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393 results for “honey bees”
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees
<p>We present a one-year-long <strong>M</strong>ulti-<strong>S</strong>ensor dataset with <strong>P</strong>henotypic trait measurements from honey <strong>B</strong>ees (MSPB). Data were continuously collected between April-2020 and April-2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), <em>Varroa</em> destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, which facilitate a broader scope of analysis on honey bees, such as bee acoustics analysis, multi-modal hive monitoring, queen presence detection, <em>Varroa </em>infection detection, hive population estimation, biological analysis of bees, etc.</p> <h3>Related Info</h3> <p>The data collection process, feature pre-processing, preliminary data analysis, and usage notes can be found in our paper <a href="https://arxiv.org/abs/2311.10876">https://arxiv.org/abs/2311.10876</a></p> <p>Check the project webpage (<a href="https://zhu00121.github.io/MSPB-webpage/">https://zhu00121.github.io/MSPB-webpage/</a>) and Github repo (<a href="https://github.com/MuSAELab/MSPB">https://github.com/MuSAELab/MSPB</a>) for more information.</p> <h3>Citation</h3> <p>Kindly cite the following paper:</p> <p>@misc{zhu2023mspb,</p> <p> title={MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees}, </p> <p> author={Yi Zhu and Mahsa Abdollahi and Ségolène Maucourt and Nico Coallier and Heitor R. Guimarães and Pierre Giovenazzo and Tiago H. Falk},</p> <p> year={2023},</p> <p> eprint={2311.10876},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={eess.AS}</p> <p>}</p> <h3>Contact</h3> <p>You can contact us at Yi.Zhu@inrs.ca, if you encounter any questions accessing the data.</p>
Data from: Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones
<p>This vcf file contains 7.023.689 SNPs and 870 honey bee samples, as described in the paper "Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones" by Wragg et al., available at https://doi.org/10.1101/2021.09.20.460798 as preprint.</p> <p>Eight hundred and seventy haploid drone samples from several honey bee subspecies hybrids were sequenced and aligned to the HAv3.1 reference genome. Sequence read alignment and genotyping quality filters were used to obtain a selection of 7.023.689 high-quality SNPs. The file Diversity_Study_629_Samples.txt corresponds to the 629 unique samples that were used for the diversity study described in the paper and can be used to recreate the restricted diversity dataset using bcftools or an equivalent software.</p> <p>Having sequenced haploid drones, heterozygous SNPs resulting from duplicated regions could be filtered out and the data is phased.</p>
Data used in Machine learning reveals the waggle drift's role in the honey bee dance communication system
<p><strong>Data and metadata used in "Machine learning reveals the waggle drift’s role in the honey bee dance communication system" </strong></p> <p>All timestamps are given in ISO 8601 format.</p> <p><strong>The following files are included:</strong></p> <p><strong>Berlin2019_waggle_phases.csv, Berlin2021_waggle_phases.csv</strong></p> <p>Automatic individual detections of waggle phases during our recording periods in 2019 and 2021.</p> <ul> <li> <p>timestamp: Date and time of the detection.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>x_median, y_median: Median position of the bee during the waggle phase (for 2019 given in millimeters after applying a homography, for 2021 in the original image coordinates).</p> </li> <li> <p>waggle_angle: Body orientation of the bee during the waggle phase in radians (0: oriented to the right, PI / 4: oriented upwards).</p> </li> </ul> <p><strong>Berlin2019_dances.csv</strong></p> <p>Automatic detections of dance behavior during our recording period in 2019.</p> <ul> <li> <p>dancer_id: Unique ID of the individual bee.</p> </li> <li> <p>dance_id: Unique ID of the dance.</p> </li> <li> <p>ts_from, ts_to: Date and time of the beginning and end of the dance.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>median_x, median_y: Median position of the individual during the dance.</p> </li> <li> <p>feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.</p> </li> </ul> <p><strong>Berlin2019_followers.csv</strong></p> <p>Automatic detections of attendance and following behavior, corresponding to the dances in Berlin2019_dances.csv.</p> <ul> <li> <p>dance_id: Unique ID of the dance being attended or followed.</p> </li> <li> <p>follower_id: Unique ID of the individual attending or following the dance.</p> </li> <li> <p>ts_from, ts_to: Date and time of the beginning and end of the interaction.</p> </li> <li> <p>label: “attendance” or “follower”</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> </ul> <p><strong>Berlin2019_dances_with_manually_verified_times.csv</strong></p> <p>A sample of dances from Berlin2019_dances.csv where the exact timestamps have been manually verified to correspond to the beginning of the first and last waggle phase down to a precision of ca. 166 ms (video material was recorded at 6 FPS).