Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs

Tracing the genealogy origin of geographic populations based on genomic variation and deep learning

Assigning a query individual animal or plant to its derived population is a prime task in diverse applications related to organismal genealogy. Such endeavors have conventionally relied on short DNA sequences under a phylogenetic framework. These methods naturally show constraints when the inferred population sources are ambiguously phylogenetically structured, a scenario demanding substantially more informative genetic signals. Recent advances in cost-effective production of whole-genome sequences and artificial intelligence have created an unprecedented opportunity to trace the population origin for essentially any given individual, as long as the genome reference data are comprehensive and standardized. Here, we developed a convolutional neural network method to identify population origins using genomic SNPs. Three empirical datasets (an Asian honeybee, a red fire ant, and a chicken datasets) and two simulated populations are used for the proof of concepts. The performance tests indicate that our method can accurately identify the genealogy origin of query individuals, with success rates ranging from  93 % to 100 %. We further showed that the accuracy of the model can be significantly increased by refining the informative sites through FST filtering. Our method is robust to configurations related to batch sizes and epochs, whereas model learning benefits from the setting of a proper preset learning rate. Moreover, we explained the importance score of key sites for algorithm interpretability and credibility, which has been largely ignored. We anticipate that by coupling genomics and deep learning, our method will see broad potential in conservation and management applications that involve natural resources, invasive pests and weeds, and illegal trades of wildlife products.

Allele Frequencies of Genetic Variants Associated with Varroa Drone Brood Resistance (DBR) in Apis mellifera Subspecies across the European Continent

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.

Reduction of genetic diversity in 'Alalā (Hawaiian crow; Corvus hawaiiensis) between the late 1800s and the late 1900s

Genetic and genomic data are increasingly used to aid conservation management of endangered species by providing insights into evolutionary histories, factors associated with extinction risks, and potential for future adaptation. For the 'Alalā, or Hawaiian crow (Corvus hawaiiensis), genetic concerns include negative correlations between inbreeding and hatching success. However, it is unclear if low genetic diversity and inbreeding depression are consequences of a historical population bottleneck, or if 'Alalā had historically low genetic diversity that predated human influence, perhaps as a result of earlier declines or founding events. In this study, we applied a hybridization-based sequence capture to generate a genome-wide single nucleotide polymorphism (SNP) dataset for comparing historical specimens collected in the 1890s, when 'Alalā were more numerous, to samples taken between 1973 and 1998, when 'Alalā population densities were near the lowest documented levels in the wild, prior to all individuals being collected for captive rearing. We found low genome-wide diversity in both sample groups, however, the modern sample group (1973 to 1998 cohort) exhibited relatively fewer polymorphic alleles, a lower proportion of polymorphic loci, and lower observed heterozygosity, consistent with a population decline and potential bottleneck effects. These results combined with a current low population size highlight the importance of continued efforts by conservation managers to mitigate inbreeding and maintain founder representation to preserve what genetic diversity remains.

Data mining reveals tissue-specific expression and host lineage-associated forms of Apis mellifera filamentous virus

Background

Apis mellifera filamentous virus (AmFV) is a large double-stranded DNA virus of uncertain phylogenetic position that infects honey bees (Apis mellifera). Little is known about AmFV evolution or molecular aspects of infection. Accurate annotation of open-reading frames (ORFs) is challenged by weak homology to other known viruses. This study was undertaken to evaluate ORFs (including coding-frame conservation, codon bias, and purifying selection), quantify genetic variation within AmFV, identify host characteristics that covary with infection rate, and examine viral expression patterns in different tissues.

Methods

Short-read data were accessed from the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI). Sequence reads were downloaded from accessions meeting search criteria and scanned for kmers representative of AmFV genomic sequence. Samples with kmer counts above specified thresholds were downloaded in full for mapping to reference sequences and de novo assembly.

