Taxonomic identification using multivariate morphometric statistics in Panamanian Carollia bats (Chiroptera: Phyllostomidae) - Morales , María; Araúz , Jacobo; Abrego , Jeancarlos; Guevara , Nelson; Trujillo , Julio; Bruner-Montero , Gaspar; Romero, Emilio Eduardo; Añino , Yostin (Revista de Biología Tropical, 2024)
Introduction: Carollia is characterized by the challenge of identifying individuals of different species. In Panama, no investigation has been conducted on this genus’s accurate classification and identification. However, molecular and phylogenetic studies have been conducted in other regions of the Americas, demonstrating the difficulty of morphological differentiation. The taxonomic keys for identifying this genus tend to vary, making it difficult in Panamanian localities. Objective: To evaluate the external morphometric and morphological characteristics of Carollia specimens using multivariate statistical techniques to facilitate species identification. Methods: We used previous data matrices, which were updated in the field from October 2022 to January 2023 using mist nets. External morphometric measurements (tail, forearm, hand wing, tibia, calcaneus, tragus, hair colour, and body size) and individual characteristics were recorded. 263 specimens of the four species reported for Panama were recorded. We used univariate statistics to compare each of these characteristics between species. Multivariate analyses, such as principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and partial least square discriminant analysis (PLS-DA) were then performed to identify the species based on external morphological and morphometric characteristics. Decision trees were also used for species classification. Results: Linear discriminant analysis (LDA) and decision trees proved to be the best option for classifying the species with up to 99 % efficiency. The most relevant characters for such classifications are the length of the tail and forearm. Conclusion: Morphometric characteristics alone do not provide adequate species discrimination. However, by analysing the parameters using multivariate models, the accuracy of the discriminatory capacity is significantly improved.