JOURNAL ARTICLE
Cuticular morphology of Schinus L. and related genera.
Published In: Botanical Journal of the Linnean Society, 2025, v. 208, n. 1. P. 91 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Matel, Theodore P; Gandolfo, Maria A; Mitchell, John D 3 of 3
Abstract
This article focuses on the characterization of leaf cuticular traits in four genera of the Anacardiaceae family—Schinus, Lithrea, Mauria, and Euroschinus—using scanning electron and light microscopy. It documents variation in stomatal distribution, stomatal complex morphology, trichome types, and epidermal cell features across 53 species, highlighting that Euroschinus uniquely possesses acuminate-cylindrical glands, which may serve as a synapomorphy for the genus. Schinus, the most species-rich Neotropical genus studied, exhibits considerable variability in stomatal distribution, including hypostomous, amphistomous, and partially amphistomous leaves, with evidence suggesting this trait may be influenced by phenotypic plasticity rather than strictly phylogenetic factors. Multivariate analyses show that cuticular morphology effectively distinguishes among the genera Euroschinus, Mauria, and Lithrea but does not clearly resolve species groups within Schinus. The study underscores the systematic value of trichome characters in Anacardiaceae and calls for further research on stomatal variability using multiple specimens and controlled environmental studies.
Additional Information
- Source:Botanical Journal of the Linnean Society. 2025/05, Vol. 208, Issue 1, p91
- Document Type:Article
- Subject Area:Agriculture and Agribusiness
- Publication Date:2025
- ISSN:0024-4074
- DOI:10.1093/botlinnean/boae071
- Accession Number:185321234
- 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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