distance. These were detected using a 40% cloud probability threshold. These were projected over 400 m, followed by a 150 m buffer to expand the final mask.

A high cloud mask was created to cover longer shadows created by taller, larger clouds. These clouds were detected based on an 80% cloud probability threshold, followed by an erosion and dilation of 300 m to remove small clouds. These were then projected over a 1.5 km distance followed by a 300 m buffer.

The buffering was applied as the cloud masking would often miss significant portions of the edges of clouds and their shadows. The buffering allowed a higher percentage of the cloud to be excluded, whilst retaining as much of the original imagery as possible.

The parameters for the cloud masking (probability threshold, projection distance and buffer radius) were determined through trial and error on a small number of scenes. The algorithm used is significantly better than the default Sentinel 2 cloud masking and slightly better than the COPERNICUS/S2_CLOUD_PROBABILITY cloud mask because it masks out shadows, however there is potentially significant improvements that could be made to the method in the future.

Erosion, dilation and buffer operations were performed at a lower image resolution than the native satellite image resolution to improve the computational speed. The resolution of these operations were adjusted so that they were performed with approximately a 4 pixel resolution during these operations. This made the cloud mask significantly more spatially coarse than the 10 m Sentinel imagery. This resolution was chosen as a trade-off between the coarseness of the mask verse the processing time for these operations.

This data was sourced from the Australian Institute of Marine Science, for more setail see http://www.aims.gov.au/ Note : Raw data has been adjusted to allow for the fact that original data was …Show full descriptionThis data was sourced from the Australian Institute of Marine Science, for more setail see http://www.aims.gov.au/ Note : Raw data has been adjusted to allow for the fact that original data was based on financial years and represented 1990.0 as 1 July 1989 (for example) - it now represents 1 Jan 1990 Data used to produce Figure MAR25 of SoE2016. See; https://soe.environment.gov.au/theme/marine-environment/topic/2016/state-and-trends-marine-biodiversity-quality-habitats-and#marine-environment-figure-25

The dataset contains information of juvenile coral demographics from multiple regions (n = 4) situated throughout the Torres Straits (Far North) and the Great Barrier Reef (North, Central, South). Each region has multiple reefs (n = 2-5), with multiple sites (n = 1-6) nested within each reef, and multiple quadrats within each site (n = 3-9). At the beginning of the study, permanent quadrats (50 x 50 cm) were placed around =>1 juvenile coral (<40 mm maximum diameter), standardised to a 5 m depth profile. All juvenile corals within the quadrat had their location mapped, size measured (maximum diameter), identified to the lowest taxon possible (typically genus), and orientation categorised (exposed, vertical, cryptic). An image of the entire quadrat was taken, plus four detailed images of each quadrat quadrant. Processing of the in-situ maps and images included mapping the xy co-ordinates of every mapped individual colony, and calculating the % cover of the benthic substrate. Every ~12 months, the entire process is repeated. Quadrats are revisited, the juvenile corals re-measured, marked as dead, and new colonies added, and associated images taken. Datasets allow for the quantification of juvenile coral demographic rates (growth, survival, and recruitment), changes in % cover within the quadrats, with all data geolocated to the 16 xy coordinates within each quadrat.

Access Metadata is fully public. Data files will be uploaded and made fully public once the research has been published. For special requests, please directly contact the Data Custodian and CC the Project Leader.

Lineage: NA – the dataset is the raw dataset prior to cleaning. It contains all necessary information to conduct cleaning by the user.

The dataset consists of multiple data files from four types of instruments to visualize current, temperature flow patterns and coral stress responses around the north west section of Moore Reef including a reef pass, taken during 2018-2021. These include 19 temperature and 12 current loggers and 69 CTD casts, along with fluorometry measurements of 33 tagged coral colonies. This dataset also includes photos of the tagged corals along with CoralWatch colour cards during a 5-week period from Nov-Dec 2020.

