JOURNAL ARTICLE

Seeing across variable ecological and social environments: comparative eye morphology of marine and terrestrial hermit crabs (Decapoda: Anomura: Coenobitidae, Paguridae).

  • Published In: Journal of Crustacean Biology, 2024, v. 44, n. 2. P. 1 1 of 3

  • Database: Environment Complete 2 of 3

  • Authored By: Steele, Elliott P; Laidre, Mark E 3 of 3

Abstract

This article focuses on comparative external eye morphology among three hermit crab species (Crustacea: Decapoda: Anomura) inhabiting distinct ecological and social environments: the highly social, diurnal terrestrial species Coenobita compressus; its less social, nocturnal sister species Coenobita clypeatus from a rugged forested habitat; and the less social marine intertidal species Pagurus longicarpus. The study found significant macroscopic differences in eye dimensions (length, width, thickness) between marine and terrestrial species, and notably larger eye volume in the highly social C. compressus compared to C. clypeatus, potentially linked to its social lifestyle. Microscopically, all species exhibited higher ommatidial facet density and smaller facet diameter in the ventral eye region compared to the dorsal region, suggesting conserved facet distribution patterns despite environmental differences. These findings provide a foundational framework for future phylogenetically controlled investigations into the ecological and social drivers of eye morphology within this diverse clade.

Additional Information

  • Source:Journal of Crustacean Biology. 2024/06, Vol. 44, Issue 2, p1
  • Document Type:Article
  • Subject Area:Anatomy and Physiology
  • Publication Date:2024
  • ISSN:0278-0372
  • DOI:10.1093/jcbiol/ruae025
  • Accession Number:178184569
  • Copyright Statement:Copyright of Journal of Crustacean Biology 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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