Potential impact of coral reef condition on stress and cleaning behaviour of the Caribbean sharknose gobies Elacatinus evelynae.
Published In: Cybium: International Journal of Ichthyology, 2025, v. 49, n. 4. P. 359 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: POÇAS, Ana M.; SANTOS, Teresa; MESSIAS, João P. M.; WINBERG, Svante; de ABREU, Murilo S.; SOARES, Marta C. 3 of 3
Abstract
Coral reefs are among the most diverse and complex ecosystems. However, these habitats are currently facing severe pressures due to climate change, costal development and coral disease, influencing life on coral reefs. Considering that the degradation of the coral reefs can asymmetrically impact all organisms, we here asked if coral reef condition variability would be associated with change in stress and cleaning behaviour in Caribbean sharknose cleaning gobies Elacatinus evelynae. This study was conducted across five different reefs of the Island of Curaçao. We found significant differences in reef condition, in fish communities, in numbers of cleaning gobies and cleaning stations (i.e., goby territories) across all our sampled reefs. Specifically, the gobies from Carmabi (the most degraded reef) showed simultaneously lower cleaning activity and higher whole-body cortisol levels compared to gobies from other reefs. Collectively, our findings suggest that reef condition status may be implicated to stress and behavioural output variation of cleaning gobies. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Cybium: International Journal of Ichthyology. 2025/12, Vol. 49, Issue 4, p359
- Document Type:Article
- Subject Area:Science
- Publication Date:2025
- ISSN:0399-0974
- DOI:10.26028/cybium/2025-031
- Accession Number:191442618
- Copyright Statement:Copyright of Cybium: International Journal of Ichthyology is the property of Societe Francaise d'Ichtyologie (SFI) 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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