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393 results for “honey bees”
Data from: Floral bagging differentially affects handling behaviors and single-visit pollen deposition by honey bees and native bees
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Bioinformatic pipeline: Vast differences in strain-level diversity in the gut microbiota of two closely related honey bee species
<p>This data-set contains the full bioinformatic pipeline used to analyze metagenomic samples in the study "Vast differences in strain-level diversity in the gut microbiota of two closely related honey bee species" (Ellegaard et al. 2020, Current Biology). </p> <p>New metagenomic samples were generated for the study, for which the raw data is available on the NCBI Sequence Read Achive, under accession: PRJNA59809.</p> <p>The data of this submission consist of 9 tar-balls, as further described here below. Download and unpack to view the contents (tar -zxvf filename.tar.gz). For each tarball, all directories contain README.txt files, describing the contents of the directory. Due to size constraints, some intermediate files have been omitted, and some workflows are demonstrated for a subset of the data. However, the full analysis can be reproduced from the raw data, using the provided scripts.</p> <p>All scripts are included within the directories where they were applied. Perl-scripts contain documentation, which can be viewed by typing: "perl script_name.pl -h". For R scripts, the usage is indicated as a comment in the top lines of each script. Note that many of the scripts require specific input-files to be present in the run-directory. Their usage is demonstrated within the workflow directories in bash-scripts (*.sh). Commands used for generating plots and some statistics are given within workflow directories in text-files "R.commands" when applicable.</p> <p>Aside from custom code, the pipeline also utilizes various open-source Software packages, which are detailed in the file "software_dependencies.txt". Note, while many of the scripts will run fast on any computer, some steps of the pipeline are computationally demanding, and will require significant computing time, as well as storage space. When scripts are known to be time-consuming, this is indicated in the script help message.</p> <p>Description of tarballs.</p> <p>raw_data_processing.tar.gz: Describes the quality-control and trimming of raw data, and includes info on the sequencing run.</p> <p>databases.tar.gz: Contains all databases used for analysis, in addition to relevant meta-data.</p> <p>mapping_stats.tar.gz: Contains a file with the number of reads mapped to the honey bee gut microbiota database and the host genomes, for each sample. Bash-scripts are provided, detailing how the mapping was done and quantified.</p> <p>orthologs_phylogenies.tar.gz: Contains the pipeline for inferring orthologous gene-families and core genome phylogenies, as well as scripts for filtering of single-copy core gene families.</p> <p>assemblies.tar.gz: Contains the final de novo metagenome assembly files (contig fasta-files), gener<br> ated for both complete and rarefied read subsets. Bash-scripts detailing the assembly commands are also provided.</p> <p>SDP_validation.tar.gz: Contains the pipeline for metagenomic validation of candidate SDPs. Final output-files, containing the percentage identity of recruited metagenomic ORFs to database core genes, are provided for each candidate SDP. Additionally, a small example dataset is provided, where the intermediate result-files can be viewed.</p> <p>community_profiling.tar.gz: Contains the pipeline for community profiling, i.e. the quantification of individual community members (SDPs) across samples. Final output files are provided, including mapped read coverage on core gene families and corresponding plots. A small bam-file (containing data from a single subset sample), is also provided, in order to demonstrate the pipeline, together with all scripts used.</p> <p>snv_profiling.tar.gz: Contains the pipeline used for SNV profiling, including filtering and analysis. Final filtered vcf-files are provided for each SDP. Analytical output files are also provided, including data on shared SNV fractions, distance matrices, and cumulative curves.</p> <p>metagenomic_ORF_analyses.tar.gz: Contains the pipeline for analysis of metagenomic ORFs. This includes prediction of ORFs, clustering, annotation and functional characterization. ORF sequences, annotation files, and cluster-files are provided.</p>
Genomic regions influencing aggressive behavior in honey bees are defined by colony allele frequencies
For social animals, the genotypes of group members affect the social environment, and thus individual behavior, often indirectly. We used genome-wide association studies (GWAS) to determine the influence of individual vs. group genotypes on aggression in honey bees. Aggression in honey bees arises from the coordinated actions of colony members, primarily nonreproductive "soldier" bees, and thus, experiences evolutionary selection at the colony level. Here, we show that individual behavior is influenced by colony environment, which in turn, is shaped by allele frequency within colonies. Using a population with a range of aggression, we sequenced individual whole genomes and looked for genotype–behavior associations within colonies in a common environment. There were no significant correlations between individual aggression and specific alleles. By contrast, we found strong correlations between colony aggression and the frequencies of specific alleles within colonies, despite a small number of colonies. Associations at the colony level were highly significant and were very similar among both soldiers and foragers, but they covaried with one another. One strongly significant association peak, containing an ortholog of the Drosophila sensory gene dpr4 on linkage group (chromosome) 7, showed strong signals of both selection and admixture during the evolution of gentleness in a honey bee population. We thus found links between colony genetics and group behavior and also, molecular evidence for group-level selection, acting at the colony level. We conclude that group genetics dominates individual genetics in determining the fatal decision of honey bees to sting.