Results

At least three distinct evolutionary lineages of AmFV exist. Clade 1 predominates in Europe but in the Americas and Africa it is replaced by the other clades as infection level increases in hosts. Only clade 3 was found at high relative abundance in hosts with African ancestry, whereas all clades achieved high relative abundance in bees of non-African ancestry. In Europe and Africa, clade 2 was generally detected only in low-level infections but was locally dominant in some North American samples. The geographic distribution of clade 3 was consistent with an introduction to the Americas with 'Africanized' honey bees in the 1950s. Localized genomic regions of very high nucleotide divergence in individual isolates suggest recombination with additional, as-yet unidentified AmFV lineages. A set of 155 high-confidence ORFs was annotated based on evolutionary conservation in six AmFV genome sequences representative of the three clades. Pairwise protein-level identity averaged 94.6% across ORFs (range 77.1-100%), which generally exhibited low evolutionary rates and moderate to strong codon bias. However, no robust example of positive diversifying selection on coding sequence was found in these alignments. Most of the genome was detected in RNA short-read alignments. Transcriptome assembly often yielded contigs in excess of 50 kb and containing ORFs in both orientations, and the termini of long transcripts were associated with tandem repeats. Lower levels of AmFV RNA were detected in brain tissue compared to abdominal tissue, and a distinct set of ORFs had minimal to no detectable expression in brain tissue. A scan of DNA accessions from the parasitic mite Varroa destructor was inconclusive with respect to replication in that species.

Discussion

Collectively, these results expand our understanding of this enigmatic virus, revealing transcriptional complexity and co-evolutionary associations with host lineage.

Haplotypes of the tRNAleu-COII mtDNA Region in Russian Apis mellifera Populations

Analysis of the mtDNA tRNAleu-COII locus is a widely used tool to establish belonging to a particular evolutionary lineage of Apis mellifera L. (lineages A, M, C, O, and Y). In Russia, most of the area was once inhabited by Apis mellifera mellifera from the M evolutionary lineage, but the introduction of bee subspecies from the southern regions of Russia (A. m. caucasica, A. m. carnica) and from abroad (A. m. carnica, A. m. ligustica) led to fragmentation of their native range. In this study, the results of assessing the haplotype number for the tRNAleu-COII locus of mtDNA in Russian Apis mellifera populations were presented. We analyzed 269 colonies from 19 regions of Russia. As a result, two evolutionary lineages were identified: the East European lineage C (26.4%) and the Northwestern European lineage M (73.6%). A total of 29 haplotypes were identified, 8 of them were already reported, and 21 were found to be novel. From the C lineage, haplotypes C1, C2, C2c, C2j, and C3 were predominant. All M lineage samples from Russia belong to the M17 and M4' haplogroups but have only minor variations in the form of nucleotide substitutions. An analysis of publications devoted to the tRNAleu-COII locus haplotypes, as well as an analysis of the available tRNAleu-COII sequences in GenBank, showed that there is still a problem with the haplotype nomenclature.

Multiple mitogenomes indicate Things Fall Apart with Out of Africa or Asia hypotheses for the phylogeographic evolution of Honey Bees (Apis mellifera)

Previous morpho-molecular studies of evolutionary relationships within the economically important genus of honey bees (Apis), including the Western Honey Bee (A. mellifera L.), have suggested Out of Africa or Asia origins and subsequent spread to Europe. I test these hypotheses by a meta-analysis of complete mitochondrial DNA coding regions (11.0 kbp) from 22 nominal subspecies represented by 78 individual sequences in A. mellifera. Parsimony, distance, and likelihood analyses identify six nested clades: Things Fall Apart with Out of Africa or Asia hypotheses. Molecular clock-calibrated phylogeographic analysis shows instead a basal origin of A. m. mellifera in Europe ~ 780 Kya, and expansion to Southeast Europe and Asia Minor ~ 720 Kya. Eurasian bees spread southward via a Levantine/Nilotic/Arabian corridor into Africa ~ 540 Kya. An African clade re-established in Iberia ~ 100 Kya spread thereafter to westerly Mediterranean islands and back into North Africa. Nominal subspecies within the Asia Minor and Mediterranean clades are less differentiated than are individuals within other subspecies. Names matter: paraphyletic anomalies are artefacts of mis-referral in GenBank of sequences to the wrong subspecies, or use of faulty sequences, which are clarified by inclusion of multiple sequences from available subspecies.

AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap

Honey bee, Apis mellifera, drones are typically haploid, developing from an unfertilized egg, inheriting only their queen's alleles and none from the many drones she mated with. Thus the ordered combination or 'phase' of alleles is known, making drones a valuable haplotype resource. We collated whole-genome sequence data for 1,407 drones, including 45 newly sequenced Scottish drones, collectively representing 19 countries, 8 subspecies and various hybrids. Following alignment to Amel_HAv3.1, variant calling and quality filtering, we retained 17.4 M high quality variants across 1,328 samples with a genotyping rate of 98.7%. We demonstrate the utility of this haplotype resource, AmelHap, for genotype imputation, returning >95% concordance when up to 61% of data is missing in haploids and up to 12% of data is missing in diploids. AmelHap will serve as a useful resource for the community for imputation from low-depth sequencing or SNP chip data, accurate phasing of diploids for association studies, and as a comprehensive reference panel for population genetic and evolutionary analyses.

Honey bee (Apis mellifera) wing images: a tool for identification and conservation

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.

Identification of runs of homozygosity in Western honey bees (Apis mellifera) using whole-genome sequencing data

Runs of homozygosity (ROH) are continuous homozygous segments that arise through the transmission of haplotypes that are identical by descent. The length and distribution of ROH segments provide insights into the genetic diversity of populations and can be associated with selection signatures. Here, we analyzed reconstructed whole-genome queen genotypes, from a pool-seq data experiment including 265 Western honeybee colonies from Apis mellifera mellifera and Apis mellifera carnica. Integrating individual ROH patterns and admixture levels in a dynamic population network visualization allowed us to ascertain major differences between the two subspecies. Within A. m. mellifera, we identified well-defined substructures according to the genetic origin of the queens. Despite the current applied conservation efforts, we pinpointed 79 admixed queens. Genomic inbreeding (F ROH) strongly varied within and between the identified subpopulations. Conserved A. m. mellifera from Switzerland had the highest mean F ROH (3.39%), while queens originating from a conservation area in France, which were also highly admixed, showed significantly lower F ROH (0.45%). The majority of A. m. carnica queens were also highly admixed, except 12 purebred queens with a mean F ROH of 2.33%. Within the breed-specific ROH islands, we identified 14 coding genes for A. m. mellifera and five for A. m. carnica, respectively. Local adaption of A. m. mellifera could be suggested by the identification of genes involved in the response to ultraviolet light (Crh-BP, Uvop) and body size (Hex70a, Hex70b), while the A. m. carnica specific genes Cpr3 and Cpr4 are most likely associated with the lighter striping pattern, a morphological phenotype expected in this subspecies. We demonstrated that queen genotypes derived from pooled workers are useful tool to unravel the population dynamics in A. mellifera and provide fundamental information to conserve native honey bees.

Genetic Structure of Apis cerana Populations from South Korea, Vietnam and the Russian Far East Based on Microsatellite and Mitochondrial DNA Polymorphism

In this article, we present the results of the genetic analysis of Apis cerana samples from the Russian Far East, South Korea and Vietnam. An analysis of the polymorphism of seven microsatellite loci and an assessment of the haplotype diversity of the mtDNA tRNAleu-COII locus were performed. A fragment of about 431 bp in tRNAleu-COII was sequenced. The analysis showed the presence of 14 haplotypes, while the predominant haplotype was Japan1. Microsatellite data revealed two differentiated clusters. The first cluster contained tropical climate A. cerana samples from Vietnam, and the second cluster combined temperate climate A. cerana samples from the Russian Far East and South Korea.

