AndreaBozzo/ceres-open-data-index huggingface dataset:
Destabilization of mutualistic interactions shapes the early heat stress response of the coral holobiont

In a 2-month manipulative experiment, the coral Porites lutea was exposed to gradually increasing temperatures corresponding to 0&ndash;8 degree heating weeks (DHW) and the response of the coral holobiont was assessed using coral and Symbiodiniaceae transcriptomics, microbial 16S rRNA gene sequencing and physiological measurements. 

For full details see Marangon et al (2025).

Organization: Australian Ocean Data Network
Final Report: Joint investigation into statistical methodologies underpinning the derivation of toxicant guideline values in Australia and New Zealand

This report summarises the outcomes of a one-year joint collaborative research project involving  Australian  and  Canadian  researchers  who  undertook  extensive  investigations  into methodologies and tools associated with SSD modelling, focusing on Burrlioz and ssdtools.

Research undertaken included exploring and resolving issues with numerical instability and developing alternative solutions for estimating hazard concentrations (HCx) and/or associated 95% confidence intervals  of  the  Burr  III  and  inverse  Pareto  distributions;  comparisons  between  Burrlioz  and ssdtools  using  collated benchmark  and synthetic datasets; the  development  and evaluation  of mixture distributions as a potential candidate for accommodating bimodal data; and an assessment of bias and coverage across a range of candidate distribution sets used in model averaging. Based on the findings, the project recommends that the Australian-New Zealand and Canadian jurisdictions adopt the R-package ssdtools using an expanded default set of distributions.

The report was prepared for the Department of Agriculture, Water and the Environment.

Organization: Australian Ocean Data Network
Changing dynamics of Great Barrier Reef hard coral cover in the Anthropocene

The Australian Institute of Marine Science&rsquo;s Long-Term Monitoring Program (LTMP) were analysedfor changing disturbance-recovery cycles over the past 4 decades of monitoring to assess the status of the GBR following a period of intense, widespread disturbances from 2011 to 2022.

Disturbances included COTS outbreaks (Incipient and Active Outbreaks), a tropical cyclone (categories 1&ndash;5) or coral bleaching (categories 1&ndash;5).

Full description found at Emslie, M.J., Ceccarelli, D.M., Logan, M. et al. Changing dynamics of Great Barrier Reef hard coral cover in the Anthropocene. Coral Reefs 43, 747&ndash;762 (2024). https://doi.org/10.1007/s00338-024-02498-5

Organization: Australian Ocean Data Network
Research and resupply vessel Aurora Australis: a platform of the IMOS Ships of Opportunity - Research Vessels Real-Time Air-Sea Flux Sub-Facility

As part of the Ships of Opportunity - Research Vessels Real-Time Air-Sea Flux (ASF) Sub-Facility - multi-purpose research and resupply vessel Aurora Australis collects underway meteorological and oceanographic observations (including SST) during its voyages in Sub-Antarctic and Antarctic areas. Data products are quality controlled meteorological and SST observations, and bulk air-sea fluxes. 

The Aurora Australis is a highly sophisticated multi purpose vessel which is designed to fulfil the role of a research, supply expedition vessel capable of operating in tropical and polar regions. 

The Aurora Australis is a 95m, 3911t vessel and operates under the call sign VNAA. Cruise schedules are available at https://secure3.aad.gov.au/public/schedules/sitreps.cfm

Real-time, Sea Surface Temperature (SST) is processed from the vessel's SBE 38 calibrated temperature sensor located in the water intake pipe at ~5 m depth.

Research Vessel Real Time Air-Sea Fluxes, aims to provide with "climate quality" meteorological measurement systems, capable of providing high quality air-sea flux measurements and delivered to researchers on a near real-time basis. Observations are wind speed and direction, air temperature, relative humidity, air pressure, long-wave, short-wave, ultraviolet and photosynthetically active radiation and precipitation.

Data streams are fed into the existing ship data management system, and broadcast via satellite back to Australia every 30 minutes. The observations are quality controlled at the Bureau of Meteorology and air-sea fluxes calculated using the COARE Bulk Flux algorithm. 

A daily file of 1-minute averages of the observations and a daily file of 5-minute average calculated bulk fluxes are generated shortly after 0000UTC and provided to the AODN.

