JOURNAL ARTICLE

Floral development of one of the oldest dioecious lineages of Arecaceae reveals different stages of dicliny in pistillate and staminate flowers.

  • Published In: Botanical Journal of the Linnean Society, 2023, v. 201, n. 4. P. 400 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Reis, Sarah Barbosa; Mello, Ana Caroline Marques Pereira; Rech, André Rodrigo; Oliveira, Denise Maria Trombert 3 of 3

Abstract

This article focuses on the anatomical floral development of dioecious species in the Arecaceae subtribe Mauritiinae, a lineage within the Calamoideae subfamily notable for its diversity of sexual systems. Using light microscopy, the study describes the ontogeny of staminate and pistillate flowers in Lepidocaryum tenue, Mauritia flexuosa, and Mauritiella armata, identifying three key developmental phases and revealing that flowers initially form all four floral whorls before differentiating into unisexual forms. The findings suggest that Mauritiinae flowers may have evolved from a hermaphrodite ancestor, with staminate flowers showing degeneration of pistillodes and pistillate flowers retaining staminodes that produce non-viable pollen, potentially serving to attract pollinators. Overall, the floral anatomy across these species is similar, supporting their close phylogenetic relationship and indicating a transitional stage in the evolution of dicliny within this group.

Additional Information

  • Source:Botanical Journal of the Linnean Society. 2023/04, Vol. 201, Issue 4, p400
  • Document Type:Article
  • Subject Area:Anatomy and Physiology
  • Publication Date:2023
  • ISSN:0024-4074
  • DOI:10.1093/botlinnean/boac063
  • Accession Number:162589535
  • Copyright Statement:Copyright of Botanical Journal of the Linnean Society 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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