Using the Software DeepWings© to Classify Honey Bees across Europe through Wing Geometric Morphometrics

DeepWings© is a software that uses machine learning to automatically classify honey bee subspecies by wing geometric morphometrics. Here, we tested the five subspecies classifier (A. m. carnica, Apis mellifera caucasia, A. m. iberiensis, Apis mellifera ligustica, and A. m. mellifera) of DeepWings© on 14,816 wing images with variable quality and acquired by different beekeepers and researchers. These images represented 2601 colonies from the native ranges of the M-lineage A. m. iberiensis and A. m. mellifera, and the C-lineage A. m. carnica. In the A. m. iberiensis range, 92.6% of the colonies matched this subspecies, with a high median probability (0.919). In the Azores, where the Iberian subspecies was historically introduced, a lower proportion (85.7%) and probability (0.842) were observed. In the A. m mellifera range, only 41.1 % of the colonies matched this subspecies, which is compatible with a history of C-derived introgression. Yet, these colonies were classified with the highest probability (0.994) of the three subspecies. In the A. m. carnica range, 88.3% of the colonies matched this subspecies, with a probability of 0.984. The association between wing and molecular markers, assessed for 1214 colonies from the M-lineage range, was highly significant but not strong (r = 0.31, p < 0.0001). The agreement between the markers was influenced by C-derived introgression, with the best results obtained for colonies with high genetic integrity. This study indicates the good performance of DeepWings© on a realistic wing image dataset.

A genotyping by sequencing approach can disclose Apis mellifera population genomic information contained in honey environmental DNA

Awareness has been raised over the last years on the genetic integrity of autochthonous honey bee subspecies. Genomic tools available in Apis mellifera can make it possible to measure this information by targeting individual honey bee DNA. Honey contains DNA traces from all organisms that contributed or were involved in its production steps, including the honey bees of the colony. In this study, we designed and tested a genotyping by sequencing (GBS) assay to analyse single nucleotide polymorphisms (SNPs) of A. mellifera nuclear genome using environmental DNA extracted from honey. A total of 121 SNPs (97 SNPs informative for honey bee subspecies identification and 24 SNPs associated with relevant traits of the colonies) were used in the assay to genotype honey DNA, which derives from thousands of honey bees. Results were integrated with information derived from previous studies and whole genome resequencing datasets. This GBS method is highly reliable in estimating honey bee SNP allele frequencies of the whole colony from which the honey derived. This assay can be used to identify the honey bee subspecies of the colony that produced the honey and, in turn, to authenticate the entomological origin of the honey.

DeepWings©: Automatic Wing Geometric Morphometrics Classification of Honey Bee (Apis mellifera) Subspecies Using Deep Learning for Detecting Landmarks

Honey bee classification by wing geometric morphometrics entails the first step of manual annotation of 19 landmarks in the forewing vein junctions. This is a time-consuming and error-prone endeavor, with implications for classification accuracy. Herein, we developed a software called DeepWings© that overcomes this constraint in wing geometric morphometrics classification by automatically detecting the 19 landmarks on digital images of the right forewing. We used a database containing 7634 forewing images, including 1864 analyzed by F. Ruttner in the original delineation of 26 honey bee subspecies, to tune a convolutional neural network as a wing detector, a deep learning U-Net as a landmarks segmenter, and a support vector machine as a subspecies classifier. The implemented MobileNet wing detector was able to achieve a mAP of 0.975 and the landmarks segmenter was able to detect the 19 landmarks with 91.8% accuracy, with an average positional precision of 0.943 resemblance to manually annotated landmarks. The subspecies classifier, in turn, presented an average accuracy of 86.6% for 26 subspecies and 95.8% for a subset of five important subspecies. The final implementation of the system showed good speed performance, requiring only 14 s to process 10 images. DeepWings© is very user-friendly and is the first fully automated software, offered as a free Web service, for honey bee classification from wing geometric morphometrics. DeepWings© can be used for honey bee breeding, conservation, and even scientific purposes as it provides the coordinates of the landmarks in excel format, facilitating the work of research teams using classical identification approaches and alternative analytical tools.

