Native bees are essential in healthy ecosystems. With declines in domestic bee hives, native bees may increasingly need to compensate for lost pollination services. Efforts have been made to implement bee-friendly land-use practices to support native bee communities. One such strategy is the inclusion of flowering plant species that provide forage into landscaping or agricultural settings. However, little is known about the regional foraging preferences of native bees. A comprehensive understanding of seasonal pairings between local plant and bee species would allow selective plantings to promote the regional native bee community, or even target those that pollinate specific crops. The goal of the present work is to develop a method for identifying genera-specific native bee foraging habits using a three-loci, single PCR, DNA-based identification of pollen grains taken from sampled native bees. Total pollen load from a single bee was collected and placed in an aqueous suspension to facilitate random sampling of pollen grains. Using aniline stain to determine viability, isolated pollen grains were transferred to a PCR tube containing three sets of primers, Phire™ Plant Direct PCR Master Mix, and deionized water in a 25-µL reaction. Amplification was carried out at an annealing temperature of 60°C for 35 cycles.
Results/Conclusions
Preliminary PCR experiments demonstrated that three loci could be amplified directly from an individual pollen grain: the internal transcribed spacer (ITS) region in the pollen nuclear DNA, the intergenic spacer between trnH and psbA in the plastid DNA, and the plastid rbcL gene. All three regions showed bands on agarose gel after a multiplex PCR reaction, at an annealing temperature of 60°C. The trnH-psbA and ITS regions were less than 500 bp, while rbcL was around 1.5 kb. Amplification of two or more regions of individual pollen grain DNA in a single PCR reaction can generate multiple amplicons for sequencing from a single source. The use of three loci in plant identification by DNA barcoding helps to improve the discriminatory power of these conserved regions when identifying the species of pollen within the GenBank® database.