JOURNAL ARTICLE
Contrasting patterns of genetic structure and population demography in two Dicraeus species feeding on bamboo flowers in Japan.
Published In: Biological Journal of the Linnean Society, 2024, v. 143, n. 2. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sakata, Yuzu; Hirota, Shun K; Matsuo, Ayumi; Kobayashi, Keito; Nakahama, Naoyuki; Suyama, Yoshihisa 3 of 3
Abstract
This article focuses on the phylogenetic relationships and population genetic structures of two bamboo-feeding insect species, *Dicraeus phyllostachyus* and *Dicraeus nartshukae*, in Japan, leveraging a recent nationwide mass flowering event of bamboo. Using mitochondrial cytochrome oxidase I (COI) gene sequencing and genome-wide single-nucleotide polymorphisms (SNPs) detected by multiplexed inter-simple sequence repeat genotyping by sequencing (MIG-seq), the study found genetic similarity across wide geographic ranges for both species but contrasting population structures: *D. phyllostachyus* showed no clear genetic clustering and evidence of recent population expansion, while *D. nartshukae* exhibited distinct northern and southern genetic groups with limited gene flow between them. The findings suggest that bamboo mass flowering events influence the genetic structure of these florivorous insects through trophic interactions and highlight the importance of comparing closely related species to understand outbreak dynamics and evolutionary history in phytophagous insects.
Additional Information
- Source:Biological Journal of the Linnean Society. 2024/10, Vol. 143, Issue 2, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2024
- ISSN:0024-4066
- DOI:10.1093/biolinnean/blad171
- Accession Number:180625886
- Copyright Statement:Copyright of Biological 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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