Data

Machine Learning for the Fast and Accurate Assessment of Fitness in Coral Early Life History

Name: Machine Learning for the Fast and Accurate Assessment of Fitness in Coral Early Life History
Published: 2026

Australian Institute of Marine Science

Australian Institute of Marine Science (AIMS)

Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]

ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://apps.aims.gov.au/metadata/view/db810609-3fac-41e2-8453-b0945e8d86be&rft.title=Machine Learning for the Fast and Accurate Assessment of Fitness in Coral Early Life History&rft.identifier=https://apps.aims.gov.au/metadata/view/db810609-3fac-41e2-8453-b0945e8d86be&rft.publisher=Australian Institute of Marine Science (AIMS)&rft.description=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 laboratoryBoth 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 “D” 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).Maintenance and Update Frequency: asNeededStatement: Quigley, K.M.; Marzonie, M.; Ramsby, B.; Abrego, D.; Milton, G.; van Oppen, M.J.; Bay, L.K. Variability in Fitness Trade-Offs Amongst Coral Juveniles With Mixed Genetic Backgrounds Held in the Wild. Front. Mar. Sci. 2021, 8, 161. https://doi.org/10.3389/fmars.2021.636177 (Data Record: https://apps.aims.gov.au/metadata/view/6280b092-f96e-414e-8251-9f4e64694812)&rft.creator=Australian Institute of Marine Science (AIMS) &rft.date=2026&rft.coverage=westlimit=147.056138; southlimit=-19.268297; eastlimit=147.056138; northlimit=-19.268297&rft.coverage=westlimit=147.056138; southlimit=-19.268297; eastlimit=147.056138; northlimit=-19.268297&rft.coverage=westlimit=147.6495; southlimit=-18.8217; eastlimit=147.6495; northlimit=-18.8217&rft.coverage=westlimit=147.6495; southlimit=-18.8217; eastlimit=147.6495; northlimit=-18.8217&rft_rights=Creative Commons Attribution 3.0 Australia License http://creativecommons.org/licenses/by/3.0/au/&rft_rights=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.&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). (2021). Machine Learning for the Fast and Accurate Assessment of Fitness in Coral Early Life History. https://apps.aims.gov.au/metadata/view/db810609-3fac-41e2-8453-b0945e8d86be, accessed[date-of-access].&rft_subject=oceans&rft.type=dataset&rft.language=English Access the data

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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). (2021). Machine Learning for the Fast and Accurate Assessment of Fitness in Coral Early Life History. https://apps.aims.gov.au/metadata/view/db810609-3fac-41e2-8453-b0945e8d86be, accessed[date-of-access]".

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Full description

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 “D” 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).

Lineage

Maintenance and Update Frequency: asNeeded

Statement: Quigley, K.M.; Marzonie, M.; Ramsby, B.; Abrego, D.; Milton, G.; van Oppen, M.J.; Bay, L.K. Variability in Fitness Trade-Offs Amongst Coral Juveniles With Mixed Genetic Backgrounds Held in the Wild. Front. Mar. Sci. 2021, 8, 161. https://doi.org/10.3389/fmars.2021.636177 (Data Record: https://apps.aims.gov.au/metadata/view/6280b092-f96e-414e-8251-9f4e64694812)

Notes

Credit
Macadam, A (AIMS)

Credit
Nowell, CJ. Monash University, Australia

Credit
Quigley, K. (AIMS)

Modified: 25 09 2026

This dataset is part of a larger collection

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text: westlimit=147.056138; southlimit=-19.268297; eastlimit=147.056138; northlimit=-19.268297

text: westlimit=147.6495; southlimit=-18.8217; eastlimit=147.6495; northlimit=-18.8217

Subjects

oceans |

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Other Information

Macadam, A., Nowell, C. J., & Quigley, K. (2021). Machine Learning for the Fast and Accurate Assessment of Fitness in Coral Early Life History. Remote Sensing, 13(16). https://doi.org/10.3390/rs13163173

doi : https://doi.org/10.3390/rs13163173 DOI Link

GitHub Repository: Machine Learning pipeline for measuring coral survival, size, and colour https://github.com/LaserKate/Coral_Ilastik_Pipeline

url : https://github.com/LaserKate/Coral_Ilastik_Pipeline External Link

Identifiers

global : db810609-3fac-41e2-8453-b0945e8d86be