Tool for genomic selection and breeding to evolutionary adaptation: Development of a 100K single nucleotide polymorphism array for the honey bee
High-throughput high density genotyping arrays continue to be a fast, accurate, and cost-effective method for genotyping thousands of polymorphisms in high numbers of individuals. Here we have developed a new high-density SNP genotyping array (103 270 SNPs) for honey bees, one of the most ecologically and economically important pollinators worldwide. SNPs were detected by conducting whole genome resequencing of 61 honey bee drones (haploid males) from throughout Europe. Selection of SNPs for the chip was done in multiple steps using several criteria. The majority of SNPs were selected based on their location within known candidate regions or genes underlying a range of honey bee traits, including hygienic behaviour against pathogens, foraging and subspecies. Additionally, markers from a GWAS of hygienic behaviour against the major honey bee parasite Varroa destructor, were brought over. The chip also includes SNPs associated with each of three major breeding objectives - honey yield, gentleness and Varroa resistance. We validated the chip and make recommendations for its use by determining error rates in repeat genotypings, examining the genotyping performance of different tissues, and by testing how well different sample types represent the queen's genotype. The latter is a key test because it is highly beneficial to be able to determine the queen's genotype by non-lethal means. The array is now publicly available and we suggest it will be a useful tool in genomic selection and honey bee breeding, as well as for GWAS of different traits, and for population genomic, adaptation and conservation questions.
Honey bee lifespan: the critical role of pre-foraging stage
<p><span>Assessing the various anthropogenic pressures imposed on honey bees requires characterizing the patterns and drivers of natural mortality. Using automated life-long individual monitoring devices, we monitored worker bees in different geographical, seasonal and colony contexts creating a broad range of hive conditions. We measured their life-history traits and notably assessed whether lifespan is influenced by pre-foraging flight experience. Our results show that the age at the first flight and onset of foraging are critical factors that determine, to a large extent, lifespan. Most importantly, our results indicate that a large proportion (40%) of the bees die during pre-foraging stage, and for those surviving, the elapsed time and flight experience between the first flight and the onset of foraging is of paramount importance to maximize the number of days spent foraging. Once in the foraging stage, individuals experience a constant mortality risk of 9% and 36% per hour of foraging and per foraging day, respectively. In conclusion, the pre-foraging stage during which bees perform orientation flights is a critical driver of bee lifespan. We believe these data on the natural mortality risks in honey bee workers will help assess the impact of anthropogenic pressures on bees.</span></p>
Data from: Antibiotics in hives and their effects on honey bee physiology and behavioral development
<p>Recurrent honey bee losses make it critical to understand the impact of human interventions, such as antibiotics use in apiculture. Antibiotics are used to prevent or treat bacterial infections in colonies. However, little is known about their effects on honey bee development. We studied the effect of two commercial beekeeping antibiotics on the bee physiology and behavior throughout development. Our results show that antibiotic treatments have an effect on amount of lipids and rate of behavioral development. Lipid amount in treated bees was higher than those not treated. Also, the timing of antibiotic treatment had distinct effects for the age of onset of behaviors starting with cleaning, then nursing and lastly foraging. Bees treated during larva-pupa stages demonstrated an accelerated behavioral development and loss of lipids, while bees treated from larva to adulthood had a delay in behavioral development and loss of lipids. The effects were shared across the two antibiotics tested, Terramycin<sup>R</sup> (oxytetracycline) and Tylan<sup>R</sup> (tylosin tartrate). These results on effects of antibiotic treatments suggest a role of microbiota in the interaction between the fat body and brain that is important for honey bee behavioral development.</p>
Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation
<p>This excel file (DOI: <a href="https://doi.org/10.5281/zenodo.3755675">https://doi.org/10.5281/zenodo.3755675</a>) provides the collection of raw data used for developing the first integrative Quantitative Structure-Activity Relationship (QSAR) model using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD<sub>50</sub>) and ii) to profile the Mode of Action (MoA) of pesticides active substances in honey bees (<em>Apis mellifera</em>)<em>. </em>Chemical identifiers (e.g. SMILES, CAS n., InChI) and acute contact toxicity data (LD<sub>50</sub>) on honey bees were used to develop and validate i) a two-category QSAR model (toxic/non-toxic; n=411) (sensitivity =0.93), specificity =0.85), balanced accuracy =0.90), Matthews correlation coefficient MCC=0.78), and ii) a regression-based model (n=113) (R2=0.74; MAE=0.52). Similarly, current study proposes the first MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD<sub>50s</sub> data points from three different databases such as EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase. Such classification allows to further define MoAs and the target site of Plant Protection Products (PPPs) active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals.</p> <p>The full data collection and analysis of QSAR models, toxicity data (LD<sub>50</sub>) and Mode of Action (Moa) data are described in Carnesecchi et al., 2020 (DOI: doi.org/10.1016/j.scitotenv.2020.139243).</p> <p>This work was supported by the European Food Safety Authority (EFSA) [contract number: OC/EFSA/SCER/2018/01 and NP/EFSA/AFSCO/2016/02 (Edoardo Carnesecchi)].</p>
Screening and microsatellite data for Varroa infesting resistant honey bee pupae
<p>We investigated how the evolution of host resistance could affect the infesting population of Varroa mites. We screened a Varroa-resistant honey bee population near Toulouse, France, for a Varroa resistance trait: the inhibition of Varroa's reproduction in drone pupae. We then genotyped Varroa which had co-infested a cell using microsatellites. Across all resistant honey bee colonies, Varroa's reproductive success was significantly higher in co-infested cells but the distribution of Varroa between singly and multiply infested cells was not different from random. While there was a trend for increased reproductive success when Varroa of differing haplotypes co-infested a cell, this was not significant. This dataset may be useful to anybody interested in the evolution of host-resistance on the parasitic mite Varroa destructor. There were no legal or ethical considerations associated with this study.</p>
Interaction of field realistic doses of clothianidin and Varroa destructor parasitism on adult honey bee (Apis mellifera L.) health and neural gene expression, and antagonistic effects on differentially expressed genes
<p>While many studies have examined the effects of neonicotinoid insecticides and the parasitic mite <em>Varroa destructor</em> on honey bees (<em>Apis mellifera</em>), more information on the combined effects of such stressors on gene expression, including neural related genes, and their impact on biological pathways is needed. This study analyzed the effects of field realistic concentrations of the neonicotinoid clothianidin on adult bees infested and not infested with <em>V</em>. <em>destructor</em> over 21 consecutive days and then determined bee survivorship, weight, deformed wing virus (DWV) levels and gene expression. <em>V</em>. <em>destructor</em> parasitism with or without clothianidin exposure was significantly associated with decreased survivorship, weight loss and higher DWV levels, while clothianidin exposure was only associated with higher levels of DWV. Expression analysis of the neural genes <em>AmNlg-1</em>, <em>BlCh</em> and <em>AmAChE-2</em> showed that <em>V</em>. <em>destructor</em> caused a significant down-regulation