Tags: Atmosphere | Atmospheric Pressure | Atmospheric Pressure, Atmosphere | Atmospheric Temperature | Skin Temperature, Atmosphere | Atmospheric Temperature | Surface Air Temperature, Atmosphere | Atmospheric Water Vapor | Humidity, Atmosphere | Precipitation | Precipitation Rate, Atmosphere | Radiation Budget | Heat Flux, Atmosphere | Radiation Budget | Longwave Radiation, Atmosphere | Radiation Budget | Shortwave Radiation, Global / Oceans | Indian Ocean, Global / Oceans | Southern Ocean, IMOS Platform | VNAA | Aurora Australis, Oceans | Ocean Optics | Photosynthetically Active Radiation, Oceans | Ocean Temperature | Sea Surface Temperature, Regional Seas | Tasman Sea
Organization: Australian Ocean Data Network
IMOS - Ships of Opportunity - Air-Sea Flux : Meteorological and SST Observations collected on the Research Vessel Aurora Australis

This dataset contains environmental observations as well as ship parameters (such as speed and direction).

As part of the Ships of Opportunity - Research Vessels Real-Time Air-Sea Flux (ASF) Sub-Facility, R.V. Aurora Australis collects underway meteorological and oceanographic observations during scientific and Antarctic resupply voyages in the oceans adjacent to Australia. 

Data streams are fed into the existing ship data management system, and broadcast via satellite back to Australia 30 minutes. The observations are quality controlled at the Bureau of Meteorology and air-sea fluxes calculated using the COARE Bulk Flux algorithm. 

A daily file of 1-minute averages of the observations are generated shortly after 0000UTC and provided to the AODN.

The data is contained in NetCDF format files and consists of environmental observations as well as ship parameters (such as speed and direction). Wind is observed with two different sensors, and the relative (observed) and true (relative to fixed earth) wind speed and direction provided. Air temperature and humidity is observed on the port and starboard of the main mast. Short-wave (sunlight) and long-wave (infra-red radiation or heat, from sky and clouds) is also observed on the port and starboard side. Precipitation is observed with an optical rain-gauge and traditional siphon gauge from the foremast.  Instrument meta-data and QC flags are also contained in the files.

Tags: Atmosphere | Atmospheric Pressure | Atmospheric Pressure, Atmosphere | Atmospheric Temperature | Surface Air Temperature, Atmosphere | Atmospheric Water Vapor | Humidity, Atmosphere | Precipitation | Precipitation Rate, Atmosphere | Radiation Budget | Longwave Radiation, Atmosphere | Radiation Budget | Shortwave Radiation, Countries | Australia, Global / Oceans | Indian Ocean, Global / Oceans | Southern Ocean, Oceans | Ocean Optics | Photosynthetically Active Radiation, Oceans | Ocean Temperature | Sea Surface Temperature, Oceans | Ocean Winds | Surface Winds, Regional Seas | Tasman Sea, air_pressure, air_temperature, rainfall_rate, relative_humidity, sea_water_temperature, surface_downwelling_longwave_flux_in_air, surface_downwelling_photosynthetic_radiative_flux_in_air, surface_downwelling_shortwave_flux_in_air, thickness_of_rainfall_amount, wind_from_direction, wind_speed
Organization: Australian Ocean Data Network
Microbiome dynamics across life stages, generations and climate treatments in a tropical sea urchin

Given the critical role of microbes in host health, understanding microbial contribution to transgenerational plasticity is essential for generating realistic predictions of coral reef responses to future climate. Through a 4-year multigenerational experiment, the microbial dynamics in the tropical sea urchin Echinometra sp. A were explored under temperature and pCO2 levels predicted for years 2050 and 2100 (RCP 8.5).

Publication of this study:

Marangon, E., Uthicke, S., Patel, F., Marzinelli, E. M., Bourne, D. G., Webster, N. S., &amp; Laffy, P. W. (2023). Life-stage specificity and cross-generational climate effects on the microbiome of a tropical sea urchin (Echinodermata: Echinoidea). Molecular Ecology, 00, 1&ndash;16. https://doi.org/10.1111/mec.17124

Realted papers from the same experimental study (urchin physiological and behavioural performances):

Uthicke, S., Patel, F., Karelitz, S., Luter, H., Webster, N., &amp; Lamare, M. (2020). Key biological responses over two generations of the sea urchin Echinometra sp. A under future ocean conditions. Marine Ecology Progress Series, 637, 87&ndash;101. https://doi.org/10.3354/meps13236

