Identities, concentrations, and sources of pesticide exposure in pollen collected by managed bees during blueberry pollination

Bees are critical for crop pollination, but there is limited information on levels and sources of pesticide exposure in commercial agriculture. We collected pollen from foraging honey bees and bumble bees returning to colonies placed in blooming blueberry fields with different management approaches (conventional, organic, unmanaged) and located across different landscape settings to determine how these factors affect pesticide exposure. We also identified the pollen and analyzed whether pesticide exposure was correlated with corbicular load composition. Across 188 samples collected in 2 years, we detected 80 of the 259 pesticide active ingredients (AIs) screened for using a modified QuEChERS method. Detections included 28 fungicides, 26 insecticides, and 21 herbicides. All samples contained pesticides (mean = 22 AIs per pollen sample), with pollen collected from bees on conventional fields having significantly higher average concentrations (2019 mean = 882.0 ppb) than those on unmanaged fields (2019 mean = 279.6 ppb). Pollen collected by honey bees had more AIs than pollen collected by bumble bees (mean = 35 vs. 19 AIs detected at each farm, respectively), whereas samples from bumble bees had higher average concentrations, likely reflecting differences in foraging behavior. Blueberry pollen was more common in pollen samples collected by bumble bees (25.9% per sample) than honey bees (1.8%), though pesticide concentrations were only correlated with blueberry pollen for honey bees. Pollen collected at farms with more blueberry in the surrounding landscape had higher pesticide concentrations, mostly AIs applied for control of blueberry pathogens and pests during bloom. However, for honey bees, the majority of AIs detected at each farm are not registered for use on blueberry at any time (55.2% of AIs detected), including several highly toxic insecticides. These AIs therefore came from outside the fields and farms they are expected to pollinate. For bumble bees, the majority of AIs detected in their pollen are registered for use on blueberry during bloom (56.9% of AIs detected), though far fewer AIs were sprayed at the focal farm (16.7%). Our results highlight the need for integrated farm and landscape-scale stewardship of pesticides to reduce exposure to pollinators during crop pollination.

Pollen morphological ultrastructure of Melastoma L. (Melastomataceae)

Melastoma L. is a type genus of Melastomataceae. Melastoma malabthricum is the type with the widest area of spread compared with other species in the genus Melastoma. This research aim to study pollen morphological ultrastructure of Melastoma. Research carried out with collecting species which include to Melastoma with survey methode. Pollen morphological ultrastructure observation used Scanning Electron Microscope Type JSM-IT-200. Pollen sample obtained from the flowers collection where not yet anthesis. In Laboratory polen was fixation, dehidration and coating then observed with electrone microscope to taken pollen photo and identification. Two species are prolate spheroidal that are M. malabathricum dan M. tebauchina. Four species are oblate spheroidal that are M. setigerum, M. baccarianum, M.minahasae dan M. malabathricum var. malabathricum. The results scanning electron microscope from six species Melastoma seen only have aperture like a gap called colpus with amount variation. Pollen ornamentation on six species Melastoma looks like striate type.

Quantitative Amplicon Sequencing for Meta-DNA Analysis Reveals Patterns in Pollen Use by Bees

ABSTRACTAn underdeveloped but potentially valuable molecular method in ecology is the ability to quantify the frequency with which foraging pollinators carry different plant pollens. Thus far, DNA metabarcoding has only reliably identified the presence/absence of a plant species in a pollen sample, but not its relative abundance in a mixed sample. Here we use a system of four congeneric, co-flowering plants in the genus Clarkia and their bee pollinators to (1) develop a molecular method to quantify different Clarkia pollens found on foraging bees; and (2) determine if bee pollinators carry Clarkia pollens in predictable ways, based on knowledge of their foraging behaviors. We develop a molecular method we call quantitative amplicon sequencing (qAMPseq) which varies cycling number (20, 25, 30, and 35 cycles) in polymerase chain reaction (PCR), individually indexing the same samples in different cycle treatments, and sequencing the resulting amplicons. These values are used to approximate an amplification curve for each Clarkia species in each sample, similar to the approach of quantitative PCR, which can then be used to estimate the relative abundance of the different Clarkia species in the sample. Using this method, we determine that bee visitation behaviors are generally predictive of the pollens that bees carry while foraging. We also show that some bees carry multiple species of Clarkia at the same time, indicating that Clarkia likely compete via interspecific pollen transfer. In addition to adding a ‘missing link’ between bee visitation behavior and actual pollen transfer, we suggest qAMPseq as another molecular method to add to the developing molecular ecology and pollination biology toolbox.

Bayesian ages for pollen records since the last glaciation in North America

Terrestrial pollen records are abundant and widely distributed, making them an excellent proxy for past vegetation dynamics. Age-depth models relate pollen samples from sediment cores to a depositional age based on the relationship between sample depth and available chronological controls. Large-scale synthesis of pollen data benefit from consistent treatment of age uncertainties. Generating new age models helps to reduce potential artifacts from legacy age models that used outdated techniques. Traditional age-depth models, often applied for comparative purposes, infer ages by fitting a curve between dated samples. Bacon, based on Bayesian theory, simulates the sediment deposition process, accounting for both variable deposition rates and temporal/spatial autocorrelation of deposition from one sample to another within the core. Bacon provides robust uncertainty estimation across cores with different depositional processes. We use Bacon to estimate pollen sample ages from 554 North American sediment cores. This dataset standardizes age-depth estimations, supporting future large spatial-temporal studies and removes a challenging, computationally-intensive step for scientists interested in questions that integrate across multiple cores.

