Honey bee (Apis mellifera) wing images: a tool for identification and conservation.

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES GigaScience Pub Date : 2023-03-20 Epub Date: 2023-03-27 DOI:10.1093/gigascience/giad019
Andrzej Oleksa, Eliza Căuia, Adrian Siceanu, Zlatko Puškadija, Marin Kovačić, M Alice Pinto, Pedro João Rodrigues, Fani Hatjina, Leonidas Charistos, Maria Bouga, Janez Prešern, İrfan Kandemir, Slađan Rašić, Szilvia Kusza, Adam Tofilski
{"title":"Honey bee (Apis mellifera) wing images: a tool for identification and conservation.","authors":"Andrzej Oleksa, Eliza Căuia, Adrian Siceanu, Zlatko Puškadija, Marin Kovačić, M Alice Pinto, Pedro João Rodrigues, Fani Hatjina, Leonidas Charistos, Maria Bouga, Janez Prešern, İrfan Kandemir, Slađan Rašić, Szilvia Kusza, Adam Tofilski","doi":"10.1093/gigascience/giad019","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The honey bee (Apis mellifera) is an ecologically and economically important species that provides pollination services to natural and agricultural systems. The biodiversity of the honey bee in parts of its native range is endangered by migratory beekeeping and commercial breeding. In consequence, some honey bee populations that are well adapted to the local environment are threatened with extinction. A crucial step for the protection of honey bee biodiversity is reliable differentiation between native and nonnative bees. One of the methods that can be used for this is the geometric morphometrics of wings. This method is fast, is low cost, and does not require expensive equipment. Therefore, it can be easily used by both scientists and beekeepers. However, wing geometric morphometrics is challenging due to the lack of reference data that can be reliably used for comparisons between different geographic regions.</p><p><strong>Findings: </strong>Here, we provide an unprecedented collection of 26,481 honey bee wing images representing 1,725 samples from 13 European countries. The wing images are accompanied by the coordinates of 19 landmarks and the geographic coordinates of the sampling locations. We present an R script that describes the workflow for analyzing the data and identifying an unknown sample. We compared the data with available reference samples for lineage and found general agreement with them.</p><p><strong>Conclusions: </strong>The extensive collection of wing images available on the Zenodo website can be used to identify the geographic origin of unknown samples and therefore assist in the monitoring and conservation of honey bee biodiversity in Europe.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"12 ","pages":""},"PeriodicalIF":11.8000,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10041535/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GigaScience","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/gigascience/giad019","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/3/27 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Background: The honey bee (Apis mellifera) is an ecologically and economically important species that provides pollination services to natural and agricultural systems. The biodiversity of the honey bee in parts of its native range is endangered by migratory beekeeping and commercial breeding. In consequence, some honey bee populations that are well adapted to the local environment are threatened with extinction. A crucial step for the protection of honey bee biodiversity is reliable differentiation between native and nonnative bees. One of the methods that can be used for this is the geometric morphometrics of wings. This method is fast, is low cost, and does not require expensive equipment. Therefore, it can be easily used by both scientists and beekeepers. However, wing geometric morphometrics is challenging due to the lack of reference data that can be reliably used for comparisons between different geographic regions.

Findings: Here, we provide an unprecedented collection of 26,481 honey bee wing images representing 1,725 samples from 13 European countries. The wing images are accompanied by the coordinates of 19 landmarks and the geographic coordinates of the sampling locations. We present an R script that describes the workflow for analyzing the data and identifying an unknown sample. We compared the data with available reference samples for lineage and found general agreement with them.

Conclusions: The extensive collection of wing images available on the Zenodo website can be used to identify the geographic origin of unknown samples and therefore assist in the monitoring and conservation of honey bee biodiversity in Europe.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
蜜蜂(Apis mellifera)翅膀图像:一种识别和保护工具。
背景:蜜蜂(Apis mellifera)是一种具有重要生态和经济价值的物种,为自然和农业系统提供授粉服务。蜜蜂在其原生地的部分地区的生物多样性因养蜂业的迁徙和商业繁殖而受到威胁。因此,一些适应当地环境的蜜蜂种群正面临灭绝的威胁。保护蜜蜂生物多样性的一个关键步骤是可靠地区分本地蜜蜂和非本地蜜蜂。翅膀几何形态计量学是其中一种可用的方法。这种方法速度快、成本低,而且不需要昂贵的设备。因此,科学家和养蜂人都可以轻松使用。然而,由于缺乏可用于不同地理区域比较的可靠参考数据,翅膀几何形态计量学具有挑战性:在此,我们前所未有地收集了 26,481 张蜜蜂翅膀图像,代表了来自 13 个欧洲国家的 1,725 个样本。这些翅膀图像附有 19 个地标的坐标和采样地点的地理坐标。我们介绍了一个 R 脚本,其中描述了分析数据和识别未知样本的工作流程。我们将数据与现有的参考样本进行了比较,发现两者基本一致:Zenodo网站上广泛收集的翅膀图像可用来识别未知样本的地理来源,从而帮助监测和保护欧洲的蜜蜂生物多样性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
期刊最新文献
A multi-omics data analysis workflow packaged as a FAIR Digital Object Evolutionary genomics of three agricultural pest moths reveals rapid evolution of host adaptation and immune-related genes. A high-quality chromosomal genome assembly of the sea cucumber Chiridota heheva and its hydrothermal adaptation. Vulture: cloud-enabled scalable mining of microbial reads in public scRNA-seq data. A graph clustering algorithm for detection and genotyping of structural variants from long reads.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1