Karelitz, S., Lamare, M., Patel, F., Gemmell, N., &amp; Uthicke, S. (2020). Parental acclimation to future ocean conditions increases development rates but decreases survival in sea urchin larvae. Marine Biology, 167(1), 2. https://doi.org/10.1007/s00227-019-3610-5

Uthicke, S., Patel, F., Petrik, C., Watson, S., Karelitz, S. E., &amp; Lamare, M. D. (2021). Cross-generational response of a tropical sea urchin to global change and a selection event in a 43-month mesocosm study. Glob Change Biol, 27: 3448-3462. https://doi.org/10.1111/gcb.15657

Organization: Australian Ocean Data Network
Settlement cue selectivity by larvae of the destructive crown-of-thorns starfish

This study assessed the relative capacity of a diversity of coralline algae to induce COTS settlement in order to test the hypothesis that the alga Lithothamnion cf. proliferum promotes higher settlement rates than the other species. 

Western Pacific COTS (Acanthaster cf. solaris) larvae were reared at the Australian Institute of Marine Science (AIMS) National Sea Simulator. Larval development was microscopically examined until metamorphic competency 14 days post-fertilization was determined. Experimental treatments included 14 living coralline algae species and one living Peyssonneliaceae alga plus structural control (sterile aragonite) and filtered seawater (FSW) control treatments. 12 replicate settlement assays were conducted for each of the 17 experimental treatments with six well cell culture plates, and fully randomised distrubution of all replicate assays among the 204 wells. 

After adding 10 ml FSW and a single chip of one of 15 different algal species or sterile aragonite to the wells, approximately 10 competent COTS larvae per well were introduced using glass pipettes. All well plates were kept in a temperature-controlled room (28&deg;C) matching the light conditions during larval rearing (12 L : 12 D, light&ndash;dark).

Larval settlement was scored 24 and 48 h after larvae were introduced using stereo microscopes. Scoring included:

<code>the number of competent late-brachiolaria larvae remaining in the water column (= swimming)

he number of individuals that had successfully attached to the treatment chip or well bottom and commenced or completed metamorphic transformation into a juvenile with radial symmetry (= settled)
</code>

Statistical analyses were performed using R software (v. 4.1.3). 

The molecular sequences for each algal species can be accessed in the GenBank database under accession numbers OP830444 to OP830473

species  | psbA GenBank accession No. | rbcL GenBank accession No.
Melyvonnea cf. madagascariensis | OP830452 | OP830467
Neogoniolithon fosliei | OP830450 | OP830465
Adeylithon bosencei | OP830454 | OP830469
Hydrolithon cf. reinboldii | OP830457 | OP830472
Lithophyllum cf. insipidum | OP830456 | OP830471
Lithothamnion cf. proliferum | OP830448 | OP830463
Titanoderma cf. tessellatum | OP830447 | OP830462
Amphiroa foliacea | OP830453 | OP830468
Sporolithon sp. | OP830455 | OP830470
Ramicrusta sp. | OP830458 | OP830473
Lithophyllum cf. pygmaeum | OP830449 | OP830464
Porolithon sp. A | OP830446 | OP830461
Porolithon sp. B | OP830444 | OP830459
Lithophyllum cf. kotschyanum | OP830451 | OP830466
Porolithon sp. | C OP830445 | OP830460

Experimental assay data and description are available from the Dryad Digital Repository linked below.

Organization: Australian Ocean Data Network
EDNA data for Crown of Thorns Seastar (Acanthaster cf. solaris) - CCIP-RT-02

Following on from previous CoTS Control Innovation Program (CCIP) projects focused on developing an eDNA method to detect and quantify CoTS, the data here include field data gathered as part of an effort to operationalise previous research. 

Data Dictionary
Field name: Explanation/units
Date: Julian Date
Year: Collection Year
Reef: Reef Name
ReefName: Official GBRMPA Reef name and ID
Site_name: Site name
Site_No: Site number
Lat: Latitude in decimal degrees
Long: Longitude in decimal degrees
Project: Project Code
Collection organisation: Organisation that conducted the primary collection
Collection vessel: Vessel from which samples were collected
Reef_rep: replicate number on a reef basis
Site_rep: replicate number on a site
PCR1_binary: First technical PCR replicate: positive or negative (1 or 0)
PCR2_binary: First technical PCR replicate: positive or negative (1 or 0)
LOD_sample_positive: Limit of Detection based: 1 if either of the technical replicates are positive
PCR1_conc: CoTS eDNA copy number per L water filtered, first technical replicate
PCR2_conc: CoTS eDNA copy number per L water filtered, second technical replicate
Conc_mean: CoTS eDNA copy number per L water filtered, averaged over both replicates

Organization: Australian Ocean Data Network
IMOS - Ships of Opportunity - Research Vessels Real-Time Air-Sea Flux (ASF) Sub-Facility

The Ships of Opportunity - Research Vessels Real-Time Air-Sea Flux (ASF) Sub-Facility, collects underway meteorological and oceanographic observations during scientific  and Antarctic resupply voyages in the oceans adjacent to Australia. Data products are quality controlled observations, and bulk air-sea fluxes and input observations.

