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Artificial intelligence and Internet of Things are playing an increasingly important role in monitoring beehives. In this paper, we propose a method for automatic recognition of honeybee type by analyzing the sound generated by worker bees and drone bees during their flight close to an entrance to a beehive. We conducted a wide comparative study to determine the most effective preprocessing of audio signals for the detection problem. We compared the results for several different methods for signal representation in the frequency domain, including mel-frequency cepstral coefficients (MFCCs), gammatone cepstral coefficients (GTCCs), the multiple signal classification method (MUSIC) and parametric estimation of power spectral density (PSD) by the Burg algorithm. The coefficients serve as inputs for an autoencoder neural network to discriminate drone bees from worker bees. The classification is based on the reconstruction error of the signal representations produced by the autoencoder. We propose a novel approach to class separation by the autoencoder neural network with various thresholds between decision areas, including the maximum likelihood threshold for the reconstruction error. By classifying real-life signals, we demonstrated that it is possible to differentiate drone bees and worker bees based solely on audio signals. The attained level of detection accuracy enables the creation of an efficient automatic system for beekeepers.

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Ants are ubiquitous and eusocial insects that exhibit frequent physical contact among colony members, thereby increasing their susceptibility to diseases. Some species are often found in beehives and in their surroundings, where they exploit the food resources of honey bees. This intricate relationship may facilitate the interspecific transmission of honey bee pathogens to ants, although ants themselves may contribute to spillback phenomena. The objective of this study was to assess the presence and abundance of honey bee pathogens in ants sampled from Italian apiaries. A total of 37 colonies within 24 apiaries across 7 regions were monitored. In total, 6 pathogens were detected in adult ants and 3 in the brood. In particular, the study revealed a high prevalence of honey bee pathogens in ants, with DWV, BQCV, and CBPV being the most commonly encountered. The brood also tested positive for the same viruses. Notably, all analyzed viruses were found to be replicative in both adult ants and ant broods. Furthermore, co-infections were prevalent, suggesting complex pathogen interactions within ant populations. Statistical analysis indicated significant differences in pathogen prevalence and abundance among ant species and sample types. The findings highlight active infection in both the ants and the brood, suggesting a potential role of ants as reservoir hosts and vectors of honey bee pathogens emphasizing the need for further research to understand the implications of interspecific pathogen transmission on ant and bee health.

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Microplastics, MPs, plastic fragments with a dimension lower than 5 mm, and microfibers, MFs, synthetic and natural/artificial fibrous fragments with a diameter lower than 50 µm, are ubiquitous pollutants identified in different environmental compartments. In this work the occurrence of MPs and MFs on honeybees, Apis mellifera, and beehive products was evaluated, using Fourier transform infrared microspectroscopy, confirming that MPs and MFs are widely present as air contaminants in all the apiary's areas (high and low urbanized areas) in Southern Italy. Results indicated that independently from the site, both honeybees and honey samples, are contaminated by MFs with non-natural color. The majority of MFs were of natural origin followed by artificial MFs and synthetic MFs. Moreover, the chemical composition of MFs isolated from honeybees reflect that used in synthetic fabrics, leading to the hypothesis that they are released from textile to air where are captured by bees. Results highlight that MFs represent a class of ubiquitous airborne anthropogenic pollutants. The identification of polytetrafluoroethylene, PTFE, MPs in honeybees confirm the recent findings that PTFE MPs are diffuse soil and air contaminants while the identification of polyethylene, PE, based MPs in honey samples, from low density urban sites, could be correlated to the large use of PE in agriculture. In the honey samples, also polycaprolactone, PCL, MPs were identified, mainly in high density urban sites, confirming that biodegradable materials could be further pollutants in the environments. The results indicate that honeybees are contaminated by MPs and MFs during their flights or picking up from the hive components, flowers, from other nest mates, from the clothes of the beekeeper, among others and some of them could be transferred to honey samples that could be also affected by soil contamination.

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Propolis, a bee product, is known for its variability of chemical and bioactive profiles. However, Portuguese propolis from Gerês, normally obtained by mixing propolis from three places-Bugalho, Felgueiras and Toutelo-has shown similar chemical and biological profiles over the years. Recently, a new propolis place-Roca-was added to the apiary to replace Bugalho, lost to the 2017 wildfires, hence questioning the previously claimed constancy of Gerês propolis. To unravel to what extent the beehive relocation affected this constancy, we studied different Gerês propolis samples collected in three consecutive years (2017-2019) composed of different combinations of source places. Two honey samples, collected before (2017) and after (2018) the occurrence of the wildfire, were also investigated. Total phenolics, flavonoids and ortho-diphenols contents were determined and the antioxidant and antimicrobial activities were evaluated, using the DPPH assay and the agar dilution method, respectively. Although both antimicrobial and antioxidant activities were generally in the ranges usually obtained from Gerês propolis, some variations were detected for the samples, with different compositions when compared to previous years. This work reinforces the importance of the consistency of a combination of several factors for the protection and preservation of the flora near the hives, providing bee products with more constant chemical and biological profiles over the years.

