Data

Wave energy and other environmental drivers as predictors of seeded-coral performance on the Great Barrier Reef

Australian Institute of Marine Science
Australian Institute of Marine Science (AIMS)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
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Devices were placed at 10 sites per reef (n = 25 devices per site), spanning five wave-energy levels, and censused over 1.5 to 2 years. Environmental data included wave energy estimates, bottom stress, flow velocity, sedimentation, and benthic community composition. Analyses used Bayesian models to assess survival and size in relation to environmental predictors. The dataset supports research on fine-scale spatial variability in coral restoration outcomes.Maintenance and Update Frequency: notPlannedStatement: The dataset was collected using standardized and validated methods to ensure high data quality and consistency. Coral larvae were sourced from broodstock colonies collected at Moore, Davies, and Heron Reefs (permits #G21/45348.1 and #G21/45166.1) and spawned at the Australian Institute of Marine Science (AIMS) under controlled conditions. Larval rearing, settlement, and device preparation followed established protocols to minimize variability in initial conditions. 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While AIMS has made every reasonable effort to ensure high quality of the data, products and services, to the extent permitted by law the data, products and services are provided without any warranties of any kind, either expressed or implied, including without limitation any implied warranties of title, merchantability, and fitness for a particular purpose or non-infringement. AIMS make no representation or warranty that the data, products and services are accurate, complete, reliable or current. To the extent permitted by law, AIMS exclude all liability to any person arising directly or indirectly from the use of the data, products and services.&rft_rights=Attribution: Format for citation of metadata sourced from Australian Institute of Marine Science (AIMS) in a list of reference is as follows: Australian Institute of Marine Science (AIMS). (2025). Wave energy and other environmental drivers as predictors of seeded-coral performance on the Great Barrier Reef. https://doi.org/10.25845/9PAE-ZZ03, accessed[date-of-access].&rft_subject=oceans&rft_subject=Temperature of the water body&rft_subject=Current speed in the water body&rft_subject=Total Solids Sediment&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Open Licence view details
CC-BY

Creative Commons Attribution 3.0 Australia License
http://creativecommons.org/licenses/by/3.0/au/

Use Limitation: All AIMS data, products and services are provided "as is" and AIMS does not warrant their fitness for a particular purpose or non-infringement. While AIMS has made every reasonable effort to ensure high quality of the data, products and services, to the extent permitted by law the data, products and services are provided without any warranties of any kind, either expressed or implied, including without limitation any implied warranties of title, merchantability, and fitness for a particular purpose or non-infringement. AIMS make no representation or warranty that the data, products and services are accurate, complete, reliable or current. To the extent permitted by law, AIMS exclude all liability to any person arising directly or indirectly from the use of the data, products and services.

Attribution: Format for citation of metadata sourced from Australian Institute of Marine Science (AIMS) in a list of reference is as follows: "Australian Institute of Marine Science (AIMS). (2025). Wave energy and other environmental drivers as predictors of seeded-coral performance on the Great Barrier Reef. https://doi.org/10.25845/9PAE-ZZ03, accessed[date-of-access]".

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Full description

This dataset explores how wave energy affects the survival and growth of coral recruits on seeded devices. Data were collected to understand how environmental factors influence post-settlement coral survival, aiming to improve seeding device deployment strategies.


Broodstock colonies of three Acropora species (A. hyacinthus, A. millepora, and A. tenuis) were collected and spawned at the Australian Institute of Marine Science. The resulting larvae were reared, settled onto devices, and deployed between January and February 2022 across wave energy gradients at Moore, Davies, and Heron Reefs on the Great Barrier Reef. Devices were placed at 10 sites per reef (n = 25 devices per site), spanning five wave-energy levels, and censused over 1.5 to 2 years. Environmental data included wave energy estimates, bottom stress, flow velocity, sedimentation, and benthic community composition. Analyses used Bayesian models to assess survival and size in relation to environmental predictors. The dataset supports research on fine-scale spatial variability in coral restoration outcomes.

Lineage

Maintenance and Update Frequency: notPlanned
Statement: The dataset was collected using standardized and validated methods to ensure high data quality and consistency. Coral larvae were sourced from broodstock colonies collected at Moore, Davies, and Heron Reefs (permits #G21/45348.1 and #G21/45166.1) and spawned at the Australian Institute of Marine Science (AIMS) under controlled conditions. Larval rearing, settlement, and device preparation followed established protocols to minimize variability in initial conditions. Survival data were collected quarterly at Moore and Davies Reefs and annually at Heron Reef. Devices (n = 25 per site) were scored for survival at the tab level (n = 3 tabs per device), using a binary metric where a value of 1 indicated the presence of at least one surviving coral on the tab. Environmental data were obtained from multiple sources and instruments: Wave energy and bottom stress: Modelled at a 10-m resolution using numerical wave models (Callaghan et al., 2015). Flow velocity: Measured in situ with drag-tilt current meters (Marotte HS) deployed approximately 50 cm above the substrate, recording at 1-minute intervals. Temperature: Recorded at 10-minute intervals using HOBO Water Temp Pro v2 loggers. Sedimentation: Quantified using standardized SedPods and TurfPods, with sediment samples filtered, dried, and weighed. Benthic community composition: Documented through quadrat photography and analyzed via ReefCloud, with both manual and AI-assisted point classification. Quality assurance measures included consistent deployment protocols, replicated site sampling, regular calibration of instruments, and validation of model outputs with field data. Data were checked for anomalies and missing values. Bayesian models were validated through diagnostic checks (e.g., trace plots, Rhat values), and environmental predictors were cross-verified through correlation tests and principal component analyses to ensure reliability.

Notes

Credit
Jurriaans, S
Credit
Australian Institute of Marine Science (AIMS), Australia
Credit
Randall, CJ
Credit
Lefevre, C
Credit
Page, CA
Credit
Giuliano, C
Credit
Whitman, TN
Credit
Sims, CA
Credit
Radford, B
Credit
Puotinen, M
Credit
Allen, K
Credit
Reef Restoration and Adaptation Program (RRAP)

Modified: 19 09 2025

This dataset is part of a larger collection

Click to explore relationships graph

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Identifiers
  • global : c3ede810-4115-4276-b19c-176a8566ad85
ACN 633 798 857