Research Vessel Real Time Air-Sea Fluxes, equips the Marine National Facility (MNF) (Research Vessels Southern Surveyor and Investigator), the Australian Antarctic Division (Research and Supply Vessels Aurora Australis and Nuyina), and Research Vessel Tangaroa with "climate quality" meteorological measurement systems, capable of providing high quality air-sea flux measurements and delivered to researchers on a near real-time basis. Obtaining the full set of air-sea fluxes essential for climate studies requires observations of: wind, air and sea temperature, humidity, pressure, precipitation, long- and short-wave radiation. 

The existing ship meteorological sensor sets have been completed with IMOS instruments and a comprehensive annual calibration programs implemented. Data streams are fed into the existing ship data management system, and broadcast via satellite back to Australia routinely. The observations are quality controlled at the Bureau of Meteorology and air-sea fluxes calculated using the COARE Bulk Flux algorithm. 

A daily file of 1-minute averages of the observations and a daily file of calculated bulk fluxes are generated shortly after 0000UTC and provided to IMOS.

Tags: Atmosphere | Atmospheric Pressure | Surface Pressure, Atmosphere | Atmospheric Temperature | Surface Air Temperature, Atmosphere | Atmospheric Water Vapor | Humidity, Atmosphere | Atmospheric Winds | Surface Winds, Atmosphere | Precipitation | Precipitation Amount, Atmosphere | Precipitation | Precipitation Rate, Atmosphere | Radiation Budget | Heat Flux, Atmosphere | Radiation Budget | Longwave Radiation, Atmosphere | Radiation Budget | Shortwave Radiation, Global / Oceans | Indian Ocean, Global / Oceans | Pacific Ocean, Global / Oceans | Southern Ocean, IMOS Platform | VLHJ | Southern Surveyor, IMOS Platform | VLMJ | Investigator, IMOS Platform | VNAA | Aurora Australis, IMOS Platform | ZMFR | Tangaroa, IMOS Sub-Facility | SOOP-ASF | Research Vessels Real-Time Air-Sea Fluxes, Oceans | Ocean Optics | Photosynthetically Active Radiation, Oceans | Ocean Temperature | Sea Surface Temperature, Regional Seas | Coral Sea, Regional Seas | Tasman Sea
Organization: Australian Ocean Data Network
Machine Learning for the Fast and Accurate Assessment of Fitness in Coral Early Life History

A machine learning pipeline was developed to measure three key indicators of coral juvenile fitness rapidly and accurately: survival, size, and colour.

Two substrates were used to classify pixels through a machine learning image analysis pipeline to quickly identify and measure coral juveniles:

a) field deployed terracotta tiles
               -1200 images with three different time point in the field
               -deployed at Davies reef in the Great Barrier Reef for a year-long experiment (see Quigley et.al)

b) laboratory-maintained PVC plastic slides.
               -900 slides with images taken at 12 time points in the laboratory

Both substrates used coral juveniles from the species Acropora tenuis within the first year of life. For both sets of images, measurements were taken manually for proof of study. Size was calibrated using the scale bar present in each image. Colour of juveniles was assessed using the CoralWatch Health Chart and was matched to the closest score on the &ldquo;D&rdquo; scale by a single person to minimise observer bias. Survival of juveniles was classified by eye as either alive or dead.

Machine learning image analysis pipeline was developed for measuring coral survival, size, and colour. Assessment of manual vs. pipeline calculations of coral juvenile colour, size and survival, as well as time comparison between manual vrs pipeline measurements, were carried out. Further details are presented in the publication Macadam et al. (2021).

Organization: Australian Ocean Data Network
Novel reference transcriptomes for the sponges Carteriospongia foliascens and Cliona orientalis and associated algal symbiont Gerakladium endoclionum

Transcriptomic analysis of sponges that have been exposed to different environmental conditions can improve our understanding of molecular stress response pathways and enhance our ability to effectively manage these ecologically important filter feeders.