</p> <ul> <li> <p>dance_id: Unique ID of the dance.</p> </li> <li> <p>dancer_id: Unique ID of the dancing individual.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.</p> </li> <li> <p>dance_start, dance_end: Manually verified date and times of the beginning and end of the dance.</p> </li> </ul> <p><strong>Berlin2019_dance_classifier_labels.csv</strong></p> <p>Manually annotated waggle phases or following behavior for our recording season in 2019 that was used to train the dancing and following classifier. Can be merged with the supplied individual detections.</p> <ul> <li> <p>timestamp: Timestamp of the individual frame the behavior was observed in.</p> </li> <li> <p>frame_id: Unique ID of the video frame the behavior was observed in.</p> </li> <li> <p>bee_id: Unique ID of the individual bee.</p> </li> <li> <p>label: One of “nothing”, “waggle”, “follower”</p> </li> </ul> <p><strong>Berlin2019_dance_classifier_unlabeled.csv</strong></p> <p>Additional unlabeled samples of timestamp and individual ID with the same format as Berlin2019_dance_classifier_labels.csv, but without a label. The data points have been sampled close to detections of our waggle phase classifier, so behaviors related to the waggle dance are likely overrepresented in that sample.</p> <p><strong>Berlin2021_waggle_phase_classifier_labels.csv</strong></p> <p>Manually annotated detections of our waggle phase detector (bb_wdd2) that were used to train the neural network filter (bb_wdd_filter) for the 2021 data.</p> <ul> <li> <p>detection_id: Unique ID of the waggle phase.</p> </li> <li> <p>label: One of “waggle”, “activating”, “ventilating”, “trembling”, “other”. Where “waggle” denoted a waggle phase, “activating” is the shaking signal, “ventilating” is a bee fanning her wings. “trembling” denotes a tremble dance, but the distinction from the “other” class was often not clear, so “trembling” was merged into “other” for training.</p> </li> <li> <p>orientation: The body orientation of the bee that triggered the detection in radians (0: facing to the right, PI /4: facing up).</p> </li> <li> <p>metadata_path: Path to the individual detection in the same directory structure as created by the waggle dance detector.</p> </li> </ul> <p><strong>Berlin2021_waggle_phase_classifier_ground_truth.zip</strong></p> <p>The output of the waggle dance detector (bb_wdd2) that corresponds to Berlin2021_waggle_phase_classifier_labels.csv and is used for training. The archive includes a directory structure as output by the bb_wdd2 and each directory includes the original image sequence that triggered the detection in an archive and the corresponding metadata. The training code supplied in bb_wdd_filter directly works with this directory structure.</p> <p><strong>Berlin2019_tracks.zip</strong></p> <p>Detections and tracks from the recording season in 2019 as produced by our tracking system. As the full data is several terabytes in size, we include the subset of our data here that is relevant for our publication which comprises over 46 million detections. We included tracks for all detected behaviors (dancing, following, attending) including one minute before and after the behavior. We also included all tracks that correspond to the labeled and unlabeled data that was used to train the dance classifier including 30 seconds before and after the data used for training.<br> We grouped the exported data by date to make the handling easier, but to efficiently work with the data, we recommend importing it into an indexable database.</p> <p>The individual files contain the following columns:</p> <ul> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>timestamp: Date and time of the detection.</p> </li> <li> <p>frame_id: Unique ID of the video frame of the recording from which the detection was extracted.</p> </li> <li> <p>track_id: Unique ID of an individual track (short motion path from one individual). For longer tracks, the detections can be linked based on the bee_id.</p> </li> <li> <p>bee_id: Unique ID of the individual bee.</p> </li> <li> <p>bee_id_confidence: Confidence between 0 and 1 that the bee_id is correct as output by our tracking system.</p> </li> <li> <p>x_pos_hive, y_pos_hive: Spatial position of the bee in the hive on the side indicated by cam_id. Given in millimeters after applying a homography on the video material.</p> </li> <li> <p>orientation_hive: Orientation of the bees’ thorax in the hive in radians (0: oriented to the right, PI / 4: oriented upwards).</p> </li> </ul> <p><strong>Berlin2019_feeder_experiment_log.csv</strong></p> <p>Experiment log for our feeder experiments in 2019.</p> <ul> <li> <p>date: Date given in the format year-month-day.</p> </li> <li> <p>feeder_cam_id: Numeric ID of the feeder.</p> </li> <li> <p>coordinates: Longitude and latitude of the feeder. For feeders 1 and 2 this is only given once and held constant. Feeder 3 had varying locations.