A DNA Barcoding Approach to Characterize Pollen Collected by Honeybees

Abstract

In the present study, we investigated DNA barcoding effectiveness to characterize honeybee pollen pellets, a food supplement largely used for human nutrition due to its therapeutic properties. We collected pollen pellets using modified beehives placed in three zones within an alpine protected area (Grigna Settentrionale Regional Park, Italy). A DNA barcoding reference database, including rbcL and trnH-psbA sequences from 693 plant species (104 sequenced in this study) was assembled. The database was used to identify pollen collected from the hives. Fifty-two plant species were identified at the molecular level. Results suggested rbcL alone could not distinguish among congeneric plants; however, psbA-trnH identified most of the pollen samples at the species level. Substantial variability in pollen composition was observed between the highest elevation locality (Alpe Moconodeno), characterized by arid grasslands and a rocky substrate, and the other two sites (Cornisella and Ortanella) at lower altitudes. Pollen from Ortanella and Cornisella showed the presence of typical deciduous forest species; however in samples collected at Ortanella, pollen of the invasive Lonicera japonica, and the ornamental Pelargonium x hortorum were observed. Our results indicated pollen composition was largely influenced by floristic local biodiversity, plant phenology, and the presence of alien flowering species. Therefore, pollen molecular characterization based on DNA barcoding might serve useful to beekeepers in obtaining honeybee products with specific nutritional or therapeutic characteristics desired by food market demands.

Flower preferences and pollen transport networks for cavity‐nesting solitary bees: Implications for the design of agri‐environment schemes

“…While plant identification from DNA has presented numerous challenges (CBOL 2009), it has the advantage of providing finer definition of species‐specific foraging preferences for plant families whose pollen is difficult to distinguish morphologically (Bell et al., 2016; Bruni et al., 2015; Hawkins et al., 2015; Kraaijeveld et al., 2015; Pornon, Andalo, Burrus, & Escaravage, 2017). DNA sequencing has been used successfully to identify the foraging preference of honeybees and the floral composition of their honey (Bruni et al., 2015; De Vere et al., 2017; Galimberti et al., 2014; Hawkins et al., 2015; Jain, Jesus, Marchioro, & Araújo, 2013; Valentini, Miquel, & Taberlet, 2010). Several studies have also used pollen DNA sequencing to identify solitary bee foraging preferences (Sickel et al., 2015; Wilson, Sidhu, Levan, & Holway, 2010).…”

High-throughput sequencing of African chikanda cake highlights conservation challenges in orchids

“…So far the only plant studies to use nrITS in metabarcoding have used nrITS2 to determine the taxonomic composition of pollen collected by honey bees (Galimberti et al 2014;Richardson et al 2015) and species composition in herbal medicines (Cheng et al 2014;Ivanova et al 2016).…”

Ecosystem services mapping for municipal policy: ESTIMAP and zoning for urban beekeeping

“…Yet vehicle exhaust can disrupt bees' ability to detect floral odours (Girling et al 2013), pollination rates can decrease as traffic speeds increase (Dargas et al 2016) and collisions with vehicles may lead to increased bee mortality (Kallioniemi et al 2017). We therefore attempted to capture the detrimental effects that greater levels of automotive traffic could have on pollinator foraging by generating a value-reduction layer based on cells' proximity to aboveground, high-traffic roads (defined as Motorways, Freeways and Major roads in the Teleatlas® Multinet™ Dataset, 2013).…”

“…For the scope of the present study, however, we sought a coarse estimation of potential honeybee foraging pressure over an entire growing season, using a simple diffusion model that assumes a homogeneous distribution of floral resources throughout the landscape. and Garbuzov et al (2015b) used waggle dance analyses (von Frisch 1967) to generate probability maps of bee foraging patterns in Brighton, UK: an urban landscape with a population density similar to that of Oslo (3 445 ind · km ). They then used the distribution of points on these maps to model the probability of a site's visitation as a function of the site's distance from the hive.…”

“…Olsson et al (2015) argue that this is because the Lonsdorf model does not include a behavioural mechanism to simulate how bees forage selectively in areas with higher resources. Empirical studies of bee foraging patterns demonstrate that bee densities depend on both local habitat quality and the quality of the surrounding habitat (SteffanDewenter et al 2002, Carvell et al 2011, Woodcock et al 2014). Yet the effects surrounding habitat can have on the importance of a given patch are complex.…”