Genome-skimming provides accurate quantification for pollen mixtures.

Area of Science:

• Ecology
• Molecular Biology
• Conservation Biology

Background:

• Pollinator foraging partitioning is key to understanding food webs and conservation.
• Metabarcoding is used for pollen analysis but can suffer from PCR amplification bias.
• Accurate pollen quantification is crucial for ecological and conservation studies.

Purpose of the Study:

• To evaluate genome-skimming via direct shotgun sequencing for quantifying mixed pollen samples.
• To assess the accuracy and reliability of genome-skimming compared to traditional methods.
• To determine the sensitivity of genome-skimming for detecting low-proportion pollen.

Main Methods:

• Utilized mock samples of mixed flower and bee pollen (5 and 14 types, respectively).
• Employed direct shotgun sequencing (genome-skimming) without PCR amplification.
• Analyzed sequence read counts to estimate pollen frequencies and compared them to known proportions.

Main Results:

• High repeatability and accuracy in identifying pollen from mixtures with varied species ratios.
• All pollen species were detected, with estimated frequencies strongly correlated to actual proportions (R² = 86.7%, p < 2.2e-16).
• Quantified pollen proportions to the correct order of magnitude for >97% of taxa, including those at 0.2% abundance; minimal DNA from single bee loads was sufficient.

Conclusions:

• Genome-skimming is a feasible and effective method for identifying and quantifying mixed pollen.
• This technique provides reliable taxon identification and relative abundance, improving plant-pollinator interaction research.
• Applications include pollen preference, diet analysis, and landscape resource use studies in pollinators like bees.