</p> </li> <li> <p>time_opened, time_closed: Date and time when the feeder was set up or closed again.<br> sucrose_solution: Concentration of the sucrose solution given as sugar:water (in terms of weight). On days where feeder 3 was open, the other two feeders offered water without sugar.</p> </li> </ul> <p> </p> <ul> </ul> <p><strong>Software used to acquire and analyze the data:</strong></p> <ul> <li> <p><a href="https://github.com/BioroboticsLab/bb_pipeline">bb_pipeline: Tag localization and decoding pipeline</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_pipeline_models">bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_binary">bb_binary: Raw detection data storage format</a></p> </li> <li> <p><a href="https://doi.org/10.5281/zenodo.4436419">bb_irflash: IR flash system schematics and arduino code</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_imgacquisition">bb_imgacquisition: Recording and network storage </a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, feature extraction</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_tracking">bb_tracking: Tracking of bee detections over time</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_wdd2">bb_wdd2: Automatic detection and decoding of honey bee waggle dances</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_wdd_filter/">bb_wdd_filter: Machine learning model to improve the accuracy of the waggle dance detector</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_dance_networks/tree/master/bb_dance_networks">bb_dance_networks: Detection of dancing and following behavior from trajectories</a></p> </li> </ul> <p> </p>
Social networks predict the life and death of honey bees - Data
<p><strong>Interaction matrices and metadata used in "Social networks predict the life and death of honey bees"</strong></p> <p><a href="https://www.biorxiv.org/content/10.1101/2020.05.06.076943v2">Preprint: Social networks predict the life and death of honey bees</a></p> <p>See the README file in <a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition</a> for example code.</p> <p><strong>The following files are included:</strong></p> <p><strong>interaction_networks_20160729to20160827.h5</strong></p> <p>The social interaction networks as a dense tensor and metadata.</p> <p>Keys:</p> <ul> <li>interactions: Tensor of shape (29, 2010, 2010, 9) (days x individuals x individuals x interaction_types). I_{d,i,j,t} = log(1 + x), where x is the number of interactions of type t between individuals i and j at recording day d. See the methods section of paper of the interaction types.</li> <li>labels: Names of the 9 interaction types in the order they are stored in the interactions tensor.</li> <li>bee_ids: List of length 2010, mapping from sequential index used in the interaction tensor to the original BeesBook tag ID of the individual</li> </ul> <p><strong>alive_bees_bayesian.csv </strong></p> <p>This file contains the results of the bayesian lifetime model with one row for each bee.</p> <p>Columns:</p> <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>days_alive: Number of bees the bees was determined to be alive. If the individual was still alive at the end of the recording, the number of days from the day she hatched until the end of the recording.</li> <li>death_observed: Boolean indicator whether the death occurred during the recording period.</li> <li>annotated_tagged_date: Hatch date of the individual, i.e. the date she was tagged.</li> <li>inferred_death_date: The death date as determined by the model.</li> </ul> <p><strong>bee_daily_data.csv</strong></p> <p>This file contains one row per bee per day that she was alive for the focal period.</p> <p>Columns:</p> <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>date: Date in year-month-day format.</li> <li>age: Age in days. Can be NaN if the bee has no associated death_date.</li> <li>network_age, network_age_1, network_age_2: The first three dimensions of network age.</li> <li>dance_floor, honey_storage, near_exit, brood_area_total: Normalized (sum to 1). Can be NaN if a bee had no high confidence detections (>0.9) for a given day. Can be 0 if a bee was only seen outside of the annotated areas.</li> <li>location_descriptor_count: The number of minutes the bee was seen in one of the location labels during that day. I.e., dance_floor * location_descriptor_count calculates the number of minutes, the bee was seen on the dance floor on the given day.</li> <li>death_date: Date the bee was last seen in the colony in year-month-day format. Can be NaN for individuals that did not die until the end of the recording period.</li> <li>circadian_rhythm: R² value of a sine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</li> <li>velocity_peak_time: Phase of the circadian sine fit in hours as an offset to 12:00 UTC. Can be NaN if circadian_rhythm is NaN.</li> <li>velocity_day, velocity_night: Mean velocity of the individual between 09:00-18:00 UTC and 21:00-06:00 UTC, respectively. Can be NaN if no velocity data was available for that interval.