Honeybees as active samplers for microplastics

Microplastics are ubiquitous and their sampling is a difficult task. Honeybees interact with the environment inside their foraging range and take pollutants with them. In this work, we demonstrated for the first time that worker bees can act as active samplers of microplastics. We collected honeybees from apiaries located in the centre of Copenhagen and from nearby semiurban and rural areas. We showed the presence of microplastics in all sampled locations mostly in the form of fragments (52%) and fibres (38%) with average equivalent diameter of 64 ± 39 μm for fibres and 234 ± 156 μm for fragments. The highest load corresponded to urban apiaries, but comparable number of microplastics was found in hives from suburban and rural areas, which can be explained by the presence of urban settlements inside the foraging range of worker bees and to the easy dispersion of small microplastics by wind. Micro-FTIR analysis confirmed the presence of thirteen synthetic polymers, the most frequently of which was polyester followed by polyethylene and polyvinyl chloride. Our results demonstrated the presence of microplastics attached to the body of the honeybees and opens a new research pathway to their use as active biosamplers for anthropogenic pollutionThe authors acknowledge the financial support provided by the Spanish Government: CTM2016-74927-C2-1-R/2-R, and the Thematic Network of Micro- and Nanoplastics in the Environment (RED2018-102345-T, EnviroPlaNet Network). CE thanks the Spanish Ministry of Science, Innovation and Universities for the award of a pre-doctoral grant (FPI

Preservation methods of honey bee-collected pollen are not a source of bias in ITS2 metabarcoding

Pollen metabarcoding is emerging as a powerful tool for ecological research and offers unprecedented scale in citizen science projects for environmental monitoring via honey bees. Biases in metabarcoding can be introduced at any stage of sample processing and preservation is at the forefront of the pipeline. While in metabarcoding studies pollen has been preserved at − 20 °C (FRZ), this is not the best method for citizen scientists. Herein, we compared this method with ethanol (EtOH), silica gel (SG) and room temperature (RT) for preservation of pollen collected from hives in Austria and Denmark. After ~ 4 months of storage, DNAs were extracted with a food kit, and their quality and concentration measured. Most DNA extracts exhibited 260/280 absorbance ratios close to the optimal 1.8, with RT samples from Austria performing slightly worse than FRZ and SG samples (P < 0.027). Statistical differences were also detected for DNA concentration, with EtOH samples producing lower yields than RT and FRZ samples in both countries and SG in Austria (P < 0.042). Yet, qualitative and quantitative assessments of floral composition obtained using high-throughput sequencing with the ITS2 barcode gave non-significant effects of preservation methods on richness, relative abundance and Shannon diversity, in both countries. While freezing and ethanol are commonly employed for archiving tissue for molecular applications, desiccation is cheaper and easier to use regarding both storage and transportation. Since SG is less dependent on ambient humidity and less prone to contamination than RT, we recommend SG for preserving pollen for metabarcoding. SG is straightforward for laymen to use and hence robust for widespread application in citizen science studies.We are deeply indebted to Susana Lopes and Maria Magalhães, from CIBIO—Research Centre in Biodiversity and Genetic Resources—InBIO Associate Laboratory, for their time devoted to library preparation and sequencing in the MiSeq. AQ acknowledges the PhD scholarship (DFA/BD/5155/2020) funded by FCT. This work was funded by the Health and Food Safety Directorate General, European Commission through the project INSIGNIA—Environmental monitoring of pesticide use through honeybees SANTE/E4/SI2.788418-SI2.788452- INSIGINIA-PP-1–1-2018. Fundação para a Ciência e a Tecnologia (FCT) provided financial support by national funds (FCT/MCTES) to CIMO (UIDB/00690/2020).info:eu-repo/semantics/publishedVersio

Results of international standardised beekeeper surveys of colony losses for winter 2012-2013 : analysis of winter loss rates and mixed effects modelling of risk factors for winter loss.