The three-year Reef Havens Research Project commenced in December 2017 in response to the unprecedented back-to-back coral bleaching events on the Great Barrier Reef in 2016 and 2017. The proposal was based on anecdotal observations during those bleaching events that local environmental conditions were a significant factor affecting bleaching severity and recovery after bleaching: reefs that were near upwellings, or experienced cooler currents or wind mixing, tended to experience less severe bleaching, and those with few other pressures tended to recover better. Could science-based localised interventions that mimic these natural stress-reducing phenomena (for example by incrementally increasing water movement, reducing water temperature by 1-2°C, or disrupting the water’s surface) reduce coral stress, bleaching severity and/or promote recovery on key reef sites, potentially providing time and space for natural acclimatization and adaptation processes to occur?

The Reef Havens Research Project is a foundational investment in an in?situ research platform that was intended to closely observe the impacts of changes in flow and temperature on coral stress, bleaching and recovery outcomes at the very small scale of a tourism site. Once small-scale water movements associated with bleaching events were understood, the project also tested whether an engineering intervention could restore “normal” water movement and mixing during doldrum coral bleaching conditions at a very small area of Moore Reef, one of the key tourism industry sites on the GBR near Cairns. It is hoped that the data generated through this research project will be used by others to build further understanding of the connection between environmental conditions and coral stress, and the potential for interventions to improve bleaching outcomes at small scales.

Methods:

To quantify current and temperature flow patterns and their impacts through the Moore Reef Pass and adjacent tourism site areas, an array of instruments was deployed. The instruments used were a Marotte HS current meter, HOBO TidbiT MX Temp 400 logger, a Walz DIVING-PAM-II underwater fluorometer and a SonTek Castaway conductivity, temperature and depth recorder (CTD).

The Marotte HS current meter is a drag-tilt current meter, developed and manufactured by the Marine Geophysics laboratory at James Cook University. The instrument consists of a buoyant enclosure containing an electronic logger, which is then fixed to a stationary point. The buoyancy force causes the instrument to float directly upwards in the absence of current. Movement of the water exerts a drag force on the instrument, tilting it over until the buoyancy, drag and tether forces are balanced. The amount of tilt is proportional to the speed of the water. The logger records the tilt angle using an accelerometer, and the tilt direction using a magnetometer. Both tilt and tilt angle were converted to current speed and direction in post-processing using a pre-defined tilt-to-speed calibration curve. The instrument also contained a temperature sensor (thermistor type with ±0.2C repeatability) that was fixed to the bottom of the logger (JCU and Marine-Geophysics-Laboratory, 2017).

An array of 12 current meters were fixed to star pickets 0.5 m above the substrate (see Marotte deployment picture) at selected locations, substrate types and depths through the reef pass and tourism pontoon areas for 5 deployments between 2018 and 2021 (see downloadable documents for details of deployments). The loggers were set to record current speed and direction (in m/s and degrees) at one second intervals, along with temperature in Celsius at one-minute intervals. The data were recorded on a micro SD card and the instrument was powered by 2 AA batteries. The raw data were converted into current speed and direction and temperature to one minute intervals with the Marotte HS config program (JCU and Marine-Geophysics-Laboratory, 2017). Along with each csv file per instrument per deployment there is a metadata text file containing the calibration and processing information. All 12 current meters were removed and data downloaded on the 13-Sept-2018.

Nineteen HOBO TidbiT MX Temp 400 loggers were installed at various depths at the northern section of Moore Reef (see Moore Reef Hobo Map) for 4 deployments between 2018 and 2021 (see downloadable document for details of deployments). Some loggers were fixed to star pickets at ~ 50cm above the substrate and others were fixed to mooring lines. The loggers recorded temperature in Celsius at five-minute intervals and at this ratio had nominal battery life of 340 days. The data were downloaded using HOBO’s mobile phone app and are provided as CSV files.

