使用色彩模式定量分析 (QCPA) 的自动化工作流程:批处理和下游数据分析指南

IF 1.8 3区 环境科学与生态学 Q3 ECOLOGY
Cedric P. van den Berg, Nicholas D. Condon, Cara Conradsen, Thomas E. White, Karen L. Cheney
{"title":"使用色彩模式定量分析 (QCPA) 的自动化工作流程:批处理和下游数据分析指南","authors":"Cedric P. van den Berg, Nicholas D. Condon, Cara Conradsen, Thomas E. White, Karen L. Cheney","doi":"10.1007/s10682-024-10291-7","DOIUrl":null,"url":null,"abstract":"<p>Animal and plant colouration presents a striking dimension of phenotypic variation, the study of which has driven general advances in ecology, evolution, and animal behaviour. Quantitative Colour Pattern Analysis (QCPA) is a dynamic framework for analysing colour patterns through the eyes of non-human observers. However, its extensive array of user-defined image processing and analysis tools means image analysis is often time-consuming. This hinders the full use of analytical power provided by QCPA and its application to large datasets. Here, we offer a robust and comprehensive batch script, allowing users to automate many QCPA workflows. We also provide a complimentary set of useful R scripts for downstream data extraction and analysis. The presented batch processing extension will empower users to further utilise the analytical power of QCPA and facilitate the development of customised semi-automated workflows. Such quantitatively scaled workflows are crucial for exploring colour pattern spaces and developing ever-richer frameworks for analysing organismal colouration accounting for visual perception in animals other than humans. These advances will, in turn, facilitate testing hypotheses on the function and evolution of vision and signals at quantitative and qualitative scales, which are otherwise computationally unfeasible.</p>","PeriodicalId":55158,"journal":{"name":"Evolutionary Ecology","volume":"49 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated workflows using Quantitative Colour Pattern Analysis (QCPA): a guide to batch processing and downstream data analysis\",\"authors\":\"Cedric P. van den Berg, Nicholas D. Condon, Cara Conradsen, Thomas E. White, Karen L. Cheney\",\"doi\":\"10.1007/s10682-024-10291-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Animal and plant colouration presents a striking dimension of phenotypic variation, the study of which has driven general advances in ecology, evolution, and animal behaviour. Quantitative Colour Pattern Analysis (QCPA) is a dynamic framework for analysing colour patterns through the eyes of non-human observers. However, its extensive array of user-defined image processing and analysis tools means image analysis is often time-consuming. This hinders the full use of analytical power provided by QCPA and its application to large datasets. Here, we offer a robust and comprehensive batch script, allowing users to automate many QCPA workflows. We also provide a complimentary set of useful R scripts for downstream data extraction and analysis. The presented batch processing extension will empower users to further utilise the analytical power of QCPA and facilitate the development of customised semi-automated workflows. Such quantitatively scaled workflows are crucial for exploring colour pattern spaces and developing ever-richer frameworks for analysing organismal colouration accounting for visual perception in animals other than humans. These advances will, in turn, facilitate testing hypotheses on the function and evolution of vision and signals at quantitative and qualitative scales, which are otherwise computationally unfeasible.</p>\",\"PeriodicalId\":55158,\"journal\":{\"name\":\"Evolutionary Ecology\",\"volume\":\"49 1\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Evolutionary Ecology\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1007/s10682-024-10291-7\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evolutionary Ecology","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s10682-024-10291-7","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

动物和植物的颜色是表型变异的一个显著方面,对其研究推动了生态学、进化论和动物行为学的普遍进步。定量色彩模式分析(QCPA)是通过非人类观察者的眼睛分析色彩模式的动态框架。然而,该系统拥有大量用户定义的图像处理和分析工具,这意味着图像分析往往非常耗时。这阻碍了 QCPA 分析能力的充分利用及其在大型数据集上的应用。在这里,我们提供了一个强大而全面的批处理脚本,允许用户自动执行许多 QCPA 工作流程。我们还为下游数据提取和分析提供了一套有用的 R 脚本。所介绍的批处理扩展将使用户能够进一步利用 QCPA 的分析能力,并促进定制化半自动工作流程的开发。这种定量缩放工作流程对于探索色彩模式空间和开发更丰富的生物色彩分析框架至关重要,这些分析框架考虑到了人类以外动物的视觉感知。反过来,这些进展将有助于在定量和定性尺度上检验视觉和信号的功能和进化假设,否则这些假设在计算上是不可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated workflows using Quantitative Colour Pattern Analysis (QCPA): a guide to batch processing and downstream data analysis

Animal and plant colouration presents a striking dimension of phenotypic variation, the study of which has driven general advances in ecology, evolution, and animal behaviour. Quantitative Colour Pattern Analysis (QCPA) is a dynamic framework for analysing colour patterns through the eyes of non-human observers. However, its extensive array of user-defined image processing and analysis tools means image analysis is often time-consuming. This hinders the full use of analytical power provided by QCPA and its application to large datasets. Here, we offer a robust and comprehensive batch script, allowing users to automate many QCPA workflows. We also provide a complimentary set of useful R scripts for downstream data extraction and analysis. The presented batch processing extension will empower users to further utilise the analytical power of QCPA and facilitate the development of customised semi-automated workflows. Such quantitatively scaled workflows are crucial for exploring colour pattern spaces and developing ever-richer frameworks for analysing organismal colouration accounting for visual perception in animals other than humans. These advances will, in turn, facilitate testing hypotheses on the function and evolution of vision and signals at quantitative and qualitative scales, which are otherwise computationally unfeasible.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Evolutionary Ecology
Evolutionary Ecology 环境科学-进化生物学
CiteScore
3.00
自引率
5.30%
发文量
70
审稿时长
3 months
期刊介绍: Evolutionary Ecology is a concept-oriented journal of biological research at the interface of ecology and evolution. We publish papers that therefore integrate both fields of research: research that seeks to explain the ecology of organisms in the context of evolution, or patterns of evolution as explained by ecological processes. The journal publishes original research and discussion concerning the evolutionary ecology of organisms. These may include papers addressing evolutionary aspects of population ecology, organismal interactions and coevolution, behaviour, life histories, communication, morphology, host-parasite interactions and disease ecology, as well as ecological aspects of genetic processes. The objective is to promote the conceptual, theoretical and empirical development of ecology and evolutionary biology; the scope extends to any organism or system. In additional to Original Research articles, we publish Review articles that survey recent developments in the field of evolutionary ecology; Ideas & Perspectives articles which present new points of view and novel hypotheses; and Comments on articles recently published in Evolutionary Ecology or elsewhere. We also welcome New Tests of Existing Ideas - testing well-established hypotheses but with broader data or more methodologically rigorous approaches; - and shorter Natural History Notes, which aim to present new observations of organismal biology in the wild that may provide inspiration for future research. As of 2018, we now also invite Methods papers, to present or review new theoretical, practical or analytical methods used in evolutionary ecology. Students & Early Career Researchers: We particularly encourage, and offer incentives for, submission of Reviews, Ideas & Perspectives, and Methods papers by students and early-career researchers (defined as being within one year of award of a PhD degree) – see Students & Early Career Researchers
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信