Data

Antifouling coatings can reduce algal growth in coral aquaculture

Name: Antifouling coatings can reduce algal growth in coral aquaculture
Published: 2026

Australian Institute of Marine Science

Australian Institute of Marine Science (AIMS) ; Australian Institute of Marine Science (AIMS) & Leibniz Centre for Tropical Marine Research

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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25845/ws1n-ah16&rft.title=Antifouling coatings can reduce algal growth in coral aquaculture&rft.identifier=https://doi.org/10.25845/ws1n-ah16&rft.publisher=Australian Institute of Marine Science (AIMS)&rft.description=The data were collected to test the antifouling efficacy and fouling community composition of recently-developed antifouling coatings on coral plugs. The same plugs were tested for A. tenuis coral larval settlement, to investigate, whether these coatings were a deterrent for coral settlement. The ultimate goal of the research was to investigate whether the coatings reduced algal growth without inhibiting larval settlement adjacent to the coatings. The data were collected from November to December, 2019 at AIMS, by the use of a camera trolley (Nikon D810 with a Nikon AF-S 60 mm f/2.8 G Micro ED Lens outfitted with four Ikelite DS160 strobes). The data (images) were analysed with the Trainable Weka Segmentation (TWS) plugin in the ImageJ software.This data collection includes: Images of coral settlement plugs with coral recruits and algal fouling Model used by the Trainable Weka Segmentation (TWS) plugin for Fiji processing program in ImageJ (version 1.53c) – this was used to segment images into different classes by the use of machine-learning based image classification modelsMaintenance and Update Frequency: notPlanned&rft.creator=Australian Institute of Marine Science (AIMS) &rft.creator=Australian Institute of Marine Science (AIMS) & Leibniz Centre for Tropical Marine Research &rft.date=2026&rft.coverage=westlimit=147.64451015136808; southlimit=-18.820972003789773; eastlimit=147.64451015136808; northlimit=-18.820972003789773&rft.coverage=westlimit=147.64451015136808; southlimit=-18.820972003789773; eastlimit=147.64451015136808; northlimit=-18.820972003789773&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_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) & Leibniz Centre for Tropical Marine Research. (2022). Antifouling coatings can reduce algal growth in coral aquaculture. https://doi.org/10.25845/ws1n-ah16, 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) & Leibniz Centre for Tropical Marine Research. (2022). Antifouling coatings can reduce algal growth in coral aquaculture. https://doi.org/10.25845/ws1n-ah16, accessed[date-of-access]".

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The data were collected to test the antifouling efficacy and fouling community composition of recently-developed antifouling coatings on coral plugs. The same plugs were tested for A. tenuis coral larval settlement, to investigate, whether these coatings were a deterrent for coral settlement. The ultimate goal of the research was to investigate whether the coatings reduced algal growth without inhibiting larval settlement adjacent to the coatings. The data were collected from November to December, 2019 at AIMS, by the use of a camera trolley (Nikon D810 with a Nikon AF-S 60 mm f/2.8 G Micro ED Lens outfitted with four Ikelite DS160 strobes). The data (images) were analysed with the Trainable Weka Segmentation (TWS) plugin in the ImageJ software.

This data collection includes:

Images of coral settlement plugs with coral recruits and algal fouling

Model used by the Trainable Weka Segmentation (TWS) plugin for Fiji processing program in ImageJ (version 1.53c) – this was used to segment images into different classes by the use of machine-learning based image classification models

Lineage

Maintenance and Update Frequency: notPlanned

Notes

Credit
Funding: Reef Restoration and Adaptation Program which is funded by the partnership between the Australian Governments Reef Trust and the Great Barrier Reef Foundation.

Credit
Röpke, LK. (1)

Credit
Soltmann, U. (2)

Credit
Randall, CJ. (3)

Credit
Kunzmann, A. (1)

Credit
(1) Leibniz Centre for Tropical Marine Research, Bremen, Germany

Credit
(2) Gesellschaft zur Förderung von Medizin-, Bio- und Umwelttechnologien e.V., Dresden, Germany

Credit
Brefeld, D. (1)

Credit
Negri, AP. (3)

Credit
(3) Australian Institute of Marine Science (AIMS)

Modified: 18 09 2026

This dataset is part of a larger collection

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text: westlimit=147.64451015136808; southlimit=-18.820972003789773; eastlimit=147.64451015136808; northlimit=-18.820972003789773

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

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oceans |

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Other Information

Roepke, L.K., Brefeld, D., Soltmann, U. et al. Antifouling coatings can reduce algal growth while preserving coral settlement. Sci Rep 12, 15935 (2022). https://doi.org/10.1038/s41598-022-19997-6

doi : https://doi.org/10.1038/s41598-022-19997-6 DOI Link

Identifiers

global : db074199-2d0f-4800-8cf9-6d409630b8c4

Antifouling coatings can reduce algal growth in coral aquaculture

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Notes

This dataset is part of a larger collection

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Fouling_intensity_classification_Roepke_et_al.csv

Segmented Images 946 tif files [zip folder size: 104 MB]

Cropped plugs, 945 tif files [zip folder size: 2.65 GB]

Final model and training files [zip folder size: 543 MB]

Roepke, L.K., Brefeld, D., Soltmann, U. et al. Antifouling coatings can reduce algal growth while preserving coral settlement. Sci Rep 12, 15935 (2022). https://doi.org/10.1038/s41598-022-19997-6