</li> <li>days_left: Difference in days between date and death_date. Can be NaN if death_date is NaN.</li> </ul> <p><strong>location_data.csv</strong></p> <p>This file contains subsampled position information for all bees during the focal period. The data contains one row for every individual for every minute of the recording if that individual was seen at least once during that minute with a tag confidence of at least 0.9. The first matching detection for each individual is used.</p> <p>Columns:</p> <p>In addition to the bee_id and date columns as in the bee_daily_data.csv, the file contains these additional columns:</p> <ul> <li>cam_id, cams: The cam_id is a numerical identifier from {0, 1, 2, 3}. Each side of the hive is filmed by two cameras where {0, 1} and {2, 3} record the same side respectively. The cams column contains values either “(0, 1)” or “(2, 3)” and indicates to which sides of the hive this detection belongs.</li> <li>x_pos_hive, y_pos_hive: The spatial positions in millimeters on the hive. The two cameras from one side share a common coordinate system.</li> <li>location: The label that was assigned to the comb at (x_pos_hive, y_pos_hive) on the given date. The label “other” indicates detections that were outside of any annotated region. The label “not_comb” indicates the wooden frame or empty space around the comb.</li> <li>timestamp, date: The timestamp indicates the beginning of each one-minute sampling interval and is given in UTC, as indicated (example: “2016-08-13 00:00:00+00:00”). The date part of the timestamp is repeated in the “date” column. Both are given in year-month-day format.</li> </ul> <p><strong>Software used to acquire and analyze the data:</strong></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition: Network age calculation and regression analyses</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline/releases/tag/2016">bb_pipeline: Tag localization and decoding pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline_models/releases/tag/2016">bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_binary/releases/tag/2016">bb_binary: Raw detection data storage format</a></li> <li><a href="https://doi.org/10.5281/zenodo.4436419">bb_irflash: IR flash system schematics and arduino code</a></li> <li><a href="https://github.com/BioroboticsLab/bb_imgacquisition/releases/tag/2016">bb_imgacquisition: Recording and network storage </a></li> <li><a href="https://github.com/BioroboticsLab/bb_behavior/releases/tag/2016">bb_behavior: Database interaction and data (pre)processing, velocity calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_circadian/releases/tag/2016">bb_circadian: Circadian rhythm calculations</a></li> <li><a href="https://github.com/BioroboticsLab/bb_tracking_2016/releases/tag/2016">bb_tracking: Tracking of bee detections over time</a></li> <li><a href="https://github.com/BioroboticsLab/bb_wdd/releases/tag/2016">bb_wdd: Automatic detection and decoding of honey bee waggle dances</a></li> <li><a href="https://github.com/BioroboticsLab/bb_interval_determination/releases/tag/2016">bb_interval_determination: Homography calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_stitcher/releases/tag/2016">bb_stitcher: Image stitching</a></li> </ul> <p> </p>
Occasional and constant exposure to dietary ethanol shortens the lifespan of worker honey bees
<p><span>Honey bees (<em>Apis mellifera</em>) are one of the most crucial pollinators, providing vital ecosystem services. Their development and functioning depend on essential nutrients and substances found in the environment. While collecting nectar as a vital carbohydrate source, bees routinely encounter low doses of ethanol from yeast fermentation. Yet, the effects of repeated ethanol exposure on bees' survival and physiology remain poorly understood. Here, we investigate the impacts of constant and occasional consumption of food spiked with 1% ethanol on honey bee mortality and alcohol dehydrogenase (ADH) activity. This ethanol concentration might be tentatively judged close to that in natural conditions. We conducted an experiment in which bees were exposed to three types of long-term diets: constant sugar solution (control group that simulated conditions of no access to ethanol), sugar solution spiked with ethanol every third day (that simulated occasional, infrequent exposure to ethanol) and daily ethanol consumption (simulating constant, routine exposure to ethanol). The results revealed that both constant and occasional ethanol consumption increased the mortality of bees, but only after several days. These mortality rates rose with the frequency of ethanol intake. The ADH activity remained similar in bees from all groups. Our findings indicate that exposure of bees to ethanol carries harmful effects that accumulate over time. Further research is needed to pinpoint the exact ethanol doses ingested with food and exposure frequency in bees in natural conditions.</span></p>
