JOURNAL ARTICLE

Comparative analysis of biofilm bacterial communities developed on different artificial reef materials.

  • Published In: Journal of Applied Microbiology, 2024, v. 135, n. 11. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Sajid, Sumbal; Zhang, Guoqiang; Zhang, Zongyao; Chen, Lianguo; Lu, Yishan; Fang, James Kar-Hei; Cai, Lin 3 of 3

Abstract

This article focuses on how different artificial reef materials influence the composition of biofilm bacterial communities and their potential to support coral larval settlement, which is critical for coral reef restoration. The study compared six materials—porcelain, granite, coral skeleton, calcium carbonate, shell cement (a mix of oyster shell powder and cement), and cement—by analyzing biofilm bacterial diversity using 16S rRNA gene sequencing. Results showed that biofilms on all materials had higher bacterial richness and evenness than seawater, with dominant phyla including Pseudomonadota, Cyanobacteria, and Bacteroidetes. Notably, shell cement biofilms exhibited higher abundances of bacterial genera such as Pseudoalteromonas and Thalassomonas, which are considered potential settlement inducers for coral larvae, suggesting shell cement’s suitability for enhancing coral rehabilitation efforts. The findings highlight the importance of substrate material selection in artificial reef design to optimize microbial communities that facilitate coral larval settlement and reef restoration.

Additional Information

  • Source:Journal of Applied Microbiology. 2024/11, Vol. 135, Issue 11, p1
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2024
  • ISSN:1364-5072
  • DOI:10.1093/jambio/lxae268
  • Accession Number:181249320
  • Copyright Statement:Copyright of Journal of Applied Microbiology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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