The Movement of Western Honey Bees (Apis mellifera L.) Among United States and Territories: History, Benefits, Risks, and Mitigation Strategies

Beekeeping is a cornerstone activity that has led to the human-mediated, global spread of western honey bees (Apis mellifera L.) outside their native range of Europe, western Asia, and Africa. The exportation/importation of honey bees (i.e., transfer of honey bees or germplasm between countries) is regulated at the national level in many countries. Honey bees were first imported into the United States in the early 1600’s. Today, honey bee movement (i.e., transport of honey bees among states and territories) is regulated within the United States at the state, territory, and federal levels. At the federal level, honey bees present in the country (in any state or territory) can be moved among states and territories without federal restriction, with the exception of movement to Hawaii. In contrast, regulations at the state and territory levels vary substantially, ranging from no additional regulations beyond those stipulated at the federal level, to strict regulations for the introduction of live colonies, packaged bees, or queens. This variability can lead to inconsistencies in the application of regulations regarding the movement of honey bees among states and territories. In November 2020, we convened a technical working group (TWG), composed of academic and USDA personnel, to review and summarize the (1) history of honey bee importation into/movement within the United States, (2) current regulations regarding honey bee movement and case studies on the application of those regulations, (3) benefits associated with moving honey bees within the United States, (4) risks associated with moving honey bees within the United States, and (5) risk mitigation strategies. This review will be helpful for developing standardized best practices for the safe movement of honey bees between the 48 contiguous states and other states/territories within the United States.

Influence of model selection and data structure on the estimation of genetic parameters in honeybee populations

Estimating genetic parameters of quantitative traits is a prerequisite for animal breeding. In honeybees, the genetic variance separates into queen and worker effects. However, under data paucity, parameter estimations that account for this peculiarity often yield implausible results. Consequently, simplified models that attribute all genetic contributions to either the queen (queen model) or the workers (worker model) are often used to estimate variance components in honeybees. However, the causes for estimations with the complete model (colony model) to fail and the consequences of simplified models for variance estimates are little understood. We newly developed the necessary theory to compare parameter estimates that were achieved by the colony model with those of the queen and worker models. Furthermore, we performed computer simulations to quantify the influence of model choice, estimation algorithm, true genetic parameters, rates of controlled mating, apiary sizes, and phenotype data completeness on the success of genetic parameter estimations. We found that successful estimations with the colony model were only possible if at least some of the queens mated controlled on mating stations. In that case, estimates were largely unbiased if more than 20% of the colonies had phenotype records. The simplified queen and worker models proved more stable and yielded plausible parameter estimates for almost all settings. Results obtained from these models were unbiased when mating was uncontrolled, but with controlled mating, the simplified models consistently overestimated heritabilities. This study elucidates the requirements for variance component estimation in honeybees and provides the theoretical groundwork for simplified honeybee models.

Population Structure and Diversity in European Honey Bees (Apis mellifera L.)—An Empirical Comparison of Pool and Individual Whole-Genome Sequencing

Background: Whole-genome sequencing has become routine for population genetic studies. Sequencing of individuals provides maximal data but is rather expensive and fewer samples can be studied. In contrast, sequencing a pool of samples (pool-seq) can provide sufficient data, while presenting less of an economic challenge. Few studies have compared the two approaches to infer population genetic structure and diversity in real datasets. Here, we apply individual sequencing (ind-seq) and pool-seq to the study of Western honey bees (Apis mellifera). Methods: We collected honey bee workers that belonged to 14 populations, including 13 subspecies, totaling 1347 colonies, who were individually (139 individuals) and pool-sequenced (14 pools). We compared allele frequencies, genetic diversity estimates, and population structure as inferred by the two approaches. Results: Pool-seq and ind-seq revealed near identical population structure and genetic diversities, albeit at different costs. While pool-seq provides genome-wide polymorphism data at considerably lower costs, ind-seq can provide additional information, including the identification of population substructures, hybridization, or individual outliers. Conclusions: If costs are not the limiting factor, we recommend using ind-seq, as population genetic structure can be inferred similarly well, with the advantage gained from individual genetic information. Not least, it also significantly reduces the effort required for the collection of numerous samples and their further processing in the laboratory.