To investigate changes in coral stress associated with water movement, 33 corals from 11 species located along pre-existing monitoring sites were selected and tagged with labelled cattle tags (see downloadable document for details). These individual colonies were underwater photographed with a CoralWatch card in which a finger was visible pointing to the colour on the card that matched the colour of the colony. Sampling was conducted once a week over 5 weeks Nov-19 to Dec-20. The intent was to compare coral stress levels in early summer, during well-mixed conditions, with late-summer, stratified, potentially bleaching conditions. Baseline early-summer stress measurements of these colonies were made with the Walz DIVING-PAM-II underwater fluorometer in 2019 but unfortunately we were prevented from accessing the tagged corals during late summer (March 2020), even though some bleaching was reported, when coronavirus restrictions shut down the GBR tourism industry. Instead we repeated the late-summer fluorometer measurements in April 2021, even though conditions were not stratified. Data were downloaded using Walz’s fluorometry software.

The CastAway CTD enabled near-instantaneous vertical profiles of temperature and salinity to be obtained at and around our field site at Moore Reef.

The CastAway CTD was deployed using a modified fishing rod and 200 lb breaking strain braid Dyneema fishing line either directly from the back of the Reef Magic Marine World Pontoon or via a small tender. The instrument was allowed to sink to the bottom and then rapidly retrieved, making measurements both on the way down and on the way up. The CastAway recorded the GPS location before and after each cast. Plots of conductivity, temperature, salinity and sound speed versus depth could be viewed immediately on the CastAway's integrated color LCD screen in the field. A total of 69 casts were performed, with each cast collecting ~100 data points. Raw data were downloaded via Bluetooth to a Windows computer for detailed analysis and/or export.

Limitations of the data:

The first and last 24 hours of the Marotte and HOBO logger datasets should be treated with caution as this may include transportation time to and from deployment site. Current Meter 12 stopped recording on the 21-Jun-2018.

The CastAway CTD was deployed on an event-based basis, enabling comparison of patterns during normal well-mixed conditions and also during calm, sunny doldrum conditions when normal mixing had failed. Two different instruments were used during the 2019-2020 sampling period, with differing calibration history. While the conductivity measurements should therefore be treated with caution, the temperature data are considered reliable.

Format:

The data set contains multiple parts which are the Marotte current meters, Hobo Temperature meters, CTD casts and fluorometry/coral watch data.

The Marotte current meter dataset consists of individual csv files for 12 instruments for 5 deployments (see downloadable documents for details of deployments).

The HOBO temperature meters datasets consist of individual csv files for an array of HOBO loggers for 4 deployments (see downloadable documents for deployment details).

The CTD datasets consist of CSV files for deployments on each of three dates. Please note that these deployments were deliberately performed on particular dates to measure extreme conditions of stratification, and their deviation from normality, rather than an attempt to rigorously document conditions over time.

The coral watch data consist of two photos for each colony, the first with a close up of the labelled cattle tag and the second showing the coral watch card colour reference matched to coral colony colour. There are 5 folders labelled according to sampling date. This also includes a CSV file with the PAM data.

Data Dictionary:

• This dataset consists of data files compiled from 12 Marotte HS current meters over 5 deployments at Moore Reef Pass (see Moore Reef Marotte Map) between May-18 and Aug-20. The spreadsheet for each Marotte contains the processed data of: Timestamp (one minute intervals), Current Speed (m/s), Direction (degrees), Speed Upper (m/s), Speed lower (m/s), Tilt (radians), Direction (radians), Battery (Volts) and Temperature (Celsius).

• This dataset also consist of HOBO temperature logger data, that recorded water temperature (Celsius) and Timestamp (5 minute intervals) for 4 deployments between Jun-20 and Aug-20.

• The dataset contains 5 folders under coral watch colonies, with each folder containing jpeg photos of tagged coral colonies.

