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

Area of Science:

• Ecology, Evolution, and Animal Behaviour
• Phenotypic Variation
• Animal and Plant Colouration

Background:

• Colour patterns are a key aspect of phenotypic variation, driving advances in ecology, evolution, and animal behaviour.
• Quantitative Colour Pattern Analysis (QCPA) analyzes colour patterns from non-human perspectives but is often time-consuming due to extensive manual image processing.
• The time-intensive nature of QCPA hinders its application to large datasets and full utilization of its analytical capabilities.

Purpose of the Study:

• To introduce a robust batch script for automating QCPA workflows.
• To provide complementary R scripts for downstream data extraction and analysis.
• To empower researchers to utilize QCPA's full analytical power for large-scale studies.

Main Methods:

• Development of a comprehensive batch script to automate common QCPA image processing and analysis tasks.
• Provision of associated R scripts for efficient data extraction and subsequent analysis.
• Implementation of semi-automated workflows for analyzing organismal colouration.

Main Results:

• A batch processing extension for QCPA has been developed.
• The script automates numerous QCPA workflows, significantly reducing analysis time.
• Complementary R scripts facilitate downstream data extraction and analysis.

Conclusions:

• The batch script empowers researchers to apply QCPA to larger datasets, accelerating research in animal colouration.
• Automated workflows facilitate the exploration of colour pattern spaces and the development of advanced analytical frameworks.
• These advancements enable quantitative testing of hypotheses on the evolution and function of visual signals in non-human animals.