Two common and widely distributed Indo-Pacific sponge species&mdash;Carteriospongia foliascens and Cliona orientalis were collected in May 2015 from Fantome Island and Pelorus Island in the Great Barrier Reef. Since C. orientalis is a bio-eroding sponge, ten C. orientalis drill cores (* 5cmin

diameter) were collected from a single individual growing on a dead colony of Porites sp. An individual of C.foliascens was cut into ten pieces (see Pineda et al. 2016). Sponges were healed and acclimated under natural light and flowthrough seawater for 4 weeks before experiments were performed.

Sponges were subjected to five different treatments at the Australian Institute of Marine Science (AIMS) National Sea Simulator: (i) decreased salinity, (ii) elevated temperature, (iii) elevated suspended sediment concentrations(SSCs) and sediment deposition, (iv) light attenuation, and(v) no stress control. Further details on these experiments can be found in Pineda et al. (2016).

Transcriptome sequences were assembled for C. foliascens and C. ori-entalis and from the C. orientalis holobiont, i.e., host and symbiont, a partial reference transcriptome for Gerakladium endoclionum was constructed. Further details are found in the linked paper below Strehlow et al. (2021).

Organization: Australian Ocean Data Network
Cape Ferguson (AIMS Wharf) Automated Marine Weather And Oceanographic Station

This historical weather data from the Cape Ferguson (AIMS Wharf) weather outstation covers the period 1-11-1983 to 30-5-1984.

Afterwards, the same unit was deployed at Cape Bowling Green and collected data at that location for the period 18-9-1984 to 4-10-1985.

Both data collection periods used telemetery to send binary data daily to a computer controlled base station. Data was verified by comparing three sets of the same data, received over three days. The base station passed data to the central computing facility at AIMS for processing.

Data recorded covers: Wind direction (0-360&deg;); Wind run (km/hr); Air temperature (0-40&deg;C); Water temperature (0-40&deg;C); Barometric pressure (mB); Solar radiation (0-2500 &micro;E/sec/M2).

Other variables:
Time of day (e.g. 23:46:03); Day of the year (e.g. 118); Year (e.g. 86).

These data are collected to support scientific research. Data are made available on request to other researchers and to the public as well as being available from the AIMS web site

Information about accuracy and the sensors used are in the Data Quality section of this metadata record.

Organization: Australian Ocean Data Network
Interactions among wildtype and heat-evolved photosymbionts shape performance of coral recruits

This data shows evidence that interactions between Symbiodiniaceae strains influence the performance of their coral host.

The data was collected as part of a study investigating the implication of inoculating coral recruits with multiple Symbiodiniaceae strains.

This data was collected in the National Sea Simulator at the Australian Institute of Marine Science between November 2022 and February 2023.

This data was collected using a Nikon D810, and by taking biological samples for NextGen sequencing. 

This data was analysed using code available at https://github.com/ViableSpider/SI_IVORY_2025

Organization: Australian Ocean Data Network
Shark depredation and behavioural interactions with fishing gear in a recreational fishery in Western Australia

Shark depredation, whereby a shark consumes an animal caught by fishing gear, can cause higher mortality for target species, injury to sharks and the loss of catch and fishing gear. A critical first step towards potential mitigation is understanding this behaviour and the shark species involved, because the identity of depredating shark species is unknown in many fisheries, and behavioural dynamics of shark interactions with fishing gear are not well understood. We used line-mounted video cameras in a recreational fishery in the Ningaloo region of Western Australia to: (1) identify shark species responsible for depredation, (2) investigate behavioural interactions with fishing gear, (3) identify the prevalence of retained fishing gear in sharks and (4) quantify the influence of environmental variables and fishing methods on shark abundance during demersal fishing at 92 locations.

The study was led by UWA (Jon Mitchell - then PhD student) and performed in the Ningaloo region of Western Australia.

<code>We mounted high-definition video cameras on fishing lines to monitor shark depredation events and interactions with fishing gear.

We recorded location, number of lines in the water, whether fishing vessels were fishing at anchor vs. drifting, fishing depth, gear and bait type
</code>

The influence of fishing methods and environmental variables on the abundance of sharks at each location was investigated. All data were collected on an ipad using the software application &#39;Collector for ArcGIS&#39;.

Video was analysed using EventMeasure.

Organization: Australian Ocean Data Network