Automatic Pollen Species Image Identification

Recent data shows increasing numbers of hay fever patients, with approximately 10-30% of the population affected worldwide (Pawankar et al. 2011). This increase is most likely caused by prolonged and intensified pollen seasons which in turn have been linked to increased CO2 concentrations (Ziska et al. 2003, D'Amato et al. 2007, Albertine et al. 2014). Apart from this, especially in cities, the so-called ‘heat island effect’ enables exotic plant species to establish themselves there. In the Netherlands alone, six new species settle in cities on a yearly basis and some of these are severely allergenic (Denters 2004). Pollen concentrations in the air are currently monitored using pollen samplers that collect pollen on sticky traps. These are checked manually under the microscope, a process that requires highly trained specialists. Moreover, microscopic pollen identification rarely allows discrimination of pollen types at species or even genus level even though the allergenicity may be very different. While there has been progress in automating the microscope using machine learning, automatic microscopes have not been able to systematically identify pollen to the species level. We designed an automated approach identify a predefined set of pollen on microscopic pollen samples. We use 2D light microscope images and a confocal fluorescence microscope for 3D images to create a reference dataset of highly similar pollen species to train automated image recognition software, and compare the results. The most accurate method will be used to apply to a pollen sample time series (1970-present) to find trends in allergenic pollen species over time. Here I present the first results of this research and the challenges to overcome.

Determination of Antioxidant Activity, Phenolic Compound, Mineral Contents and Fatty Acid Compositions of Bee Pollen Grains Collected from Different Locations

The objective of the present work was to investigate the influence of locations on bioactive propertiest, phenolic compounds and mineral contents of bee pollens. The oil content of pollen grains changed between 3.50% (Alanya) and 6.85% (Russia-Perm Region). The highest total phenolic content (720 mg/100g) and antioxidant activity values (81.4%) were observed in pollens obtained from the Russia-Perm Region and Alanya districts, respectively. Additionally, the highest carotenoid was found in a pollen sample collected from Karaman (Sarıveliler) (98.6 mg/g). The major phenolic compounds were (+)-catechin (66.75-337.39 mg/100g) and quercetin (61.2-1221.7 mg/100g) in all pollen samples. The pollen samples were observed to be a significant source of potassium (3846-6287 mg/kg), phosphorus (2947-5010 mg/kg), calcium (1022-2424 mg/kg) and sulfur (1744-2397 mg/kg). All of the analysis results were significantly affected by supplying locations. The antioxidant activity values of pollens were found partly similar and varied depending on locations. The content of saturated fatty acid (palmitic) was high (20-30%) in the tested pollen samples but did not exceed the content of linoleic acid.

Honey Bee Exposure to Pesticides: A Four-Year Nationwide Study

Pollinators, including honey bees, are responsible for the successful reproduction of more than 87% of flowering plant species: they are thus vital to ecosystem health and agricultural services world-wide. To investigate honey bee exposure to pesticides, 168 pollen samples and 142 wax comb samples were collected from colonies within six stationary apiaries in six U.S. states. These samples were analyzed for evidence of pesticides. Samples were taken bi-weekly when each colony was active. Each apiary included thirty colonies, of which five randomly chosen colonies in each apiary were sampled for pollen. The pollen samples were separately pooled by apiary. There were a total of 714 detections in the collected pollen and 1008 detections in collected wax. A total of 91 different compounds were detected: of these, 79 different pesticides and metabolites were observed in the pollen and 56 were observed in the wax. In all years, insecticides were detected more frequently than were fungicides or herbicides: one third of the detected pesticides were found only in pollen. The mean (standard deviation (SD)) number of detections per pooled pollen sample varied by location from 1.1 (1.1) to 8.7 (2.1). Ten different modes of action were found across all four years and nine additional modes of action occurred in only one year. If synergy in toxicological response is a function of simultaneous occurrence of multiple distinct modes of action, then a high frequency of potential synergies was found in pollen and wax-comb samples. Because only pooled pollen samples were obtained from each apiary, and these from only five colonies per apiary per year, more data are needed to adequately evaluate the differences in pesticide exposure risk to honey bees among colonies in the same apiary and by year and location.

Dioxin Analysis of Bee Pollen Pellets Collected by Apis mellifera L. in Rural Area of Turkey

Bee pollen, an important bee product, is harvested as a food supplement for humans, so it must be safe in terms of toxic components for consumption. The aim of this study is to determine the amounts of polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), dioxin-like polychlorinated biphenyls (dl-PCBs) and non dioxin-like PCBs (ndl-PCBs) in the bee pollen pellets of Apis mellifera L. collected from Çankırı, located in the central Anatolia region of Turkey, between June and July 2014. Six types of pollen belonging to four families: Centaurea triumfettii L. - Asteraceae family; Brassica spp. L. - Brassicaceae family; Cistus spp. L. - Cistaceae family; Onobrychis spp. L., Hedysarum spp. L. and Trifolium spp. L. - Fabaceae family, were determined through microscopic analysis. Dioxin and PCB congeners were determined in a pooled bee pollen sample and all the results were found lower than the European Union regulatory limits for other foods. To the best of our knowledge, this is among the first studies on dioxin analysis in bee pollen worldwide.