JOURNAL ARTICLE

On the Challenges of Identifying Benthic Dominance on Anthropocene Coral Reefs.

  • Published In: BioScience, 2023, v. 73, n. 3. P. 220 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Tebbett, Sterling B; Crisp, Samantha K; Evans, Richard D.; Fulton, Christopher J; Pessarrodona, Albert; Wernberg, Thomas; Wilson, Shaun K; Bellwood, David R 3 of 3

Abstract

This article examines the concept of dominance in coral reef ecosystems, highlighting how varying definitions and methodological approaches to measuring dominance—particularly the inclusion or exclusion of benthic categories like algal turfs and crustose coralline algae—significantly influence perceptions of reef community structure and phase shifts. Using a global dataset of benthic composition, the study finds that algal groups often dominate reef benthos more than previously recognized, and that excluding certain benthic components inflates coral dominance estimates. The authors emphasize the need for a standardized, comprehensive framework for benthic classification and monitoring that accounts for the full diversity of reef organisms, especially different algal functional groups, to better understand ecosystem changes and manage coral reefs effectively in the Anthropocene. They also note that shifts in dominance between corals and algae occur in both directions more frequently than traditionally assumed, suggesting a more dynamic reef benthic landscape than commonly portrayed.

Additional Information

  • Source:BioScience. 2023/03, Vol. 73, Issue 3, p220
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2023
  • ISSN:0006-3568
  • DOI:10.1093/biosci/biad008
  • Accession Number:162631842
  • Copyright Statement:Copyright of BioScience 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.