This article presents results of an analysis of winter losses of honey bee colonies from 19 mainly European countries, most of which implemented the standardised 2013 COLOSS questionnaire. Generalised linear mixed effects models (GLMMs) were used to investigate the effects of several factors on the risk of colony loss, including different treatments for Varroa destructor, allowing for random effects of beekeeper and region. Both winter and summer treatments were considered, and the most common combinations of treatment and timing were used to define treatment factor levels. Overall and within country colony loss rates are presented. Significant factors in the model were found to be: percentage of young queens in the colonies before winter, extent of queen problems in summer, treatment of the varroa mite, and access by foraging honey bees to oilseed rape and maize. Spatial variation at the beekeeper level is shown across geographical regions using random effects from the fitted models, both before and after allowing for the effect of the significant terms in the model. This spatial variation is considerable

Semi-automated sequence curation for reliable reference datasets in ITS2 vascular plant DNA (meta-)barcoding

One of the most critical steps for accurate taxonomic identification in DNA (meta)-barcoding is to have an accurate DNA reference sequence dataset for the marker of choice. Therefore, developing such a dataset has been a long-term ambition, especially in the Viridiplantae kingdom. Typically, reference datasets are constructed with sequences downloaded from general public databases, which can carry taxonomic and other relevant errors. Herein, we constructed a curated (i) global dataset, (ii) European crop dataset, and (iii) 27 datasets for the EU countries for the ITS2 barcoding marker of vascular plants. To that end, we first developed a pipeline script that entails (i) an automated curation stage comprising five filters, (ii) manual taxonomic correction for misclassified taxa, and (iii) manual addition of newly sequenced species. The pipeline allows easy updating of the curated datasets. With this approach, 13% of the sequences, corresponding to 7% of species originally imported from GenBank, were discarded. Further, 259 sequences were manually added to the curated global dataset, which now comprises 307,977 sequences of 111,382 plant species.AQ acknowledges the PhD scholarship (2020.05155.BD), funded by the Portuguese Foundation for Science and Technology (FCT). This work was developed in the framework of INSIGNIA – Environmental monitoring of pesticide use through honeybees (SANTE/E4/SI2.788418-SI2.788452-INSIGINIA-PP-1-1-2018) and INSIGNIA-EU - Preparatory action for monitoring of environmental pollution using honey bees (Procurement procedure ENV/2021/OP/0014 of 28-09-2021). FCT provided financial support by national funds (FCT/MCTES) to CIMO (UIDB/00690/2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Standard survey methods for estimating colony losses and explanatory risk factors in Apis mellifera

This chapter addresses survey methodology and questionnaire design for the collection of data pertaining to estimation of honey bee colony loss rates and identification of risk factors for colony loss. Sources of error in surveys are described. Advantages and disadvantages of different random and non-random sampling strategies and different modes of data collection are presented to enable the researcher to make an informed choice. We discuss survey and questionnaire methodology in some detail, for the purpose of raising awareness of issues to be considered during the survey design stage in order to minimise error and bias in the results. Aspects of survey design are illustrated using surveys in Scotland. Part of a standardized questionnaire is given as a further example, developed by the COLOSS working group for Monitoring and Diagnosis. Approaches to data analysis are described, focussing on estimation of loss rates. Dutch monitoring data from 2012 were used for an example of a statistical analysis with the public domain R software. We demonstrate the estimation of the overall proportion of losses and corresponding confidence interval using a quasi-binomial model to account for extra-binomial variation. We also illustrate generalized linear model fitting when incorporating a single risk factor, and derivation of relevant confidence intervals

Multi-country loss rates of honey bee colonies during winter 2016/2017 from the COLOSS survey

Publication history: Accepted - 5 March 2018; Published online - 8 May 2018.In this short note we present comparable loss rates of honey bee colonies during winter 2016/2017 from 27 European countries plus Algeria, Israel and Mexico, obtained with the COLOSS questionnaire. The 14,813 beekeepers providing valid loss data collectively wintered 425,762 colonies, and reported 21,887 (5.1%, 95% confidence interval 5.0–5.3%) colonies with unsolvable queen problems and 60,227 (14.1%, 95% CI 13.8–14.4%) dead colonies after winter. Additionally we asked for colonies lost due to natural disaster, which made up another 6,903 colonies (1.6%, 95% CI 1.5–1.7%). This results in an overall loss rate of 20.9% (95% CI 20.6–21.3%) of honey bee colonies during winter 2016/2017, with marked differences among countries. The overall analysis showed that small operations suffered higher losses than larger ones (p < 0.001). Overall migratory beekeeping had no significant effect on the risk of winter loss, though there was an effect in several countries. A table is presented giving detailed results from 30 countries. A map is also included, showing relative risk of colony winter loss at regional level.The authors are also grateful to various national funding sources for their support of some of the monitoring surveys [including, in the Republic of Serbia, MPNTR-RS, through grant number III46002]. The authors acknowledge the financial support by the University of Graz for open access publication

