1. Adaptation through natural selection is the natural response of bee populations to environmental changes and the challenge of pests and diseases. The richness in biodiversity of races and ecotyp...

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.

Honeybees are of great importance because of their role in pollination as well as for hive products. The population of managed colonies fluctuates over time, and recent monitoring reports show different levels of colony losses in many regions and countries. The cause of this kind of loss is a combination of various factors, such as the parasitic mite Varroa destructor, viruses, pesticides, management practices, climate change, and other stress factors. Having in mind that the economic aspect of honeybee colony losses has not been estimated, a pioneer effort was made for developing a methodology that estimates the economic impact of honeybee colony losses. Winter loss data was based on 2993 answers of the COLOSS standard questionnaire survey of honeybee winter colony losses for 2016/2017. In addition, market and financial data were used for each country. In a comparative analysis, an assessment on the economic impact of colony losses in Austria, Czechia, and Macedonia was made. The estimation considered the value of the colonies and the potential production losses of the lost colonies and of surviving but weak colonies. The direct economic impact of winter honeybee colony losses in 2016/2017 in Austria was estimated to be about 32 Mio €; in Czechia, 21 Mio €; and in Macedonia, 3 Mio €. Economic impact reflects the different value levels in the three countries, national colony populations, and the magnitude of colony losses. This study also suggests that economic losses are much higher than the subsidies, which underlines the economic importance of honeybees for the agricultural sector.

IBRA, the world's longest established apicultural research publishers, promotes the value of bees by providing information on bee science and beekeeping worldwide. A not-for-profit organisation founded in 1949

IBRA, the world's longest established apicultural research publishers, promotes the value of bees by providing information on bee science and beekeeping worldwide. A not-for-profit organisation founded in 1949

Genetically linked small and large dairy cattle populations were simulated to test the effect of different sources of information from foreign populations on the accuracy of predicting breeding values for young animals in a small population. A large dairy cattle population (PL) with >20 generations was simulated, and a small subpopulation (PS) with 3 generations was formed as a related population, including phenotypes and genomic information. Predicted breeding values for young animals in the small population were calculated using BLUP and single-step genomic BLUP (ssGBLUP) in 4 different scenarios: (S1) 3,166 phenotypes, 22,855 pedigree animals, and 1,000 to 6,000 genotypes for PS; (S2) S1 plus genomic estimated breeding value (GEBV) for 4,475 sires from PL as external information; (S3) S1 plus 221,580 phenotypes, 402,829 pedigree animals, and 53,558 genotypes for PL; and (S4) single nucleotide polymorphism (SNP) effects calculated based on PL data. The ability to predict true breeding value was assessed in the youngest third of the genotyped animals in the small population. When data only from the small population were used and 1,000 animals were genotyped, the accuracy of GEBV was only 1 point greater than the estimated breeding value accuracy (0.32 vs. 0.31). Adding external GEBV for sires from PL did not considerably increase accuracy (0.33 vs. 0.32 in S1). Combining phenotypes, pedigree, and genotypes for PS and PL was beneficial for predicting accuracy of GEBV in the small population, and the prediction accuracy of GEBV in this scenario was 0.38 compared with 0.31 from estimated breeding values. When SNP effects from PL were used to predict GEBV for young genotyped animals from PS, accuracy was greatest (0.56). With 6,000 genotyped animal in PS, accuracy was greatest (0.61) with the combined populations. In a small population with few genotypes, the highest accuracy of evaluation may be obtained by using SNP effects derived from a related large population.