of all of them, whereas clothianidin caused a significant down-regulation of only <em>AmNrx-1</em> and <em>BlCh</em>. An interaction was only detected for <em>AmNrx-1</em> expression. RNAseq analysis showed that clothianidin exposure resulted in 6.5 times more up-regulated differentially expressed genes (DEGs) than <em>V</em>. <em>destructor</em> alone and 123 times more than clothianidin combined with <em>V</em>. <em>destructor</em>. Similar results were obtained with down-regulated DEGs, except for a higher number of DEGs shared between <em>V</em>. <em>destructor</em> and the combined stressors. KEGG (Kyoto Encyclopedia of Genes and Genomes) biological pathway analysis of the DEGs showed that the stressor linked to the highest number of KEGG pathways was clothianidin, followed by <em>V</em>. <em>destructor</em>, and then considerably fewer number of KEGG pathways with the combined stressors. The reduced numbers of DEGs and KEGG pathways associated with the DEGs for the combined stressors compared to the stressors alone indicates that the interaction of the stressors is not additive or synergistic, but antagonistic. The possible implications of the antagonistic effect on the number of DEGs are discussed.</p>
Data from: Genetic architecture of a hormonal response to gene knockdown in honey bees
Variation in endocrine signaling is proposed to underlie the evolution and regulation of social life histories, but the genetic architecture of endocrine signaling is still poorly understood. An excellent example of a hormonally influenced set of social traits is found in the honey bee (Apis mellifera): a dynamic and mutually suppressive relationship between juvenile hormone (JH) and the yolk precursor protein vitellogenin (Vg) regulates behavioral maturation and foraging of workers. Several other traits cosegregate with these behavioral phenotypes, comprising the pollen hoarding syndrome (PHS) one of the best-described animal behavioral syndromes. Genotype differences in responsiveness of JH to Vg are a potential mechanistic basis for the PHS. Here, we reduced Vg expression via RNA interference in progeny from a backcross between 2 selected lines of honey bees that differ in JH responsiveness to Vg reduction and measured JH response and ovary size, which represents another key aspect of the PHS. Genetic mapping based on restriction site-associated DNA tag sequencing identified suggestive quantitative trait loci (QTL) for ovary size and JH responsiveness. We confirmed genetic effects on both traits near many QTL that had been identified previously for their effect on various PHS traits. Thus, our results support a role for endocrine control of complex traits at a genetic level. Furthermore, this first example of a genetic map of a hormonal response to gene knockdown in a social insect helps to refine the genetic understanding of complex behaviors and the physiology that may underlie behavioral control in general.
Data from: Interacting stressors matter: diet quality and virus infection in honey bee health
Honey bee population declines have been linked to multiple stressors, including reduced diet diversity and increased exposure to understudied viral pathogens. Despite interest in these factors, few experimental studies explored the interaction between diet diversity and viral infection in honey bees. Here, we used a mixture of laboratory cage and small semi-field nucleus hive experiments to determine how these factors interact. We found that high quality diets (polyfloral pollen and high quality single-source pollen) have the potential to reduce mortality in the face of infection with Israeli acute paralysis virus (IAPV).). There was a significant interaction between diet and virus infection on mortality, even in the presence of high virus titers, suggesting that good diets can help bees tolerate virus infection. Further, we found that extreme stress in the form of pollen starvation in conjunction with IAPV infection increase exiting behavior from small experimental hives. Finally, we showed that higher quality pollen diets have significantly higher iron and calcium content, suggesting micronutrient deficiencies could be an under explored area of bee nutrition.
