An insight into molecular identification and DNA barcoding of bamboo: current status, challenges, and future prospects

Introduction Bamboo exhibits a complex taxonomy due to polyploidy, hybridisation, and long flowering periods, which complicate species identification. Traditional morphological taxonomy often struggles with limited diagnostic traits and considerable phenotypic variability, leading to taxonomic ambiguities and misinterpretations. The convergence of similar traits among unrelated species further complicates phylogenetic classification. To address these challenges, integrative taxonomic approaches are needed to combine morphological, molecular, and ecological data for accurate bamboo classification. This review synthesises the current status, recent advances, and emerging trends in molecular identification and DNA barcoding of bamboo, highlighting their potential to improve taxonomy, conservation, and the sustainable utilisation of bamboo genetic resources. Method To provide a framework for the review, a scientometric analysis was performed using bibliographic records. Results The review suggests that no single marker or combination of markers can currently serve as a universal barcode across bamboo. The performance of commonly used markers such as ITS, matK, rbcL, psbA-trnH, rps16-trnQ, and alternative plastid regions provides valuable insights into species identification. Comparative evidence indicates that barcode performance is strongly marker- and lineage-dependent. These markers have improved plant taxonomy by providing more reliable species identification than traditional morphological methods. Discussion DNA barcoding has been effectively used to understand complex species relationships and identify cryptic species that are difficult to distinguish morphologically. Furthermore, next-generation sequencing (NGS) technologies have transformed genomic data acquisition, enabling species discrimination. Integrating machine learning with DNA barcoding further improves species identification accuracy and shows promise for improving conventional methodologies in bamboo systematics. However, the application of ML and NGS in bamboo is still in its early stages and will require substantial work on bamboo databases to achieve greater accuracy.