Diversity and Evolution of pogo and Tc1/mariner Transposons in the Apoidea Genomes

Bees (Apoidea), the largest and most crucial radiation of pollinators, play a vital role in the ecosystem balance. Transposons are widely distributed in nature and are important drivers of species diversity. However, transposons are rarely reported in important pollinators such as bees. Here, we surveyed 37 bee genomesin Apoidea, annotated the pogo and Tc1/mariner transposons in the genome of each species, and performed a phylogenetic analysis and determined their overall distribution. The pogo and Tc1/mariner families showed high diversity and low abundance in the 37 species, and their proportion was significantly higher in solitary bees than in social bees. DD34D/mariner was found to be distributed in almost all species and was found in Apis mellifera, Apis mellifera carnica, Apis mellifera caucasia, and Apis mellifera mellifera, and Euglossa dilemma may still be active. Using horizontal transfer analysis, we found that DD29-30D/Tigger may have experienced horizontal transfer (HT) events. The current study displayed the evolution profiles (including diversity, activity, and abundance) of the pogo and Tc1/mariner transposons across 37 species of Apoidea. Our data revealed their contributions to the genomic variations across these species and facilitated in understanding of the genome evolution of this lineage.

MtDNA Analysis Indicates Human-Induced Temporal Changes of Serbian Honey Bees Diversity

Simple Summary

The western honey bee is one of the most economically and ecologically important species currently facing serious challenges in its whole area of distribution. The honey bee is a highly diverse species with about 30 subspecies that are adapted to regional climate factors, vegetation, pests and pathogens. The local populations of honey bees are rapidly changing and their diversity is constantly manipulated by beekeepers through the import of foreign queens, selection and migratory beekeeping. This manipulation may lead to such changes that honey bees lose their ability to thrive in the areas that were previously suitable for their wellbeing. To see how this human interference changed the genetic variability of native honey bee populations from Serbia, we sequenced part of the mitochondrial genome and compared them with published sequences. Our results suggest that human influence significantly changes the natural composition of honey bees in Serbia and that the presence of some previously reported subspecies could not be confirmed.

Abstract

Local populations of Apis mellifera are rapidly changing by modern beekeeping through the introduction of nonnative queens, selection and migratory beekeeping. To assess the genetic diversity of contemporary managed honey bees in Serbia, we sequenced mitochondrial tRNA^leu-cox2 intergenic region of 241 worker bees from 46 apiaries at eight localities. Nine haplotypes were observed in our samples, with C2d being the most common and widespread. To evaluate genetic diversity patterns, we compared our data with 1696 sequences from the NCBI GenBank from neighbouring countries and Serbia. All 32 detected haplotypes belonged to the Southeast Europe lineage C, with two newly described haplotypes from our sample. The most frequent haplotype was C2d, followed by C2c and C1a. To distinguish A. m. carnica from A. m. macedonica, both previously reported in Serbia, PCR-RFLP analysis on the COI gene segment of mtDNA was used, and the result showed only the presence of A.m. carnica subspecies. An MDS plot constructed on pairwise F_ST values showed significant geographical stratification. Our samples are grouped together, but distant from the Serbian dataset from the GenBank. This, with the absence of A. m. macedonica subspecies from its historic range of distribution in southern Serbia, indicates that honey bee populations are changing rapidly due to the anthropogenic influence.

A SNP assay for assessing diversity in immune genes in the honey bee (Apis mellifera L.)