• PAM data: Type = F (fluorometer) or SPEC (Spectrophotometer) No. = measurement number by this

This dataset describes the predicted probability of seagrass presence across the Great Barrier Reef World Heritage Area and adjacent estuaries, based on six Random Forest models. The models have been mosaicked together into one raster dataset with 30m resolution.

Managing seagrass resources in the GBRWHA requires adequate information on the spatial extent of seagrass habitat. The enormous size of the GBRWHA (1000s of kilometres) and the remoteness of many seagrass meadows from human populations means that models are a useful tool to predict the probability of seagrass for areas where data is lacking.

James Cook University’s Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) has been collecting spatial data on GBR seagrass since the early 1980s. This project used TropWATER’s synthesis of seagrass site data (NESP Project 3.1 and 5.4: https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88) to predict potential seagrass habitat.

In making this data publically available for management, the authors from the TropWATER Seagrass Group request being contacted and involved in decision making processes that incorporate this data, to ensure its limitations are fully understood.

Methods:

Seagrass data The sampling methods used to study, describe and monitors seagrass meadows were developed by the TropWATER Seagrass Group and tailored to the location and habitat surveyed; descriptions and references are available in the metadata for the GBRWHA data composite ( https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88 ).

Environmental data Environmental predictors used in the models were: depth below mean sea level (Beaman 2017), relative tidal exposure (Bishop-Taylor et al. 2019), water type (Marine Water Bodies definitions version 2_4, Data courtesy of the Great Barrier Reef Marine Park Authority; Dyall et al. 2004), proportion mud in the sediment (coast and reef models, https://research.csiro.au/ereefs/models/model-outputs/access-to-raw-model-output/ ) (see also Baird et al. 2020; Margvelashvili et al. 2018), dominant sediment (estuary models only; https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88 ), benthic geomorphology (Heap and Harris 2008), benthic light https://dapds00.nci.org.au/thredds/catalog/fx3/gbr1_bgc_924/catalog.html (see also Baird et al. 2016; Baird et al. 2020), water temperature, mean current speed and salinity https://thredds.ereefs.aims.gov.au/thredds/catalog/ereefs/gbr1_2.0/all-one/catalog.html (Steven et al. 2019), wind speed ( https://thredds.ereefs.aims.gov.au/thredds/catalog/ereefs/gbr1_2.0/all-one/catalog.html ) and Australian Bureau of Meteorology’s ACCESS data products (Bureau of Meteorology 2020; Soldatenko et al. 2018; Steven et al. 2019), and latitude. Different models had different combinations of predictors after removing collinear variables and excluding variables that did not extend into an area. For example, estuary models only include depth, relative tidal exposure, dominant sediment, and latitude.

Models We modelled seagrass probability in six areas: Estuary Intertidal, Estuary Subtidal, Coast Intertidal, Coast Subtidal, Reef Intertidal and Reef Subtidal. For each area we used the machine learning technique random forest to broadly examine whether there were habitats within the GBRWHA and adjacent estuaries where seagrass was never likely to grow, using the binary classification within the site data of seagrass present (1) or absent (0) irrespective of species. Random Forest models were implemented using the randomForest package (Liaw and Wiener 2002) in R version 4.0.2 (R Core Team 2020). We used ArcGIS 10.8 to mosaic the six rasters and create a single seagrass probability raster for the GBRWHA.

Spatial limits Seagrass data north and south of the GBRWHA were not included in the analysis. The model extends across the continental shelf but excludes waters deeper than ~100m east of the shelf that were not surveyed for seagrass. Data were included when sites extended west of the GBRWHA boundary into coastal and estuarine water immediately adjacent.

Data sets The site data used in this model is available here: https://eatlas.org.au/data/uuid/5011393e-0db7-46ce-a8ee-f331fcf83a88

Further information can be found in the upcoming publications of the final report for the NESP TWQ Project 5.4.

