Interaction matrices and metadata used in "Social networks predict the life and death of honey bees"

Preprint: Social networks predict the life and death of honey bees

See the README file in bb_network_decomposition for example code.

The following files are included:

interaction_networks_20160729to20160827.h5

The social interaction networks as a dense tensor and metadata.

Keys:

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.

labels: Names of the 9 interaction types in the order they are stored in the interactions tensor.

bee_ids: List of length 2010, mapping from sequential index used in the interaction tensor to the original BeesBook tag ID of the individual

alive_bees_bayesian.csv

This file contains the results of the bayesian lifetime model with one row for each bee.

Columns:

bee_id: Numerical unique identifier for each individual.

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.

death_observed: Boolean indicator whether the death occurred during the recording period.

annotated_tagged_date: Hatch date of the individual, i.e. the date she was tagged.

inferred_death_date: The death date as determined by the model.

bee_daily_data.csv

This file contains one row per bee per day that she was alive for the focal period.

Columns:

bee_id: Numerical unique identifier for each individual.

date: Date in year-month-day format.

age: Age in days. Can be NaN if the bee has no associated death_date.

network_age, network_age_1, network_age_2: The first three dimensions of network age.

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.

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.

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.

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.

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.

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.

days_left: Difference in days between date and death_date. Can be NaN if death_date is NaN.

location_data.csv

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.

Columns:

In addition to the bee_id and date columns as in the bee_daily_data.csv, the file contains these additional columns:

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.

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.

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.

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.

Software used to acquire and analyze the data:

bb_network_decomposition: Network age calculation and regression analyses

bb_pipeline: Tag localization and decoding pipeline

bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline

bb_binary: Raw detection data storage format

bb_irflash: IR flash system schematics and arduino code

bb_imgacquisition: Recording and network storage

bb_behavior: Database interaction and data (pre)processing, velocity calculation

bb_circadian: Circadian rhythm calculations

bb_tracking: Tracking of bee detections over time

bb_wdd: Automatic detection and decoding of honey bee waggle dances

bb_interval_determination: Homography calculation

bb_stitcher: Image stitching

Ongoing honey bee colony losses are of significant international concern because of the essential role these insects play in pollinating many high nutrient crops, such as fruits, vegetables, and nuts. Both chemical and non-chemical stressors have been implicated as possible contributors to colony failure; however, the potential role(s) of commonly-used neonicotinoid insecticides has emerged as particularly concerning. Neonicotinoids act on the nicotinic acetylcholine receptors (nAChRs) in the central nervous system to eliminate target pest insects. However, mounting evidence indicates that these neonicotinoids also may adversely affect beneficial pollinators, such as the honey bee (Apis mellifera), via impairments on learning and memory, and ultimately foraging success. The specific mechanisms linking activation of the nAChR to adverse effects on learning and memory are uncertain. Additionally, clear connections between observed impacts on individual bees and colony level effects are lacking. The objective of this review was to develop adverse outcome pathways (AOPs) as a means to evaluate the biological plausibility and empirical evidence supporting (or refuting) the linkage between activation of the physiological target site, the nAChR, and colony level consequences. Development of AOPs has led to the identification of research gaps which, for example, may be of high priority in understanding how perturbation of pathways involved in neurotransmission can adversely affect normal colony functions, causing colony instability and subsequent bee population failure. A putative AOP network was developed, laying the foundation for further insights as to the role of combined chemical and non-chemical stressors in impacting bee populations. Insights gained from the putative AOP network assembly, which more realistically represents multi-stressor impacts on honey bee colonies, are promising toward understanding common sensitive nodes in key biological pathways and identifying where mitigation strategies may be focused to reduce colony losses. This dataset is not publicly accessible because: No data, literature review only. It can be accessed through the following means: N/A. Format: N/A. This dataset is associated with the following publication: LaLone, C., D. Villeneuve, J. Wu-Smart, R. Milsk, K. Sappington, K. Garber, J. Housenger, and G. Ankley. Weight of evidence evaluation of a network of adverse outcome pathways linking activation of the nicotinic acetylcholine receptor in honey bees to colony death. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 584: 751–775, (2017).

