ITS2 Metabarcoding and Open Data Approaches in Honey Bee Pollen Research

Abstract

Honey bees (Apis mellifera) are among the most important pollinating agents supporting the sustainability of terrestrial ecosystems and agricultural production. Determining the botanical origin of pollen collected by honey bees provides critical information for melissopalynology, pollination biology, colony development, and ecological monitoring studies. Conventional microscopic pollen identification methods have been widely used for many years; however, due to their time-consuming nature and the requirement for advanced expertise in pollen taxonomy, molecular-based identification methods have increasingly been preferred in recent years. In DNA metabarcoding studies, the Internal Transcribed Spacer 2 (ITS2) region has emerged as a powerful molecular marker effectively used for discriminating plant taxa. Large amounts of ITS2 sequence data have accumulated in open-access molecular databases such as NCBI GenBank. Nevertheless, the potential for re-evaluating these open-access datasets in honey bee pollen research and melissopalynological studies has not yet received sufficient attention. Re-analysis of open-access molecular datasets has the potential not only to generate new biological interpretations but also to contribute to reproducible and sustainable scientific research through the re-evaluation of existing data. In this context, interdisciplinary analysis pipelines integrating open-source analytical software workflows, BLAST-based taxonomic matching systems, metabarcoding analysis tools, and network analysis approaches are widely used. This presentation aims to highlight the importance of integrating ITS2 metabarcoding with open-data approaches in honey bee pollen research. For this purpose, a bioinformatic re-evaluation based on the re-analysis of ITS2 datasets obtained from publicly available research data is presented as an example, demonstrating the potential of such approaches for advancing melissopalynological studies through the testing of different hypotheses. The proposed approach is expected to contribute to the molecular identification of bee flora, the evaluation of pollen resources in urban landscapes, and the testing of new hypotheses in pollination research.