With a growing number of parasites and pathogens experiencing large-scale range expansions, monitoring diversity in immune genes of host populations has never been so important because it can inform on the adaptive potential to resist the invaders. Population surveys of immune genes are becoming common in many organisms, yet they are missing in the honey bee (Apis mellifera L.), a key managed pollinator species that has been severely affected by biological invasions. To fill the gap, here we identified single nucleotide polymorphisms (SNPs) in a wide range of honey bee immune genes and developed a medium-density assay targeting a subset of these genes. Using a discovery panel of 123 whole-genomes, representing seven A. mellifera subspecies and three evolutionary lineages, 180 immune genes were scanned for SNPs in exons, introns (< 4 bp from exons), 3' and 5´UTR, and < 1 kb upstream of the transcription start site. After application of multiple filtering criteria and validation, the final medium-density assay combines 91 quality-proved functional SNPs marking 89 innate immune genes and these can be readily typed using the high-sample-throughput iPLEX MassARRAY system. This medium-density-SNP assay was applied to 156 samples from four countries and the admixture analysis clustered the samples according to their lineage and subspecies, suggesting that honey bee ancestry can be delineated from functional variation. In addition to allowing analysis of immunogenetic variation, this newly-developed SNP assay can be used for inferring genetic structure and admixture in the honey bee.

Historical Changes in Honey Bee Wing Venation in Romania

Simple Summary

Honey bees, in addition to producing honey, are important pollinators of wild and cultivated plants. Unfortunately, in some places, the population of honey bees is declining. One of the factors that affect their survival is adaptation to the local environment. Bees native to a particular area survive better than those imported. Despite this fact, some beekeepers import non-native bees and use them in their apiaries. Imported bees produce hybrids with bees from surrounding colonies because beekeepers do not control their mating. In consequence, the whole population can change. In this study, we verified how the population of Romanian bees has changed over the last four decades. We found significant temporal changes in wing venation. Despite these changes, the two major subpopulations of bees separated by mountains remain distinct. We provide a tool for the easy identification of native bees from Romania, which can help to protect them.

Abstract

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.

Gray level co-occurrence matrix and extreme learning machine for Covid-19 diagnosis

Background

Chest CT is considered to be a more accurate method for diagnosing suspected patients. However, with the spread of the epidemic, traditional diagnostic methods have been unable to meet the requirements of efficiency and speed. Therefore, it is necessary to use artificial intelligence to help people make efficient and accurate judgments. A number of studies have shown that it is feasible to use deep learning methods to help people diagnose COVID-19. However, most of the existing methods are single-layer neural network structures, and their accuracy and efficiency need to be improved.

Method

In this scheme, a hybrid model is adopted. Firstly, the gray co-occurrence matrix is used to extract the features of the images, and then the extreme learning machine is used for classification.

Results

The experimental results show that the model proposed in this paper is feasible and can help medical staff to accurately determine suspected patients for subsequent isolation and treatment.

A systems-based approach to the environmental risk assessment of multiple stressors in honey bees

The European Parliament requested EFSA to develop a holistic risk assessment of multiple stressors in honey bees. To this end, a systems-based approach that is composed of two core components: a monitoring system and a modelling system are put forward with honey bees taken as a showcase. Key developments in the current scientific opinion (including systematic data collection from sentinel beehives and an agent-based simulation) have the potential to substantially contribute to future development of environmental risk assessments of multiple stressors at larger spatial and temporal scales. For the monitoring, sentinel hives would be placed across representative climatic zones and landscapes in the EU and connected to a platform for data storage and analysis. Data on bee health status, chemical residues and the immediate or broader landscape around the hives would be collected in a harmonised and standardised manner, and would be used to inform stakeholders, and the modelling system, ApisRAM, which simulates as accurately as possible a honey bee colony. ApisRAM would be calibrated and continuously updated with incoming monitoring data and emerging scientific knowledge from research. It will be a supportive tool for beekeeping, farming, research, risk assessment and risk management, and it will benefit the wider society. A societal outlook on the proposed approach is included and this was conducted with targeted social science research with 64 beekeepers from eight EU Member States and with members of the EU Bee Partnership. Gaps and opportunities are identified to further implement the approach. Conclusions and recommendations are made on a way forward, both for the application of the approach and its use in a broader context.