A standardized and statistically defensible framework for quantitative morphological analyses in taxonomic studies - Chan, Kin Onn; Grismer, L. Lee (Zootaxa, 2021)
Although body size correction and inferential statistics have been used in morphological studies for many decades, their applications are far from being ubiquitous. We performed a meta-analysis to quantify the extent of taxonomic papers that performed body size correction and implemented a statistical hypothesis testing framework during the analysis of morphological data. Our results indicate that in most papers, neither of these analyses were performed but instead, cursory comparisons of descriptive statistics were presented. With the development of numerous freely available and powerful statistical programs such as R, we find it prudent to outline a standardized and statistically defensible framework to enhance the workflow of morphological analyses in taxonomic studies. This 5-step approach can be applied to meristic and mensural data across a wide range of taxonomic groups. We include an easy-to-use companion R script to facilitate the implementation of this workflow. Our proposed framework is not rooted in phylogenetic or evolutionary theory and hence, should not be used in place of explicit species delimitation techniques. Nevertheless, it can be incorporated into a more robust integrative taxonomic framework and is particularly useful for identifying diagnostic characters for species diagnoses.