JOURNAL ARTICLE
Low genetic differentiation despite high habitat fragmentation in an endemic and endangered species of Iridaceae from South America: implications for conservation.
Published In: Botanical Journal of the Linnean Society, 2025, v. 207, n. 1. P. 56 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Forgiarini, Cristiane; Meimberg, Harald; Curto, Manuel; Stiehl-Alves, Eudes M; Vijayan, Thapasya; Engl, Pia T; Bräuchler, Christian; Kollmann, Johannes; Souza-Chies, Tatiana T de 3 of 3
Abstract
This article focuses on the genetic structure, gene flow, mating system, and conservation status of Herbertia zebrina Deble, a critically endangered herbaceous plant endemic to the grasslands of southern Brazil's Brazilian Pampa biome. Using 15 microsatellite markers, including 10 newly developed for this study, researchers identified three genetic clusters with low differentiation (7%) and limited migration among populations, but no evidence of recent genetic bottlenecks. The species is outcrossing and relies on specialized pollinators, which were rarely observed, suggesting potential pollination disturbances possibly linked to habitat fragmentation and environmental changes. Although habitat fragmentation is recent and not yet significantly correlated with genetic diversity loss, the findings highlight the need for conservation plans to preserve genetic variation and maintain metapopulation dynamics critical for the species' long-term survival.
Additional Information
- Source:Botanical Journal of the Linnean Society. 2025/01, Vol. 207, Issue 1, p56
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2025
- ISSN:0024-4074
- DOI:10.1093/botlinnean/boae036
- Accession Number:182368459
- 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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