In the face of global pollinator declines, plant-pollinator interaction networks have been studied to guide ecological conservation and restoration. In order to obtain more comprehensive and unbiased knowledge of these networks, perspectives of both plants and pollinators need to be considered integratively. Metabarcoding has seen increasing applications in characterizing pollen transported by pollinators. However, amplification bias across taxa could lead to unpredictable artefacts in pollen compositions. We examined the efficacy of a PCR-free genome-skimming method in quantifying mixed pollen, using mock samples constructed with known pollen species (5 mocks of flower pollen and 14 mocks of bee pollen). The results demonstrated a high level of repeatability and accuracy in identifying pollen from mixtures of varied species ratios. All pollen species were detected in all mock samples, and pollen frequencies estimated from the number of sequence reads of each species were significantly correlated with pollen count proportions (linear model, R2 =86.7%, P = 2.2e-16). For >97% of the mixed taxa, pollen proportion could be quantified by sequencing to the correct order of magnitude, even for species which constituted only 0.2% of the total pollen. We also showed that DNA extracted from pollen grains equivalent to those collected from a single honeybee corbicula was sufficient for the genome-skimming pipeline. We conclude that genome-skimming is a feasible approach to identifying and quantifying pollen compositions for mixed pollen samples. By providing reliable and sensitive taxon identification and relative abundance, this method is expected to improve the understanding of pollen diversity transported by pollinators and their ecological roles in the plant-pollinator networks.
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