py_tps2tnt and py_tm2tnt:Two Python programs for processing morphometric data for cladistic analyses with TNT

• Autores: Liria, Jonathan, Soto-Vivas, Ana
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad Nacional Mayor de San Marcos
• Repositorio: Revistas - Universidad Nacional Mayor de San Marcos
• Lenguaje: español
• OAI Identifier: oai:revistasinvestigacion.unmsm.edu.pe:article/30018
• Enlace del recurso: https://revistasinvestigacion.unmsm.edu.pe/index.php/rpb/article/view/30018
• Nivel de acceso: acceso abierto
• Materia: Geometric morphometrics; continuous characters; GPA; Euclidean distance; phylogeny; Morfometría geométrica; caracteres continuos; AGP; distancia Euclidiana; filogenia

The incorporation of morphometric data into cladistic analyses poses methodological challenges, particularly in handling continuous characters and landmark configurations in matrices that include multiple individuals per taxon. Although the program TNT (Tree analysis using New Technology) has integrated functionalities for directly analysing continuous and morphogeometric characters, there remains a need for complementary tools that facilitate data preparation and processing. In this context, we present two open-source applications developed in Python: py_tps2tnt and py_tm2tnt. The former transforms TPS files containing multiple individuals per taxon, performs Procrustes alignments, calculates mean configurations, computes centroid size ranges, allows the selection of inter-landmark distances, and exports various types of data into TNT-compatible formats. The latter, py_tm2tnt, focuses on traditional morphometric data, enabling the calculation of intervals, univariate statistical analyses, and the export of continuous character matrices for parsimony analysis. Both applications feature an intuitive graphical user interface, flexible data management, and direct compatibility with TNT, facilitating their integration into phylogenetic workflows. This study describes the development, core functionalities, and future perspectives of both tools. In addition, information is provided on the complementary materials (tutorial videos and real and hypothetical data sets) that can be used for its evaluation and practical application.