Limitation of the data:

The site data used in these models extends back to the mid-1980s. Large parts of the coast have not been mapped for seagrass presence since that time. The seagrass probability raster is at 30m grid resolution, however some environmental variables such as those from eReefs (wind speed, current speed, benthic light, water temperature) are from spatial data at 1km grid resolution, and are likely to vary at much smaller spatial scales that we could not include in these models.

Format:

This dataset consists of a raster dataset with a geographic coordinate system of WGS84. The raster has been saved as a layer package with symbology representing seagrass probability in 0.2 increments with a range of 0-1 (GBRWHA_seagrass_probability.lpk)

References: TBC

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.4_Seagrass-Burdekin-region

Additional licensing information: TropWATER gives no warranty in relation to the data (including accuracy, reliability, completeness, currency or suitability) and accepts no liability (including without limitation, liability in negligence) for any loss, damage or costs (including consequential damage) relating to any use of the data. TropWATER reserves the right to update, modify or correct the data at any time. The limitations of some older data included need to be understood and recognised. The TropWATER Seagrass Group would appreciate the opportunity to review documents providing research, management, legislative or compliance advice based on this data.

Data collected during a side-by-side experiment at seven mid-shelf reefs off Townsville. Crown-of-thorns starfish and coral (where relevant) measures were collected using manta tow, cull diver surveys, environmental DNA (eDNA) surveys, scooter assisted large area diver surveys (SALAD), ReefScan-Deep and ReefScan-Transom. Lineage: The data is the raw data collected in the field. The observations from each method can be linked by Reef_ID and Site.

Tropical south-western Pacific temperatures are of vital importance to the Great Barrier Reef (GBR), but the role of sea surface temperatures (SSTs) in the growth of the GBR since the Last Glacial Maximum remains largely unknown. Here we present records of Sr/Ca and d18O for Last Glacial Maximum and deglacial corals that show a considerably steeper meridional SST gradient than the present day in the central GBR. We find a 1-2 °C larger temperature decrease between 17° and 20°S about 20,000 to 13,000 years ago. The result is best explained by the northward expansion of cooler subtropical waters due to a weakening of the South Pacific gyre and East Australian Current. Our findings indicate that the GBR experienced substantial meridional temperature change during the last deglaciation, and serve to explain anomalous deglacial drying of northeastern Australia. Overall, the GBR developed through significant SST change and may be more resilient than previously thought.

