A standardized and statistically defensible framework for quantitative morphological analyses in taxonomic studies

Area of Science:

• Biology
• Zoology
• Morphometrics

Background:

• Body size correction and inferential statistics are crucial for robust morphological studies but are not widely applied.
• Many taxonomic papers rely on descriptive statistics, overlooking advanced analytical methods.

Purpose of the Study:

• To quantify the use of body size correction and statistical hypothesis testing in taxonomic morphology research.
• To propose a standardized, statistically sound 5-step framework for morphological data analysis in taxonomy.
• To provide an R script to facilitate the implementation of the proposed workflow.

Main Methods:

• A meta-analysis was conducted on taxonomic papers to assess the application of body size correction and statistical hypothesis testing.
• A 5-step analytical framework was developed for meristic and mensural data.
• A companion R script was created to support the framework's implementation.

Main Results:

• The meta-analysis revealed limited application of body size correction and inferential statistics in morphological studies.
• Descriptive statistics comparisons were more common than rigorous statistical analyses.
• The proposed framework offers a standardized approach for morphological data analysis.

Conclusions:

• A statistically defensible framework is needed to enhance morphological analyses in taxonomic studies.
• The proposed 5-step approach, with its R script, can improve the identification of diagnostic characters for species diagnoses.
• This framework can be integrated into broader integrative taxonomic approaches.