GroupStruct2: ユーザーフレンドリーな統計および視覚サポートのためのグラフィカルユーザーインターフェイス、種診断用

Statistically defensible species diagnoses are essential for producing robust taxonomies, which underpin much of biological research. Yet, most species descriptions remain largely descriptive, often lack rigorous statistical validation, and suffer from confounding factors stemming from sampling bias, geographic variation, and intraspecific diversity. These limitations are further compounded by the steep learning curve of advanced statistical and data visualization tools, which poses a major barrier for early-career scientists and researchers in under-resourced regions who may lack programming experience or access to expert support. To address these challenges, we developed GroupStruct2, a powerful yet accessible R-based Shiny application that democratizes robust statistical analysis and data visualization for species diagnosis across any taxonomic group. GroupStruct2 is implemented through a user-friendly graphical user interface (GUI) that requires no coding experience. The user-friendly interface intuitively guides users through a comprehensive workflow, from raw data upload and outlier detection to assumption testing, adaptive statistical analyses, and the generation of highly customizable, publication-ready visualizations based on the ggplot2 architecture. It supports allometric body-size correction and widely used dimension-reduction techniques, including Principal Component Analysis (PCA), Discriminant Analysis of Principal Components (DAPC), and, critically, Multiple Factor Analysis (MFA), which enables the joint analysis of meristic, morphometric, and categorical trait data within a single integrative taxonomic framework. We showcase GroupStruct2's capabilities using two empirical datasets, demonstrating how to conduct robust statistical analyses and produce publication-quality visualizations in just a few clicks. By lowering technical barriers without compromising analytical rigor, GroupStruct2 empowers researchers of all backgrounds working on any taxonomic group to conduct statistically sound and produce visually compelling species diagnoses, thereby advancing both the accessibility and the scientific rigor of taxonomy, species delimitation, and biodiversity research.