A native solitary bee to North America, the blue orchard bee (Osmia lignaria Say, Hymenoptera: Megachilidae) is a crucial pollinator for orchard crops such as apples, almonds, and cherries. Osmia lignaria is often managed commercially and sold to complement honey bee pollination services.We collected data following an accidental drop of developing immature bees inside their cocoons. These bees were part of a larger experiment performed in 2020. On June 17, 2020, bees were dropped approximately one meter onto a linoleum floor at the USDA-ARS-PWA Pollinating Insect Research Unit in Logan, Utah, USA. Developing bees were in gelatin capsules and attached to a sticky board for X-ray imaging. Using a board from the same study that had not fallen, we compared survival, life stages, and bodily injuries to document the effects of dropping immature O. lignaria a short distance.Our research highlights the risks of handling immature O. lignaria during metamorphosis. Our data provides valuable information for bee managers and researchers about the risks of physical disturbances during critical developmental stages, which could affect bee survival and pollination services in orchards.Key findings include: (1) Near-complete mortality of developing bees before the adult molt stage, (2) Insights into the vulnerability of O. lignaria during immature developmental stages, even when inside cocoons, and (3) Documentation of how mechanical injury during immature development impacts survival.The dataset provides counts of bees in different life stages and conditions, including: (1) Life status (alive or dead) at cocoon completion, pupation, and adult molt stages, (2) Sex determination for bees that reached adulthood (male or female), (3) Final life stage reached (prepupa, pupa, or adult), and (4) Body condition after the fall (malformed, melanized, no observable change, or partially melanized).Additional variables in the dataset include: (1) Sample identifiers, treatment groups, and X-ray board identifiers from the original experiment and (2) Whether the board was dropped or not.Abbreviations and acronyms in the datasetSample_ID = sample identifier (one for each individual bee)Treatment = treatment groups from the original experimentCONTROL = received a sham treatment (sterilized Ringer's Solution)VIRUS = received a virus inoculate (virus particles in Ringer's Solution)OSS10 = received organosilicon (OSS) at 10 parts per million (ppm) (diluted in Ringer's Solution)OSS100 = received OSS at 100 ppm (diluted in Ringer's Solution)OSS10V = received a virus inoculate and OSS at 10 ppmOSS100V = received a virus inoculate and OSS at 100 ppmXray_board = sticky board identifier, which stick board were samples attached to from the original experimentLifeCategory_Cocoon = life status at the time of cocoon completionLifeCategory_Pupa = life status at the time of pupationLifeCategory_Adult = life status at the time of the adult moltSex = sex determined for bees that reached the adult stageOrg_Stage = final life stage reached by beesBody_Category = body condition determined after samples were droppedBoard_Drop = whether the samples analyzed were from dropped vs. not dropped sticky boards

In this experiment, we crossed temperature (20, 25, and 30C) with humidity (95% RH) and measured the effects on bumble bee (Bombus impatiens) water balance. We had 10 replicates per treatment, with 6 source colonies, resulting in a total of 540 bees. We placed bees singly into tubes and checked for mortality every 2 hours for the first 12 hours and then every 4 hours until 5/10 bees from each temperature, humidity, colony treatment had reached mortality (52h and 48h for the two trials). We measured mass before and after mortality to evaluate water loss and then dried bees to a constant dry weight to get the initial water content and dry mass of each bee.

Graph and download economic data for Age-Adjusted Premature Death Rate for Bee County, TX (CDC20N2UAA048025) from 1999 to 2020 about Bee County, TX; premature; death; TX; rate; and USA.

Honey bee colony winter mortalities calculated for the different clusters during EPILOBEE 2012–2014 with the features of each cluster.

Beekeepers regularly employ management practices to mitigate losses during the winter, often considered the most difficult time during a colony life cycle. Management recommendations involving covering or wrapping hives in insulation during winter have a long history; over 100 years ago, most recommendations for overwintering in cold climates involved heavy insulation wraps or moving hives indoors. These recommendations began to change in the mid-20th century, but hive covers are still considered useful and are described in contemporary beekeeping manuals and cooperative extension materials. However, most of the data supporting their use is published primarily in non-peer reviewed trade journals and was collected >40 years ago. In this time, the beekeeping environment has changed substantially, with new pressures from pathogens, agrochemicals, and land use changes. Here, we provide an update to the historical literature, reporting a randomized experiment testing the effectiveness of ...

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.

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.

The dataset is in EXCEL format.