Data from: Honey bee colonies headed by hyperpolyandrous queens have improved brood rearing efficiency and lower infestation rates of parasitic Varroa mites
A honey bee queen mates on wing with an average of 12 males and stores their sperm to produce progeny of mixed paternity. The degree of a queen's polyandry is positively associated with measures of her colony's fitness, and observed distributions of mating number are evolutionary optima balancing risks of mating flights against benefits to the colony. Effective mating numbers as high as 40 have been documented, begging the question of the upper bounds of this behavior that can be expected to confer colony benefit. In this study we used instrumental insemination to create three classes of queens with exaggerated range of polyandry– 15, 30, or 60 drones. Colonies headed by queens inseminated with 30 or 60 drones produced more brood per bee and had a lower proportion of samples positive for Varroa destructor mites than colonies whose queens were inseminated with 15 drones, suggesting benefits of polyandry at rates higher than those normally obtaining in nature. Our results are consistent with two hypotheses that posit conditions that reward such high expressions of polyandry: (1) a queen may mate with many males in order to promote beneficial non-additive genetic interactions among subfamilies, and (2) a queen may mate with many males in order to capture a large number of rare alleles that regulate resistance to pathogens and parasites in a breeding population. Our results are unique for identifying the highest levels of polyandry yet detected that confer colony-level benefit and for showing a benefit of polyandry in particular toward the parasitic mite V. destructor.
Egg-size plasticity in Apis mellifera: honey bee queens alter egg size in response to both genetic and environmental factors
<p>Social evolution has led to distinct life-history patterns in social insects, but many colony-level and individual traits, such as egg size, are not sufficiently understood. Thus, a series of experiments was performed to study the effects of genotypes, colony size, and colony nutrition on variation in egg size produced by honey bee (<i>Apis</i> <i>mellifera</i>) queens. Queens from different genetic stocks produced significantly different egg sizes under similar environmental conditions, indicating standing genetic variation for egg size that allows for adaptive evolutionary change. Further investigations revealed that eggs produced by queens in large colonies were consistently smaller than eggs produced in small colonies, and queens dynamically adjusted egg size in relation to colony size. Similarly, queens increased egg size in response to food deprivation. These results could not be solely explained by different numbers of eggs produced in the different circumstances but instead seem to reflect an active adjustment of resource allocation by the queen in response to colony conditions. As a result, larger eggs experienced higher subsequent survival than smaller eggs, suggesting that honey bee queens might increase egg size under unfavorable conditions to enhance brood survival, and to minimize costly brood care of eggs that fail to successfully develop, and thus conserve energy at the colony level. The extensive plasticity and genetic variation of egg size in honey bees has important implications for understanding life history evolution in a social context and implies this neglected life history stage in honey bees may have trans-generational effects.</p>
Data from: Netted crop covers reduce honey bee foraging activity and colony strength in a mass flowering crop
The widespread use of protective covers in horticulture represents a novel landscape-level change, presenting challenges for crop pollination. Honey bees (Apis mellifera L) are pollinators of many crops, but their behaviour can be affected by conditions under covers. To determine how netting crop covers can affect honey bee foraging dynamics, colony health, and pollination services, we assessed the performance of 52 nucleus honey bee colonies in five covered and six uncovered kiwifruit orchards. Colony strength was estimated pre- and post- introduction, and the foraging of individual bees (including pollen-, nectar-, and naïve foragers) was monitored in a subset of the hives fitted with RFID readers. Simultaneously, we evaluated pollination effectiveness by measuring flower visitation rates and the number of seeds produced after single honey bee visits. Honey bee colonies under cover exhibited both an acute loss of foragers and changes in the behaviour of successful foragers. Under cover, bees were four times less likely to return after their first trip outside the hive. Consequently, the number of adult bees in hives declined at a faster rate in these orchards, with colonies losing on average 1,057 ± 274 of their bees in under two weeks. Bees that did forage under cover completed fewer trips provisioning their colony, failing to re-enter after a few short-duration trips. These effects are likely to have implications for colony health and productivity. We also found that bee density (bees/thousand flowers) and visitation rates to flowers were lower under cover, however we did not detect a resultant change in pollination. Our findings highlight the need for environment-specific management techniques for pollinators. Improving honey bee orientation under covers and increasing our understanding of the effects of covers on bee nutrition and brood rearing should be primary objectives for maintaining colonies and potentially improving pollination in these systems.