This dataset consists of summaries of benthic, fish and manta tow surveys conducted in the Capricorn-Bunker Region of the Great Barrier Reef in October 2015. It consists of three tables: A table …Show full descriptionThis dataset consists of summaries of benthic, fish and manta tow surveys conducted in the Capricorn-Bunker Region of the Great Barrier Reef in October 2015. It consists of three tables: A table summarising percent cover of benthic organisms grouped into broad categories from photo transects collected during the monitoring surveys. A table of average abundance for each reef fish species on the 5 transects in each of the three sites on each reef is presented for the eight reefs surveyed. A table of the average reef wide coral cover, the mean reef-wide density of crown-of-thorns starfish (CoTS), Acanthaster planci and the mean reef-wide density of coral trout for each reef based on manta tow surveys. This dataset is an extract from part of the AIMS Long Term Monitoring database. The rezoning of the Great Barrier Reef Marine Park (GBRMP) in 2004 increased the number and extent of ¿no-take¿ areas within the Park. This project surveys pairs of reefs, one in a ¿no-take¿ or green zone and the other a similar reef where fishing is allowed (blue zone), in five regions of the GBRMP. Green and blue zones will be surveyed for the abundance and size of fishery species, particularly coral trout, as well as wider effects on coral reef communities. Surveys of coral communities are undertaken in the same location as surveys of fishes. Biennial surveys commenced in 2006. Methods: Benthic surveys: Coral communities were surveyed on eight reefs (Boult Reef, Broomfield Reef, Erskine Reef, Fairfax Islands Reef, Hoskyn Islands Reef, Lady Musgrave Reef, Mast Head Reef, North Reef (North)) in the Capricorn-Bunkers region using digital still images taken along five permanent 50 m transects in each of three sites per reef (n = 15 transects). Forty images were selected randomly from the fifty taken per transect for analyses. The benthic organisms under five points per image arranged in a quincunx pattern were identified (n = 200 points per transect). Benthic organisms were identified to the highest taxonomic resolution possible and each organism is assigned a lifeform based on its shape. Data were converted to percent cover and the site averages is presented for each reef surveyed. AIMS LTMP Standard Operating Procedure 10. The data summaries the latest survey from a time-series of biennial surveys that commenced in 2006. Temporal trends at each reefs are available on http://data.aims.gov.au/reefpage2/allreefs.jsp Reef fish surveys: The abundance of ten families of conspicuous, diurnally active reef fishes was enumerated using standard underwater visual census techniques. Sites are located in a standard reef slope habitat on the north-eastern flank of each reef between 6 to 12m, and consist of five permanently marked 50 m transect (n=15 per reef). Damselfishes (Pomacentridae) were surveyed on one metre wide belt transects while the remaining nine families of large mobile fishes (Acanthuridae, Chaetodontidae, Labridae, Lethrinidae, Lutjanidae, Scaridae, Siganidae, Serranidae and Zanclidae) were counted on five metre wide belts. See AIMS LTMP Standard Operating Procedure 3. Manta tow surveys: Broadscale surveys of reef-wide coral cover, densities of crown-of-thorns starfish and of coral trout using manta tows involve a snorkel diver being towed around the perimeter of the reef. The boat stops every 2 min to allow data to be recorded. See AIMS LTMP Standard Operating Procedure 9. Format: CapBunk_NESP-TWQ-3-7_AIMS_Benthic_2015-10.csv, CapBunk_NESP-TWQ-3-7_AIMS_Benthic_2015-10.shp: Average percent cover of groups of benthic organisms on each of 3 sites on survey reefs in the Capricorn-Bunkers, GBR Description of lifeforms are published in the Standard Operation Procedures, Appendix III See: AIMS LTMP Standard Operating Procedure 10 The following are the classifications in the dataset: Turf algae Tabulate Acropora Coralline algae Branching Acropora Encrusting non-Acropora Sand Massive non-Acropora Macroalgae Submassive non-Acropora Soft coral Digitate Acropora Arb & Enc Soft Coral Arborescent Soft Coral Capitate Soft Coral Foliose non-Acropora Encrusting Acropora Millepora Branching non-Acropora Submassive Acropora Sponge Other organisms Encrusting Soft Coral Zoanthid Bottlebrush Acropora Dead coral (recent) Rubble Lobate Soft Coral Massive Soft Coral Mushroom coral Abiotic Solitary coral Unknown The shapefile was created from the CSV. This process involved truncating the attribute names to comply with 10 character limit of the shapefile format. The CapBunk_NESP-TWQ-3-7_AIMS_Benthic_2015-10.aliases.csv file contains a lookup table that maps the shortened attribute names in the shapefile to the original longer names in CSV file. CapBunk_NESP-TWQ-3-7_AIMS_Fish_2015-10.csv: Consists of a summary of the raw counts of reef fishes. The average abundance for each reef fish species per 1,000 m2 is presented for each of the 3 sites on the eight reefs surveyed in October 2015. Note: this table also includes rows for species that were searched for, but none were observed. These correspond to rows where the abundance is 0. CapBunk_NESP-TWQ-3-7_AIMS_Manta-tow_2015-10.csv: Summary of the manta tow results for the surveyed reefs, including the mean hard coral cover (average over all tows on reef), CoTS (count per tow) and trout (count per tow). Data Location: This dataset is saved in the eAtlas enduring data repository at: data\NESP1\3.7_GBR_Zoning\

R code. https://github.com/makosnik/whoForams. dataImport.R reads in the raw data files and creates the combined dataset used for these analyses (including FSIcategories-Genus.csv, this file is called from analyses.R). plotPreferences.R contains specific plotting functions and sets graphical parameters such as the colours used in the plots (this file is called from analyses.R). analyses.R performs the bulk of the analyses for the paper. textStats.R contains the code of the remainder of the analyses (primarily numerical results mentioned in the text; this file assumes that analyses.R has already run). plot-Sed&Lead.R contains the code used to create Figure 2.