Supplementary information for the article: Brodschneider, R.; Schlagbauer, J.; Arakelyan, I.; Ballis, A.; Brus, J.; Brusbardis, V.; Cadahía, L.; Charrière, J.-D.; Chlebo, R.; Coffey, M. F.; Cornelissen, B.; da Costa, C. A.; Danneels, E.; Danihlík, J.; Dobrescu, C.; Evans, G.; Fedoriak, M.; Forsythe, I.; Gregorc, A.; Johannesen, J.; Kauko, L.; Kristiansen, P.; Martikkala, M.; Martín-Hernández, R.; Mazur, E.; Mutinelli, F.; Patalano, S.; Raudmets, A.; Simon Delso, N.; Stevanovic, J.; Uzunov, A.; Vejsnæs, F.; Williams, A.; Gray, A. Spatial Clusters of Varroa Destructor Control Strategies in Europe. J Pest Sci 2022. https://doi.org/10.1007/s10340-022-01523-2.

Table S1. Utilized packages of the statistical software R version 4.0.4.Supplementary material for: [https://vet-erinar.vet.bg.ac.rs/handle/123456789/2469]Related to the published version: [https://vet-erinar.vet.bg.ac.rs/handle/123456789/2469

CSI pollen: diversity of honey bee collected pollen studied by citizen scientists

A diverse supply of pollen is an important factor for honey bee health, but information about the pollen diversity available to colonies at the landscape scale is largely missing. In this COLOSS study, beekeeper citizen scientists sampled and analyzed the diversity of pollen collected by honey bee colonies. As a simple measure of diversity, beekeepers determined the number of colors found in pollen samples that were collected in a coordinated and standardized way. Altogether, 750 beekeepers from 28 different regions from 24 countries participated in the two-year study and collected and analyzed almost 18,000 pollen samples. Pollen samples contained approximately six different colors in total throughout the sampling period, of which four colors were abundant. We ran generalized linear mixed models to test for possible effects of diverse factors such as collection, i.e., whether a minimum amount of pollen was collected or not, and habitat type on the number of colors found in pollen samples. To identify habitat effects on pollen diversity, beekeepers’ descriptions of the surrounding landscape and CORINE land cover classes were investigated in two different models, which both showed that both the total number and the rare number of colors in pollen samples were positively affected by ‘urban’ habitats or ‘artificial surfaces’, respectively. This citizen science study underlines the importance of the habitat for pollen diversity for bees and suggests higher diversity in urban areas

Weight Watching and the Effect of Landscape on Honeybee Colony Productivity: Investigating the Value of Colony Weight Monitoring for the Beekeeping Industry

<div><p>Over the last few decades, a gradual departure away from traditional agricultural practices has resulted in alterations to the composition of the countryside and landscapes across Europe. In the face of such changes, monitoring the development and productivity of honey bee colonies from different sites can give valuable insight on the influence of landscape on their productivity and might point towards future directions for modernized beekeeping practices. Using data on honeybee colony weights provided by electronic scales spread across Denmark, we investigated the effect of the immediate landscape on colony productivity. In order to extract meaningful information, data manipulation was necessary prior to analysis as a result of different management regimes or scales malfunction. Once this was carried out, we were able to show that colonies situated in landscapes composed of more than 50% urban areas were significantly more productive than colonies situated in those with more than 50% agricultural areas or those in mixed areas. As well as exploring some of the potential reasons for the observed differences, we discuss the value of weight monitoring of colonies on a large scale.</p></div

Average hive weights, in kg, by landscape type for a 1km radius around the hives.

<p>Agricultural (n = 49; mean = 45.94): More than 50% of the surrounding 1km landscape composed of agricultural areas; Urban (n = 12; mean = 57.50): More than 50% of the surrounding 1km landscape composed of urban areas; Mixed (n = 10; mean = 39.90): A combination of landscape types surrounding the apiary with no single habitat type representing more than 50% of the total landscape. Means that do not share a letter are statistically significant.</p