Data from: Application of ITS2 metabarcoding to determine the provenance of pollen collected by honey bees in an agroecosystem
Premise of the study: Melissopalynology, the identification of bee-collected pollen, provides insight into the flowers exploited by foraging bees. Information provided by melissopalynology could guide floral enrichment efforts aimed at supporting pollinators, but it has rarely been used because traditional methods of pollen identification are laborious and require expert knowledge. We approach melissopalynology in a novel way, employing a molecular method to study the pollen foraging of honey bees (Apis mellifera) in a landscape dominated by field crops, and compare these results to those obtained by microscopic melissopalynology. Methods: Pollen was collected from honey bee colonies in Madison County, Ohio, USA, during a two-week period in mid-spring and identified using microscopic methods and ITS2 metabarcoding. Results: Metabarcoding identified 19 plant families and exhibited sensitivity for identifying the taxa present in large and diverse pollen samples relative to microscopy, which identified eight families. The bulk of pollen collected by honey bees was from trees (Sapindaceae, Oleaceae, and Rosaceae), although dandelion (Taraxacum officinale) and mustard (Brassicaceae) pollen were also abundant. Discussion: For quantitative analysis of pollen, using both metabarcoding and microscopic identification is superior to either individual method. For qualitative analysis, ITS2 metabarcoding is superior, providing heightened sensitivity and genus-level resolution.
Data from: Chronic exposure to neonicotinoids reduces honey bee health near corn crops
Experiments linking neonicotinoids and declining bee health have been criticized for not simulating realistic exposure. Here we quantified the duration and magnitude of neonicotinoid exposure in Canada's corn-growing regions and used these data to design realistic experiments to investigate the effect of such insecticides on honey bees. Colonies near corn were naturally exposed to neonicotinoids for up to 4 months—the majority of the honey bee's active season. Realistic experiments showed that neonicotinoids increased worker mortality and were associated with declines in social immunity and increased queenlessness over time. We also discovered that the acute toxicity of neonicotinoids to honey bees doubles in the presence of a commonly encountered fungicide. Our work demonstrates that field-realistic exposure to neonicotinoids can reduce honey bee health in corn-growing regions.
Data from: Lethal and sublethal synergistic effects of a new systemic pesticide, flupyradifurone (Sivanto®) on honey bees
The honey bee (Apis mellifera L.) is an important pollinator and a model for pesticide effects on insect pollinators. The effects of agricultural pesticides on honey bee health have therefore raised concern. Bees can be exposed to multiple pesticides that may interact synergistically, amplifying their side-effects. Attention has focused on neonicotinoid pesticides, but flupyradifurone (FPF) is a novel butenolide insecticide that is also systemic and a nicotinic acetylcholine receptor (nAChR) agonist. We therefore tested the lethal and sublethal toxic effects of FPF over different seasons and worker types, and the interaction of FPF with a common SBI fungicide, propiconazole. We provide the first demonstration of adverse synergistic effects on bee survival and behaviour (poor coordination, hyperactivity, apathy) at field-realistic doses. Pesticide effects were significantly influenced by worker type and season. Foragers were consistently more susceptible to these pesticides (4-fold greater effect) than in-hive bees, and both worker types were more strongly affected by FPF in summer as compared to spring. Because risk assessment requires relatively limited tests that only marginally address bee behaviour and do not consider the influence of bee age and season, our results raise concerns about the safety of approved pesticides, including FPF. We suggest that pesticide risk assessment also test for common chemical mixture synergies on behaviour and survival.