Bioinformatic pipeline: Genomic diversity landscape of the honey bee gut microbiota
<p>This data-set describes the full bioinformatic pipeline used to analyze 54 metagenomic samples of the honey bee gut microbiota. Each sample was isolated from an individual honey bee, and all samples originate from two colonies of the Engel laboratory at the University of Lausanne, Switzerland. The full raw data-set is available from the sequence-read archive: SRP150166.</p> <p>A publication based on this analysis is currently under review, with the title: "Genomic diversity landscape of the honey bee gut microbiota", and an upload to Biorxiv is also underway.</p> <p>The data-set contains tar-balls for the different main workflows of the analysis. Dowload and unpack to view the contents (tar -zxvf filename.tar.gz). For each workflow, 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>Scripts are included within workflow directories, and are also provided as a separate tar-ball for convenience. All perl-scripts come with 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> </p> <p> </p> <p> </p>
Honey bee Seasonal mortality 2012-2014 - Epilobee analysis
<p>EPILOBEE was the first active epidemiological surveillance program implemented in 17 EU Member States, over 2 consecutive years (from autumn 2012 to summer 2014), following a harmonised protocol based on the EU reference laboratory guidelines. EFSA requested a statistical analysis on the EPILOBEE dataset to establish associations between colony mortalities and some factors including disease prevalence, the context of beekeeping and the apiary geographical distribution.The data set published is the result of the data cleaning and categorization performed on the EPILOBEE original dataset regarding seasonal mortality. The dataset comprises 4758 observations from apiaries across Europe.</p>
Honey bee Winter mortality 2012-2014 - Epilobee analysis
<p>EPILOBEE was the first active epidemiological surveillance program implemented in 17 EU Member States, over 2 consecutive years (from autumn 2012 to summer 2014), following a harmonised protocol based on the EU reference laboratory guidelines. EFSA requested a statistical analysis on the EPILOBEE dataset to establish associations between colony mortalities and some factors including disease prevalence, the context of beekeeping and the apiary geographical distribution. The data set published is the result of the data cleaning and categorization performed on the EPILOBEE original dataset regarding winter mortality. The dataset comprises 4758 observations from apiaries across Europe.</p> <p>The present dataset has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. </p> <p>The dataset is in EXCEL format.</p>
Data and Code: Host-derived organic acids enable gut colonization of the honey bee symbiont Snodgrassella alvi
<p>Raw data and codes underlying the CFU count, qPCR, metabolomics, and NanoSIMS data for the paper "Host-derived organic acids enable gut colonization of the honey bee symbiont Snodgrassella alvi". Data is subdivided by main figure in the paper. Additionally, raw GC-MS datafiles (.cdf) are provided in separate folders. </p>
The effect of Israeli acute paralysis infection on honey bee brood care behavior
<p>To protect themselves from communicable diseases, social insects utilize social immunity—behavioral, phsyiological, and organizational means to combat disease transmission and severity. Within a honey bee colony, larvae are visited thousands of times by nurse bees, representing a prime environment for pathogen transmission. We investigated a potential social immune response to Israeli acute paralysis virus (IAPV) infection in brood care, testing the hypotheses that bees will respond with behaviors that result in reduced brood care, or that infection results in elevated brood care as a virus-driven mechanism to increase transmission. We tested for group-level effects by comparing three different social environments in which 0%, 50%, or 100% of bees were experimentally infected with IAPV. We investigated individual-level effects by comparing exposed bees to unexposed bees within the mixed-exposure treatment group. We found no evidence for a social immune response at the group level; however, individually, exposed bees interacted with the larva more frequently than their unexposed nestmates. While this could increase virus transmission from adults to larvae, it could also represent a hygienic response to increase grooming when an infection is detected. Together, our findings underline the complexity of disease dynamics in complex social animal systems.</p>
Synergistic negative effects between a fungicide and high temperatures on homing behaviours in honey bees