A mathematical model is constructed to quantify the loss of resilience in collapsing honey bee colonies due to the presence of a strong Allee effect. In the model, recruitment and mortality of adult bees have substantial social components, with recruitment enhanced and mortality reduced by additional adult bee numbers. The result is an Allee effect, a net per-individual rate of hive increase that increases as a function of adult bee numbers. The Allee effect creates a critical minimum size in adult bee numbers, below which mortality is greater than recruitment, with ensuing loss of viability of the hive. Under ordinary and favorable environmental circumstances, the critical size is low, and hives remain large, sending off viably-sized swarms (naturally or through beekeeping management) when hive numbers approach an upper stable equilibrium size (carrying capacity). However, both the lower critical size and the upper stable size depend on many parameters related to demographic rates and their enhancement by bee sociality. Any environmental factors that increase mortality, decrease recruitment, or interfere with the social moderation of these rates has the effect of exacerbating the Allee effect by increasing the lower critical size and substantially decreasing the upper stable size. As well, the basin of attraction to the upper stable size, defined by the model potential function, becomes narrower and shallower, indicating the loss of resilience as the hive becomes subjected to increased risk of falling below the critical size. Environmental effects of greater severity can cause the two equilibria to merge and the basin of attraction to the upper stable size to disappear, resulting in collapse of the hive from any initial size. The model suggests that multiple proximate causes, among them pesticides, mites, pathogens, and climate change, working singly or in combinations, could trigger hive collapse. This data supplement provides a text file containing 7 scripts written in the R programming language for reproducing Figures 1–7. Resources in this dataset:Resource Title: S1 R scripts for figures. File Name: Web Page, url: https://doi.org/10.1371/journal.pone.0150055.s001 Text file containing 7 scripts written in the R programming language for reproducing Figs 1–7 of this article.Resource Software Recommended: R programming language,url: https://www.r-project.org/

Premature Death Rate for Bee County, TX was 592.80000 Rate per 100,000 in January of 2020, according to the United States Federal Reserve. Historically, Premature Death Rate for Bee County, TX reached a record high of 592.80000 in January of 2020 and a record low of 301.70000 in January of 2004. Trading Economics provides the current actual value, an historical data chart and related indicators for Premature Death Rate for Bee County, TX - last updated from the United States Federal Reserve on July of 2025.

Commercial lowbush blueberry (Vaccinium angustifolium Ait.) and cranberry (Vaccinium macrocarpon Ait.) crops benefit from the presence of honey bee (Apis mellifera L.) for pollination. Unfortunately, beekeepers are observing negative impacts of pollination services on honey bee colonies. In this study, we investigated three beekeeping management strategies (MS) and measured their impact on honey bee colony health and development. Experimental groups (five colonies/MS) were: A) Control farmland honey producing MS (control MS); B) Blueberry pollination MS (blueberry MS); C) Cranberry pollination MS (cranberry MS) and D) Double pollination MS, blueberry followed by cranberry (double MS). Our goals were to 1) compare floral abundance and attractiveness of foraging areas to honey bees between apiaries using a Geographic Information System, and 2) compare honey bee colony health status and population development between MS during a complete beekeeping season. Our results show significantly lower floral abundance and honey bee attractiveness of foraging areas during cranberry pollination compared to the other environments. The blueberry pollination site seemed to significantly reduce brood population in the colonies who provided those services (blueberry MS and double MS). The cranberry pollination site seemed to significantly reduce colony weight gain (cranberry MS and double MS) and induce a significantly higher winter mortality rate (cranberry MS). We also measured significantly higher levels of Black queen cell virus and Sacbrood virus in the MS providing cranberry pollination (cranberry MS and double MS).

Age-Adjusted Premature Death Rate for Bee County, TX was 551.00000 Rate per 100,000 in January of 2020, according to the United States Federal Reserve. Historically, Age-Adjusted Premature Death Rate for Bee County, TX reached a record high of 551.00000 in January of 2020 and a record low of 333.50000 in January of 2004. Trading Economics provides the current actual value, an historical data chart and related indicators for Age-Adjusted Premature Death Rate for Bee County, TX - last updated from the United States Federal Reserve on June of 2025.

Analysis of ‘Honey bee Seasonal mortality 2012-2014 - Epilobee analysis’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/honey-bee-seasonal-mortality-2012-2014-epilobee-analysis on 10 January 2022.

--- Dataset description provided by original source is as follows ---

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.

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 The dataset is in EXCEL format.

--- Original source retains full ownership of the source dataset ---

p = p value.

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.

