Optimizing digitalization effort in morphometrics.

Area of Science:

• Morphometrics
• Biological Data Analysis
• Quantitative Morphology

Background:

• Phenotypic quantification is crucial for studying morphological variation.
• Data acquisition time varies significantly based on methods and information needs.
• Optimizing digitization involves pooling data, automation, or reducing measurements, but pooling carries risks of operator error.

Purpose of the Study:

• To present an analytical workflow for estimating within- and among-operator biases.
• To assess the feasibility of pooling morphometric datasets.
• To optimize digitization efforts and manage errors in morphological studies.

Main Methods:

• Developed an analytical workflow to quantify operator biases.
• Applied the workflow to a worked example using Sus scrofa teeth with 18 morphometric approaches.
• Identified and quantified potential error sources in the data acquisition workflow.

Main Results:

• Pooling and sharing morphometric data necessitates careful error examination.
• The chosen morphometric approach significantly influences the amount of error.
• It is possible to estimate study analytical power using small data subsets to select optimal protocols and reduce variables, especially concerning semi-landmarks.

Conclusions:

• Pooling morphometric data should be avoided in certain cases due to potential errors.
• The workflow aids in optimizing digitization efforts and provides best practices for error management.
• Selecting appropriate morphometric protocols and managing semi-landmark data inflation is key for robust analysis.