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Implementation of marker-assisted selection (MAS) in modern beekeeping would improve sustainability, especially in breeding programs aiming for resilience against the parasitic mite Varroa destructor. Selecting honey bee colonies for natural resistance traits, such as brood-intrinsic suppression of varroa mite reproduction, reduces the use of chemical acaricides while respecting local adaptation. In 2019, eight genomic variants associated with varroa non-reproduction in drone brood were discovered in a single colony from the Amsterdam Water Dune population in the Netherlands.Recently, a new study tested the applicability of these eight genetic variants for the same phenotype on a population-wide scale in Flanders, Belgium. As the properties of some variants varied between the two studies, one hypothesized that the difference in genetic ancestry of the sampled colonies may underly these contribution shifts. In order to frame this, we determined the allele frequencies of the eight genetic variants in more than 360 Apis mellifera colonies across the European continent and found that variant type allele frequencies of these variants are primarily related to the A. mellifera subspecies or phylogenetic honey bee lineage. Our results confirm that population-specific genetic markers should always be evaluated in a new population prior to using them in MAS programs.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies’ exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony’s health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project’s data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

Nematodes are numerous in soils and play a crucial role in soil food-webs. DNA metabarcoding offers a time-effective alternative to morphology-based assessments of nematode diversity. However, it is unclear how different DNA extraction methods prior to metabarcoding could affect community analysis. We used soils with woody vegetation from a European latitudinal gradient (29 sites, 39 to 79°N, ∼4500 km, covering six biomes) to systematically evaluate the effect of two sources of nematode DNA either directly extracted from soils vs. extracted from nematodes previously isolated from soils hypothesizing that the DNA source material may produce different diversities, community structures and abundances of feeding types. Nematode-sample DNA exhibited a higher richness, while no difference in Shannon diversity was found between the approaches. The DNA sources also created significantly different community structures, with greater differences observed across soil-extracted DNA than nematode-sample DNA. The most overrepresented species in nematode-sample DNA were Heterocephalobus elongatus, Eucephalobus striatus and Hexatylus sp., whereas Phasmarhabditis sp. and Eumonhystera filiformis were overrepresented in soil-extracted DNA. Read abundances of feeding types significantly differed between the DNA sources and across sites, with a significant effect of biome on both ecto- and endoparasitic herbivores in soil-extracted DNA and for ectoparasitic herbivores only in nematode-sample DNA. Collectively, our data suggest that choice of the DNA source material may lead to different patterns of nematode community composition across space and environmental conditions. Improving the sensitivity of the soil-extracted DNA method by developing protocols using larger amounts of soil and designing nematode-specific primers will make this approach an efficient screening tool to analyse nematode diversity and community structure complementing the labour-intensive isolation of intact nematodes, Nematodes are numerous in soils and play a crucial role in soil food-webs. DNA metabarcoding offers a time-effective alternative to morphology-based assessments of nematode diversity. However, it is unclear how different DNA extraction methods prior to metabarcoding could affect community analysis. We used soils with woody vegetation from a European latitudinal gradient (29 sites, 39 to 79°N, ∼4500 km, covering six biomes) to systematically evaluate the effect of two sources of nematode DNA either directly extracted from soils vs. extracted from nematodes previously isolated from soils hypothesizing that the DNA source material may produce different diversities, community structures and abundances of feeding types. Nematode-sample DNA exhibited a higher richness, while no difference in Shannon diversity was found between the approaches. The DNA sources also created significantly different community structures, with greater differences observed across soil-extracted DNA than nematode-sample DNA. The most overrepresented species in nematode-sample DNA were Heterocephalobus elongatus, Eucephalobus striatus and Hexatylus sp., whereas Phasmarhabditis sp. and Eumonhystera filiformis were overrepresented in soil-extracted DNA. Read abundances of feeding types significantly differed between the DNA sources and across sites, with a significant effect of biome on both ecto- and endoparasitic herbivores in soil-extracted DNA and for ectoparasitic herbivores only in nematode-sample DNA. Collectively, our data suggest that choice of the DNA source material may lead to different patterns of nematode community composition across space and environmental conditions. Improving the sensitivity of the soil-extracted DNA method by developing protocols using larger amounts of soil and designing nematode-specific primers will make this approach an efficient screening tool to analyse nematode diversity and community structure complementing the labour-intensive isolation of intact nemato

Nematodes are numerous in soils and play a crucial role in soil food-webs. DNA metabarcoding offers a time-effective alternative to morphology-based assessments of nematode diversity. However, it is unclear how different DNA extraction methods prior to metabarcoding could affect community analysis. We used soils with woody vegetation from a European latitudinal gradient (29 sites, 39 to 79°N, ∼4500 km, covering six biomes) to systematically evaluate the effect of two sources of nematode DNA either directly extracted from soils vs. extracted from nematodes previously isolated from soils hypothesizing that the DNA source material may produce different diversities, community structures and abundances of feeding types. Nematode-sample DNA exhibited a higher richness, while no difference in Shannon diversity was found between the approaches. The DNA sources also created significantly different community structures, with greater differences observed across soil-extracted DNA than nematode-sample DNA. The most overrepresented species in nematode-sample DNA were Heterocephalobus elongatus, Eucephalobus striatus and Hexatylus sp., whereas Phasmarhabditis sp. and Eumonhystera filiformis were overrepresented in soil-extracted DNA. Read abundances of feeding types significantly differed between the DNA sources and across sites, with a significant effect of biome on both ecto- and endoparasitic herbivores in soil-extracted DNA and for ectoparasitic herbivores only in nematode-sample DNA. Collectively, our data suggest that choice of the DNA source material may lead to different patterns of nematode community composition across space and environmental conditions. Improving the sensitivity of the soil-extracted DNA method by developing protocols using larger amounts of soil and designing nematode-specific primers will make this approach an efficient screening tool to analyse nematode diversity and community structure complementing the labour-intensive isolation of intact nematodes