Ocean Provinces: Brazilian (B), Caribbean (C), Central Pacific (CP), Great Barrier Reef (GBR), Central Indian Ocean (CIO), Eastern Pacific (EP), Middle East (ME), Polynesia (P), South East Asia (SEA), Western Australia (WA), Western Indian Ocean (WIO).

This airborne hyperspectral (19 bands) image data of Heron Reef, Great Barrier Reef, Australia is derived from Compact Airborne Spectrographic Imager (CASI) data acquired on 1st and 3rd of July 2002, latitude -23.45, longitude 151.92. Processing and correction to at-surface data was completed by Karen Joyce (Joyce, 2004). Raw imagery consisted several images corresponding to the number of flight paths taken to cover the entire Heron Reef. Spatial resolution is one meter. Radiometric corrections converted the at-sensor digital number values to at surface spectral radiance values using sensor specific calibration coefficients and CSIRO's c-WomBat-c atmospheric correction software. Geometric corrections were done using field collected coordinates of features identified in the image. Projection used was Universal Transverse Mercator Zone 56 South and Datum used was WGS 84. Image data is in TIFF format.

This data set in excel sheet format presents results of the mail survey of 1565 residents of the GBRWHA. The dataset is accompanied by a set of 58 maps that illustrate key findings.

Project 10.2 explored how visitors and residents feel towards and perceive Great Barrier Reef World Heritage Area, as well as their willingness to pay to protect the reef and their satisfaction with current and future developments in and around the GBRWHA.

Data were collected from the residents of the GBRWHA using a mail-out survey to a geographically stratified random sample of resident households in postcodes that lay partially or entirely within the study area. The pilot stage included 230 randomly selected households (2 from each of the postcodes identified), while the main mailing included about 40 households in each postcode. Following the Dilman (2007) methodology, we sent a reminder letter with replacement questionnaire to those who had not responded four weeks later, with a third reminder after that. We estimate that just under 4,000 questionnaires reached their intended recipients, and we received 902 completed questionnaires.

We were cognizant that some demographic groups are more likely to respond to mail-out surveys than others in these regions (e.g. young males, Indigenous people). Therefore we conducted supplementary face-to-face data-collection using the same questionnaire, across various public locations such as ferry terminals, airports and beaches. These extra activities generated an additional 663 responses, bringing the total number of completed resident questionnaires to 1565.

Data Format:

Excel data sheet with each row representing a postcode within the Great Barrier Reef Catchment Area and each column providing summary information about one variable (e.g. % or respondents who have never been to the GBRWHA). The GIS maps represent the data visually (one variable per map, showing responses for each postcode).

The original raw data cannot be published for privacy reasons. Data available here is a public form of the data, aggregated by postcode.

Further details of the project, including data collection and analysis methods, can be found in:

Stoeckl, N., Farr, M. and Sakata H. (2013) What do residents and tourists ¿value¿ most in the GBRWHA? Project 10.2 interim report on residential and tourist data collection activities including descriptive data summaries. Report to the National Environmental research program. Reef and Rainforest Research Centre Limited, Cairn (pp112)

This metadata record describes vertical profile data from Conductivity Temperature and Depth (CTD) profilers collected through in situ monitoring by the Great Barrier Reef Marine Monitoring Program for Inshore Water Quality (MMP WQ). A full description of the MMP WQ and its associated datasets can be found in the parent metadata record linked above.

