No abstract

In the fight against the Varroa destructor mite, selective breeding of honey bee (Apis mellifera L.) populations that are resistant to the parasitic mite stands as a sustainable solution. Selection initiatives indicate that using the suppressed mite reproduction (SMR) trait as a selection criterion is a suitable tool to breed such resistant bee populations. We conducted a large European experiment to evaluate the SMR trait in different populations of honey bees spread over 13 different countries, and representing different honey bee genotypes with their local mite parasites. The first goal was to standardize and validate the SMR evaluation method, and then to compare the SMR trait between the different populations. Simulation results indicate that it is necessary to examine at least 35 single-infested cells to reliably estimate the SMR score of any given colony. Several colonies from our dataset display high SMR scores indicating that this trait is present within the European honey bee populations. The trait is highly variable between colonies and some countries, but no major differences could be identified between countries for a given genotype, or between genotypes in different countries. This study shows the potential to increase selective breeding efforts of V. destructor resistant populations.

Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

The honey bee (Apis mellifera) is an ecologically and economically important species that provides pollination services to natural and agricultural systems. The biodiversity of the honey bee is being endangered by the mass import of non-native queens. In many locations, it is not clear how the local populations have been affected by hybridisation between native and non-native bees. There is especially little information about temporal changes in hybridisation. In Romania, A. m. carpatica naturally occurs, and earlier studies show that there are two subpopulations separated by the Carpathian Mountains. In this study, we investigated how the arrangement of veins in bees’ wings (venation) has changed in Romanian honey bees in the last four decades. We found that in the contemporary population of Romanian bees, there are still clear differences between the intra- and extra-Carpathian subpopulations, which indicates that natural variation among honey bees is still being preserved. We also found significant differences between bees collected before and after 2000. The observed temporal changes in wing venation are most likely caused by hybridisation between native bees and non-native bees sporadically introduced by beekeepers. In order to facilitate conservation and the monitoring of native Romanian bees, we developed a method facilitating their identification.

Background The honey bee (Apis mellifera) is an ecologically and economically important species that provides pollination services to natural and agricultural systems. The biodiversity of the honey bee in parts of its native range is endangered by migratory beekeeping and commercial breeding. In consequence, some honey bee populations that are well adapted to the local environment are threatened with extinction. A crucial step for the protection of honey bee biodiversity is reliable differentiation between native and nonnative bees. One of the methods that can be used for this is the geometric morphometrics of wings. This method is fast, is low cost, and does not require expensive equipment. Therefore, it can be easily used by both scientists and beekeepers. However, wing geometric morphometrics is challenging due to the lack of reference data that can be reliably used for comparisons between different geographic regions. Findings Here, we provide an unprecedented collection of 26,481 honey bee wing images representing 1,725 samples from 13 European countries. The wing images are accompanied by the coordinates of 19 landmarks and the geographic coordinates of the sampling locations. We present an R script that describes the workflow for analyzing the data and identifying an unknown sample. We compared the data with available reference samples for lineage and found general agreement with them. Conclusions The extensive collection of wing images available on the Zenodo website can be used to identify the geographic origin of unknown samples and therefore assist in the monitoring and conservation of honey bee biodiversity in Europe.

No abstract

The importance of varroosis control in a natural and sustainable way is crucial for beekeeping, having in view the varroa mite impact on honey bee health. In the last years, we developed a highly effective procedure for treating varroa in capped brood using volatile organic acids. This procedure can be applied at any moment of the active season as it uses organic substances. Taking into account the necessity to drastically reduce the level of varroa infestation in colonies before winter bee rearing, we developed a relatively simple pilot study to preliminarily test the impact of spring treatments on varroa infestation level in brood, to be evaluated in summer when, naturally, the population of mites increases. To test the hypothesis, two experimentally treated groups and a control group were used. The treatment consisted of brushing all capped brood with formic acid of 65% concentration in one and two applications. The obtained results show very significant differences between the treated and control groups in terms of infested cell percentages evaluated in the July–August period. Consequently, the spring treatments could be an important tool in limiting the varroa mite multiplication, but further experiments are necessary to test and adapt them to different local conditions.

Honeybees (Apis mellifera L.) are excellent biosensors that can be managed to collect valuable information about environmental contamination. The main objective of the present study was to design and apply an integrative protocol to monitor honeybee colony activity and sample collection by using electronic technologies combined with classical methods in order to evaluate the exposure of honeybees to the neonicotinoids that are used in melliferous intensive crops. The monitored honeybee colonies were especially prepared and equipped to maximize their chances to collect representative samples in order to express, as well as possible, the pesticide residues that existed in the targeted crops. The samples of honey, pollen and honeybees were collected, preserved and prepared to fulfill the required quality and quantity criteria of the accredited laboratories. In total, a set of fifty samples was collected from fields, located in different areas of intensive agriculture in Romania, and was analyzed for five neonicotinoids. The obtained results show that 48% of the total analyzed samples (n = 50) contained one or more detected or quantified neonicotinoid residues. The main conclusion is that the proposed approach for sample collection and preparation could improve the evaluation methodologies for analyzing honeybees’ exposure to pesticides.