Automated workflows using Quantitative Colour Pattern Analysis (QCPA): a guide to batch processing and downstream data analysis.

Autor: van den Berg CP; School of the Environment, The University of Queensland, Brisbane, QLD 4072 Australia.; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2050 Australia.; School of Biological Sciences, University of Bristol, Bristol, BS8 1TQ UK., Condon ND; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072 Australia., Conradsen C; School of the Environment, The University of Queensland, Brisbane, QLD 4072 Australia., White TE; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2050 Australia., Cheney KL; School of the Environment, The University of Queensland, Brisbane, QLD 4072 Australia.
Jazyk: angličtina
Zdroj: Evolutionary ecology [Evol Ecol] 2024; Vol. 38 (3), pp. 387-397. Date of Electronic Publication: 2024 Mar 16.
DOI: 10.1007/s10682-024-10291-7
Abstrakt: 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.
Supplementary Information: The online version contains supplementary material available at 10.1007/s10682-024-10291-7.
Competing Interests: Conflict of interestThe authors declare no conflict of interest.
(© The Author(s) 2024.)
Databáze: MEDLINE
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