<p>Interactions between environmental stressors may contribute to ongoing pollinator declines, but have not been extensively studied. Here, we examined the interaction between the agricultural fungicide Pristine<sup>®</sup> (active ingredients: 25.2% boscalid, 12.8% pyraclostrobin) and high temperatures on critical honey bee behaviours. We have previously shown that consumption of field-realistic levels of this fungicide shortens worker lifespan in the field and impairs associative learning performance in a laboratory-based assay. We hypothesized that Pristine<sup>®</sup> would also impair homing and foraging behaviours in the field, and that an interaction with hot weather would exacerbate this effect. Both field-relevant Pristine<sup>®</sup> exposure and higher air temperatures reduced the probability of successful return on their own. Together, the two factors synergistically reduced the probability of return and increased the time required for bees to return to the hive. Pristine<sup>®</sup> did not affect the masses of pollen or volumes of nectar or water brought back to the hive by foragers, and it did not affect the ratio of forager types in a colony. However, Pristine<sup>®</sup>-fed bees brought more concentrated nectar back to the hive. As both agrochemical usage and heat waves increase, additive and synergistic negative effects may pose major threats to pollinators and sustainable agriculture. </p>
Identification data for discrimination between Apis mellifera pomonella from Kazakhstan and honey bees from other parts of the world
<p>The data in dw.xml file can be used for identification of <em>Apis mellifera pomonella</em> from Kazakhstan. This file should be open in IdentiFly software http://drawwing.org/identifly and used as classification data. The discrimination is based on 19 landmarks of a forewing. For more details see:</p> <p><br>Nawrocka A., Kandemir I., Fuchs S., Tofilski A. 2018. Computer software for identification of honey bee subspecies and evolutionary lineages. Apidologie 49: 172-184. https://doi.org/10.1007/s13592-017-0538-y</p> <p>Temirbayeva, K., Torekhanov A., Nuralieva U., Sheralieva Z., Tofilski A. 2023. In Search of Apis mellifera pomonella in Kazakhstan. Life 13:1860. https://doi.org/10.3390/life13091860</p>
The more the better: Fatty acids are predictive markers of honey bee, Apis mellifera, worker longevity
<p>Fatty acids (FA), stemming from nutrition, form triglycerides that are key components for insect energy reserves. In managed <em>Apis mellifera</em> colonies, supplementary feeding is common practice, yet micronutrients and microbiota (i.e. B-vitamins and probiotics) are often neglected. Given that B-vitamins are obligate cofactors for FA metabolism, and probiotics likely play key roles as well (i.e. <em>Lactobacillus</em> spp. synthesize B-vitamins), understanding how they contribute to FA acquisition remains unknown. Indeed, FAs are established predictors to <em>A. mellifera</em> longevity, and as such, are a logical point of interest in long-lived "winter" bees, where <em>A. mellifera</em> colony losses typically occur. Here, in a hoarding cage trial, freshly emerged adult winter workers were exposed to antibiotics (ABX) to decouple innate benefits associated to native gut microbiota, or left unexposed to ABX (N=72 cages, N=2088 experimental workers). Subsequently, all workers were fed different diets containing either probiotics, B-vitamins, with replicate treatments given <em>ad libitum</em> access to pollen (mimicking real-hive scenarios) or left blank (control). At the end of the trial, a subsample (n=356) had their total FA contents analyzed using <u>G</u>as <u>C</u>hromatography coupled to <u>F</u>lame <u>I</u>onization <u>D</u>etector (GC-FID). Irrespective of dietary treatment, every worker contained all 11 identified FAs, aligning our results with <em>a priori</em> evidence and highlighting their underlying key roles for bee physiology and health. We show for the first time that B-vitamins alone did not improve the overall abundance of individual FAs (g), yet significant differences were associated with presence/absence of bacteria and/or access to pollen, reconfirming likely ties of microbiota aiding in nutrient breakdown of complex polysaccharides found in pollen. Finally, of clear importance, there was a positive significant correlation between total lipid content and worker longevity (+2.4 median day lifespan increase / mg of FA), thereby confirming the relevance of FAs for honey bee worker longevity.</p>
The gut microbiota affects the social network of honey bees
<p>This dataset contains input files needed to reproduce the automated behavioral tracking data analyses of the research article "The gut microbiota affects the social network of honey bees”. Codes using these data and additional datasets are available at: https://github.com/JoanitoLiberti/The-gut-microbiota-affects-the-social-network-of-honey-bees/</p> <p> </p>
Data from: Evaluating the foraging performance of individual honey bees in different environments with automated field RFID systems