Sea-Bird Electronics CTD profilers are used for vertical casts and are typically fitted with additional sensors to measure parameters such as fluorescence (a proxy for chlorophyll a concentration), turbidity, beam attenuation, dissolved oxygen concentration, and photosynthetically active radiation (downwelling light) sensors. Instrument models SBE 19plus and SBE 19plusV2 are used currently, while older records include data from SBE 25, SBE 25plus, and SBE 9plus instrument models. Annual calibrations of profilers are carried out in Sea-Bird Electronics laboratories in the USA. These calibration values are included within the instrument configuration file. Pre-trip CTD checks are carried out before each field trip, which include checking the physical status of the sensors and cables and battery voltage.

Prior to conducting a cast, the CTD is secured to the hydrographic wire, tubing is removed to allow flush water to drain from the conductivity-temperature cell, and any protective caps are removed from the other sensors. The CTD is lowered into the water sitting ~3 m below the surface, and a three minute "soak" allows sensors to equilibrate and air bubbles to be flushed by the pump. The CTD is then raised to ~0.2 below the surface and the profile is commenced at a rate of 0.5 – 1 m s-1. The CTD is sent to near-bottom, ensuring it does not touch the seafloor, and retrieved to the surface. Casts are done on the sunny side of the boat to avoid the boat's shadow interfering with the measured light profiles. Data processing is conducted using Sea-Bird proprietary software and includes: conversion of raw instrumental records to measurement units, alignment, removal of ship roll, outlier removal, and bin averaging the down-cast at 1 m increments. Detailed procedures for data handling can be found in the MMP WQ's QA/QC Reports (see link below under Related Information).

CTD data can be downloaded from the Australian Ocean Data Network THREDDS Data Server in netCDF or csv format (see link below under Data Downloads). Water chemistry measurements taken at the time of each CTD cast can be retrieved from the MMP WQ physico-chemical and nutrient database (see parent metadata record above). Water quality data are collected in conjunction with the Great Barrier Reef Marine Monitoring Program for Inshore Coral Reefs (see link below under Related Information).

AIMS' full database of CTD profiles from all around northern Australia can be found attached to a separate metadata record (see link below under Related Information).

The dataset contains information of coral fertilisation success resulting from three manipulative field experiments conducted on the Great Barrier Reef (One Tree Island) and Nikko Bay (Palau). A patch of fecund adult colonies (One Tree Island: Acropora cf tenuis and Acropora digitifera; Nikko Bay: Acropora hyacinthus) were configured at each location in 3-4m depth. During spawning, eggs from a single colony were placed in 250 µm mesh containers and floated within the slick. The slicks were tracked for >60 minutes by vessel and the containers collected. Water samples for in situ sperm concentrations were also collected during the tracking period. Samples were fixed and enumerated later in the laboratory. Each collection point was georeferenced. A similar experiment was conducted on Heron Island Reef slope but using the natural configuration of fecund corals of the species Platygyra daedalea. Eggs were collected by divers and placed in mesh containers (150 µm) in a similar manner to that described above. The slick was tracked for >80 minutes by vessel and containers collected. Each collection point was georeferenced. Location details: One Tree Island, GBR Max latitude\t-23.5028 Min latitude\t-23.498 Max longitude\t152.091 Min longitude\t152.093 Coordinate reference system\tWGS84

Nikko Bay, Palau Max latitude\t 7.313333 Min latitude\t 7.316385° Max longitude\t134.497323 Min longitude\t134.493566° Coordinate reference system\tWGS84

Heron Island, GBR Max latitude\t-23.453906° Min latitude\t-23.454959° Max longitude\t151.922457° Min longitude\t151.923831° Coordinate reference system\tWGS84

Access: Metadata is fully public. Data files will be made fully public once the research has been published. For special requests, please directly contact the Data Custodian and CC the Project Leader.

Lineage: NA – the dataset is the raw dataset prior to cleaning. It contains all necessary information to conduct cleaning by the user.