Constructing pollination interaction networks based on pollen carriage data can identify more interactions in the network, leading to higher connectance and fewer specialist plants than those networks built from plant visitation data. While network characterization with pollen carriage clearly has advantages, it has not been widely used, partly because the identification of pollen via visual microscopy has several drawbacks. It requires highly specialized expertise, is time-consuming, and is often of low taxonomic resolution. To study pollination networks in a changing environment, we need accurate, high-throughput methods. DNA metabarcoding potentially allows for faster and finer-scale taxonomic resolution of pollen, but has not been applied to pollination networks. We sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures, and sequenced rbcL and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive rbcL and ITS2 databases.
With ITS2, most identifications were to the level of species (40% of taxa) or genus (38% of taxa), whereas with rbcL, only 55% of taxa were identified to genus or higher, with many more family-level identifications. We successfully built a proof-of-concept quantitative pollination network using pollen metabarcoding. From our Illumina sequence reads, we obtained a bipartite network with 38 pollinator and 51 plant taxa based on ITS2 taxonomic classification. Our work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages over microscopic identification of pollen, in terms of taxonomic resolution and consistency of turnaround time, and we expect that it will have broad utility for future studies of plant-pollinator interactions. While still in its infancy, DNA metabarcoding technology can be used today for construction of pollination networks and particularly with potential methodological refinements holds tremendous promise for transforming their empirical study in the future.