<p>Measuring the individual foraging performances of pollinators is crucial to guide environmental policies that aim at enhancing pollinator health and pollination services. Automated systems have been developed to track the activity of individual honey bees, but their deployment is extremely challenging. This has limited the assessment of individual foraging performances in full-strength bee colonies in the field. Most studies available to date have been constrained to use downsized bee colonies located in urban and suburban areas. Environmental policy-making, on the other hand, needs a more comprehensive assessment of honey bee performances in a broader range of environments, including in remote agricultural and wild areas. Here we detail a new autonomous field method to record high quality data on the flight ontogeny and foraging performance of honey bees, using Radio-Frequency Identification (RFID). We separate bee traffic into returning and exiting tunnels to improve data quality, solving many previous limitations of RFID systems caused by traffic jams and the parasitic coupling of RFID antennae. With this method, we assembled a large RFID dataset made of control bee colonies from experiments conducted in different locations and seasons. We hope our results will be a starting point to understand how ontogenetic and environmental factors affect the individual performances of honey bees, and that our method will enable the large-scale replication of individual pollinator performance studies.</p>
Data from: Iridescence untwined - Honey bees can separate hue variations in space and time
<p><span>Iridescence is a phenomenon whereby the hue of a surface changes with viewing or illumination angle. Many animals display iridescence but it currently remains unclear whether relevant observers process iridescent color signals as a complex collection of colors (spatial variation), or as moving patterns of colors and shapes (temporal variation). This is important as animals may use only the spatial or temporal component of the signal, although this possibility has rarely been considered or tested. Here, we investigated whether honey bees could separate the temporal and spatial components of iridescence by training them to discriminate between iridescent disks and photographic images of the iridescent patterns presented by the disks. Both stimuli therefore contained spatial color variation, but the photographic stimuli do not change in hue with varying angle (no temporal variation). We found that individual bee observers could discriminate the variable patterns of iridescent disks from static photographs during unrewarded tests. Control experiments showed that bees reliably discriminated iridescent disks from control silver disks, showing that bees were processing chromatic cues. These results suggest that honey bees could selectively choose to attend to the temporal component of iridescence signals to make accurate decisions. </span></p>
FIG. 6 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 6. — Two views on the same rafter (sunggau muke with renak ngelandas; 28 March 2017). Credits: M. Rhomadona (A), N. Césard (B).
FIG. 5 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 5. — Rendap rabas, before (A) and after (B) being improved (November 2013). The arrows indicate the cuts in the vegetation. Credits: N. Césard.
FIG. 1 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 1. — Hemispherical photographs of three rendap (in pale blue): direct (A), indirect (or semi-open) (B) and well (C) access paths. Credits: N. Césard.
Dataset for honey bee queen and worker learning
<p>As the primary source of colony reproduction, social insect queens play a vital role. However, the cognitive abilities of queens are not well understood, although queen learning and memory are essential in multiple species such as honey bees, in which virgin queens must leave the nest and then successful learn to navigate back over repeated nuptial flights. Because honey bee queen learning has never been previously demonstrated, and our goal was to determine formally if <em>Apis mellifera</em> queens have learning and memory. We tested olfactory learning in queens and workers and examined the role of DNA methylation, which plays a key role in long term memory formation. We provide the first evidence that honey bee queens have remarkably good learning and memory. The proportion of honey bee queens that exhibited learning was 5-fold higher than workers at every tested age and, for memory, 4-fold higher than workers at a very young age. DNA methylation evidently plays a key role in superior queen memory because queens exhibiting remote memory had a more consistent elevation in <em>Dnmt3</em> gene expression as compared to workers. Both castes also showed excellent very long-term memory (remote memory, 7 d), which was reduced by 30-36% by the DNA methylation inhibitor, <em>Dnmt3</em>. Given that queens live about 10-fold longer than workers, these results suggest that queens can serve as an exceptionally long-term reservoir of colony memory.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.