Advances in Biogeographic Analysis of Genetic Data Using Database-Driven Metrics: A Case Study on the Amazon Basin

Bedoya K.A.; Ramirez J.L. · Journal of Biogeography (2025) · DOI: 10.1111/jbi.70068

Resumen

Aim: The aim of this study is to develop and apply novel metrics for analysing genetic diversity and biogeographic patterns across river basins using data mining techniques on large genetic datasets. The study focuses on Amazon Basin freshwater fishes. Location: Amazon Basin, South America. Taxon: Actinopterygii class. Methods: Through data mining, we collected Actinopterygii sequences from the BOLD Systems BIN database and assigned them to specific sub-basins using GIS tools and extensive bibliographic searches. Genetic distances within BINs were calculated using Kimura's two-parameter model (K2P). Three novel metrics were developed: (1) the sub-basin homogeneity index (H) to quantify genetic divergence within sub-basins, (2) the sub-basin similarity index (S) to assess genetic similarity across sub-basins and (3) the regional homogeneity index (HR) to identify potential biogeographic boundaries. Results: We collected 645 BINs with 3373 sequences, overcoming data gaps through extensive bibliographic research. The H index showed similar sub-basin genetic homogeneity among sub-basins but highlighted genetic structuring within them, notably in eastern sub-basins (Tapajos, Xingu and Tocantins). The S index identified two biogeographic clusters: an eastern cluster (Xingu and Tocantins) genetically distinct from other sub-basins. The HR index supported a potential east–west boundary within the Amazon Basin but indicated challenges in defining it precisely. Main Conclusions: Historical geological events, hydrological connectivity and ecological factors have profoundly influenced the genetic diversity and biogeography of Amazonian freshwater fishes. The novel metrics developed effectively leveraged large genetic databases, offering insights into genetic structuring and relationships within the Amazon Basin that are consistent with previous literature, thus indicating their potential applicability to other regions and taxa. Our approach demonstrates the potential of integrating large genetic datasets with new analytical methods to advance biogeographic studies; nevertheless, it also emphasizes the importance of strengthening research efforts, particularly through the expansion and enrichment of publicly available genetic databases.

Instituciones: Universidad Nacional Mayor de San Marcos