Data from: Signatures of selection in the Iberian honey bee (Apis mellifera iberiensis) revealed by a genome scan analysis of single nucleotide polymorphisms
Understanding the genetic mechanisms of adaptive population divergence is one of the most fundamental endeavours in evolutionary biology and is becoming increasingly important as it will allow predictions about how organisms will respond to global environmental crisis. This is particularly important for the honey bee, a species of unquestionable ecological and economical importance that has been exposed to increasing human-mediated selection pressures. Here, we conducted a single nucleotide polymorphism (SNP)-based genome scan in honey bees collected across an environmental gradient in Iberia and used four FST-based outlier tests to identify genomic regions exhibiting signatures of selection. Additionally, we analysed associations between genetic and environmental data for the identification of factors that might be correlated or act as selective pressures. With these approaches, 4.4% (17 of 383) of outlier loci were cross-validated by four FST-based methods, and 8.9% (34 of 383) were cross-validated by at least three methods. Of the 34 outliers, 15 were found to be strongly associated with one or more environmental variables. Further support for selection, provided by functional genomic information, was particularly compelling for SNP outliers mapped to different genes putatively involved in the same function such as vision, xenobiotic detoxification and innate immune response. This study enabled a more rigorous consideration of selection as the underlying cause of diversity patterns in Iberian honey bees, representing an important first step towards the identification of polymorphisms implicated in local adaptation and possibly in response to recent human-mediated environmental changes.
Data from: Planting of neonicotinoid-treated maize poses risks for honey bees and other non-target organisms over a wide area without consistent crop yield benefit
Neonicotinoid insecticides are routinely used as seed treatments on most grain and oilseed crops in the USA, yet the extent and likelihood of spread of insecticide residues during planting has not previously been quantified. Honey bees, Apis mellifera, are highly mobile and highly sensitive to neonicotinoid residues, presenting an opportunity to estimate non-target exposures to neonicotinoids in mobile insects. We measured neonicotinoid dust drift during maize sowing and used sites of maize fields, apiary locations and honey bee foraging radii to estimate likelihood of forager exposure. We performed a concurrent multi-year field assessment of the pest management benefits of neonicotinoid-treated maize. Our results indicate that over 94% of honey bee foragers throughout the state of Indiana are at risk of exposure to varying levels of neonicotinoid insecticides, including lethal levels, during sowing of maize. We documented no benefit of the insecticidal seed treatments for crop yield during the study. Synthesis and applications. We demonstrate movement of neonicotinoid residues well beyond planted fields occurs during maize sowing in Indiana. Based on locations of maize fields and apiaries in the state, the likelihood of neonicotinoid exposure for foraging honey bees is high. Other non-target organisms are also likely to encounter neonicotinoid residues; we conservatively estimate that deposition of neonicotinoid residues on non-target lands and waterways will occur on over 42% of the state of Indiana during the period of maize sowing. However, we also demonstrate that the risk to pollinators and other non-target organisms may be rapidly and dramatically reduced without yield penalties, by aligning use rates of neonicotinoid insecticides with pest incidence.
Data from: Seasonal dynamics and co-occurrence patterns of honey bee pathogens revealed by high-throughput RT-qPCR analysis
The health of the honey bee Apis mellifera is challenged by introduced parasites that interact with its inherent pathogens and cause elevated rates of colony losses. To elucidate co-occurrence, population dynamics and synergistic interactions of honey bee pathogens, we established an array of diagnostic assays for a high-throughput qPCR platform. Assuming that interaction of pathogens requires co-occurrence within the same individual, single worker bees were analyzed instead of collective samples. Eleven viruses, four parasites and three pathogenic bacteria were quantified in more than one thousand single bees sampled from sixteen disease-free apiaries in Southwest Germany. The most abundant viruses were Black Queen Cell Virus (84%), Lake Sinai Virus 1 (42%), and Deformed Wing Virus B (35%). Forager bees from asymptomatic colonies were infected with two different viruses in average, and simultaneous infection with four to six viruses was common (14%). Also the intestinal parasites Nosema ceranae (96%) and Crithidia mellificae/Lotmaria passim (52%) occurred very frequently. These results indicate that low-level infections in honey bees are more common than previously assumed. All viruses showed seasonal variation, while N. ceranae did not. The foulbrood bacteria Paenibacillus larvae and Melissococcus plutonius were regionally distributed. Spearman's correlations and multiple regression analysis indicated possible synergistic interactions between the common pathogens, particularly for Black Queen Cell Virus. Beyond its suitability for further studies on honey bees, this targeted approach may be, due to its precision, capacity and flexibility, a viable alternative to more expensive, sequencing-based approaches in non-model systems.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.