JOURNAL ARTICLE

Leaf optical properties and photosynthesis of fern species with a wide range of divergence time in relation to mesophyll anatomy.

  • Published In: Annals of Botany, 2023, v. 131, n. 3. P. 437 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hanba, Yuko T; Nishida, Keisuke; Tsutsui, Yuuri; Matsumoto, Mayu; Yasui, Yutarou; Sizhe, Yang; Matsuura, Takumi; Akitsu, Tomoko Kawaguchi; Kume, Atsushi 3 of 3

Abstract

This article investigates the evolutionary changes in leaf mesophyll anatomy, optical properties, and photosynthetic capacity across 66 fern species from natural habitats and eight glasshouse-grown species with varying divergence times. It finds that more recently diverged ferns tend to have thicker mesophyll layers, increased chloroplast surface area facing intercellular airspaces (Sc), and thicker cell walls, which enhance light absorptance but do not correspond to higher photosynthetic capacity on a leaf-area basis. When photosynthetic traits are normalized by mesophyll thickness, more recently diverged species show lower maximum photosynthesis rates and Sc, suggesting a decrease in chloroplast density per mesophyll volume that may optimize light absorption per chloroplast under low-light conditions. The study concludes that increased mesophyll thickness and cell wall thickness in newer fern species likely improve leaf toughness and environmental adaptability rather than maximizing photosynthetic efficiency, contributing to fern diversification.

Additional Information

  • Source:Annals of Botany. 2023/02, Vol. 131, Issue 3, p437
  • Document Type:Article
  • Subject Area:Anatomy and Physiology
  • Publication Date:2023
  • ISSN:0305-7364
  • DOI:10.1093/aob/mcad025
  • Accession Number:162901637
  • Copyright Statement:Copyright of Annals of Botany 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.