The majority of invasive species are best known for their effects as predators. However, many introduced predators may also be substantial reservoirs for pathogens. Honey bee-associated viruses are found in various arthropod species including invasive ants. We examined how the globally invasive Argentine ant (Linepithema humile), which can reach high densities and infest beehives, is associated with pathogen dynamics in honey bees. Viral loads of Deformed wing virus (DWV), which has been linked to millions of beehive deaths around the globe, and black queen cell virus significantly increased in bees when invasive ants were present. Microsporidian and trypanosomatid infections, which are more bee-specific, were not affected by ant invasion. The bee virome in autumn revealed that DWV was the predominant virus with the highest infection levels and that no ant-associated viruses were infecting bees. Viral spillback from ants could increase infections in bees. In addition, ant attacks could pose a significant stressor to bee colonies that may affect virus susceptibility. These viral dynamics are a hidden effects of ant pests, which could have a significant impact on disease emergence in an economically important pollinator. Our study contributes to unravel a perhaps overlooked effect of species invasions: changes in pathogen dynamics. Methods In the austral summer of January 2019, 18 beehives from an Argentine ant-free apiary were moved into six sites in the Northland region, New Zealand, half with known Argentine ant incursions, placing three hives in each site. Monthly collections of adult worker bees from brood frames took place from January until August, except July. Quantitative PCR (qPCR) data was generated from reverse transcribed total RNA extracted from honey bees or Argentine ants on a StepOne™ Real-Time PCR cycler or a QuantStudio 7 Real-Time PCR System, respectively. Survival data was generated from field observations. RNA-seq data originates from Illumina 1.9 Hi-seq 100 base pair (bp) paired-end sequencing of total RNA from honey bees. Reads were aligned to the honey bee reference genome (Amel_HAv3.1) in HISAT2 2.1.0 to exclude host-derived reads. Unaligned reads were de novo assembled in Trinity 2.9.1 and transcript counts generated using a pipeline integrated with the Trinity package (www.github.com/trinityrnaseq/trinityrnaseq/blob/master/util/abundance_estimates_to_matrix.pl). Further details of the experimental setup and method used can be found in the accompanying manuscript.

Data for 2009 and 2010. p value in italics indicates statistically significant difference (t-Student test, p

Linden (Tilia spp.), a profusely flowering temperate tree that provides bees with vital pollen and nectar, has been associated with bumble bee (Bombus spp.) mortality in Europe and North America. Bee deaths have been attributed, with inadequate evidence, to toxicity from mannose in nectar or starvation due to low nectar in late blooming linden. Here, we investigated both factors via untargeted metabolomic analyses of nectar from five T. cordata trees beneath which crawling/dead bumble bees (B. vosnesenskii) were observed, and of thoracic muscle of 28 healthy foraging and 29 crawling bees collected from linden trees on cool mornings (< 30°C). Nectar contained the pyridine alkaloid trigonelline, a weak acetylcholinesterase inhibitor, but no mannose. Principal component analysis of muscle metabolites produced distinct clustering of healthy and crawling bees, with significant differences (P<0.05) in 34 of 123 identified metabolites. Of these, TCA (Krebs) cycle intermediates were stro...

Wild honeybees (Apis mellifera) are considered extinct in most parts of Europe. The likely causes of their decline include increased parasite burden, lack of high-quality nesting sites and associated depredation pressure, and food scarcity. In Germany, feral honeybees still colonize managed forests, but their survival rate is too low to maintain viable populations. Based on colony observations collected during a monitoring study, data on parasite prevalence, experiments on nest depredation, and analyses of land cover maps, we explored whether parasite pressure, depredation or expected landscape-level food availability explain feral colony winter mortality. Considering the colony-level occurrence of 18 microparasites in the previous summer, colonies that died did not have a higher parasite burden than colonies that survived. Camera traps installed at cavity trees revealed that four woodpecker species, great tits, and pine martens act as nest depredators. In a depredator exclusion experim...

This excel file (DOI: https://doi.org/10.5281/zenodo.3383713) provides the individual datasets on binary mixture toxicity (mortality) in bees classified according to route and exposure patterns (i.e. oral, contact, acute and chronic) and mortality endpoints (e.g.LD50, LC50) for the honeybee (Apis mellifera) and wild bee species (Osmia bicornis, Bombus terrestris). 218 individual binary mixtures were collected and included in the statistical analyses with the majority of toxicological endpoints reported as lethal doses or concentrations (e.g. LD50, LC50,) for pesticides or pesticides and veterinary drugs combinations with 133, 44 and 41 mixtures reporting acute contact toxicity (i.e. topical application), chronic oral toxicity and acute oral toxicity, respectively. Combined toxicity data for binary mixtures were available as dose response data in honeybees for acute contact toxicity (n=92) and acute oral toxicity.

The full data collection and analysis of binary mixtures are described in Carnesecchi et al., 2019 (DOI: 10.1016/j.envint.2019.105256)