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Changes in the functional shape of astigmatan mite moveable digit profiles are examined to test if Tyrophagus putrescentiae (Acaridae) is a trophic intermediate between a typical micro-saprophagous carpoglyphid (Carpoglyphus lactis) and a common macro-saprophagous glycyphagid (Glycyphagus domesticus). Digit tip elongation in these mites is decoupled from the basic physics of optimising moveable digit inertia. Investment in the basal ramus/coronoid process compared to that for the moveable digit mastication length varies with feeding style. A differentiated ascending ramus is indicated in C. lactis and in T. putrescentiae for different trophic reasons. Culturing affects relative investments in C. lactis. A markedly different style of feeding is inferred for the carpoglyphid. The micro-saprophagous acarid does not have an intermediate pattern of trophic functional form between the other two species. Mastication surface shape complexity confirms the acarid to be heterodontous. T. putrescentiae is a particularly variably formed species trophically. A plausible evolutionary path for the gradation of forms is illustrated. Digit form and strengthening to resist bending under occlusive loads is explored in detail. Extensions to the analytical approach are suggested to confirm the decoupling of moveable digit pattern from cheliceral and chelal adaptations. Caution is expressed when interpreting ordinations of multidimensional data in mites.

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There is a dearth of information on the comparative studies of the physicochemical, proximate, and antioxidant properties as well as quality standards of stingless bee honey (SBH) in Ethiopia. Hence, this study was designed to assess and compare the physicochemical, proximate, and antioxidant properties of SBH, specifically sourced from Meliponula beccarii, and produced under both wild and modern apiary conditions at two distinct geographical locations. A total of forty-six honey samples were meticulously collected from domesticated stingless bee colonies and naturally occurring wild nests at Wolmera and Cheliya districts. Pollen analysis unveiled eleven distinct bee plant species distributed across six families, with Asteraceae being the most prevalent, primarily represented by Guizotia scabra and Vernonia amygdalina. Notably, the physicochemical, proximate, and antioxidant properties of SBH collected from modern pot hives exhibited significant variances (p < .05) when compared to SBH from wild nests. Principal component analysis (PCA) delineated the differentiation of SBH sources based on both geographical location and the type of beehive. One-way ANOVA corroborated these distinctions, underscoring significantly higher levels (p < .05) of ash, electrical conductivity, free acidity, hydroxymethylfurfural, sucrose, total phenolic content, total flavonoid content, and radical scavenging activities of SBH from modern pot hives in Wolmera. Whereas, Cheliya modern pot hives recorded higher values in pH, hydroxymethylfurfural and maltose contents compared to the wild nest SBH. Further analysis through Pearson correlation highlighted a strong positive association between total phenolic content and total flavonoid content with the antioxidant capacity of SBH. These findings underscore the significance of integrating modern pot hives to enhance the quality of SBH within Ethiopia's beekeeping sector.

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Management, brood nest structure and factors associated with varroa mite infestation were studied in 60 apiaries of Africanized honey bees in the northwest region of the Central Valley of Costa Rica. Apiaries were monitored two times. The first monitoring was taken forward during the rainy season between May and November 2019. The second monitoring during the dry season between February and March 2020. Information about the beekeepers, apiaries and management was collected through a survey. Amount of open and capped brood, honey and pollen were measured in the field. The infestation rate of varroa (IRV) was quantified using standard laboratory methods. A determination of multi-residue pesticides in bee bread was made through GC-MS/MS and LC-MS/MS techniques. According to the results, most of the beekeepers produce honey (96.7%), participate in training activities (82.2%), and change the bee queens annually (70%). The first monitoring was characterized by a lower amount of capped brood and honey reserves compared to the second one. IRV was significantly higher in the first monitoring (6.0 ± 0.4) in comparison with the second one (3.0 ± 0.3) (U Mann-Whitney p < 0.001). The maximum value for the first monitoring exceeds 40%, while this value was close to 25% in the second monitoring. Mite infestation exposed significant differences in relation to the variables associated to the beekeeper's management, i.e., change of bee queen (p = 0.002) or when beekeepers monitor varroa mites (p = 0.004). Additionally, the IRV had inverse correlations (p < 0.01) with the number of comb sides with capped brood (Spearman's rho coefficient = - 0.190), and honey reserves (Spearman's rho coefficient = - 0.168). Furthermore, 23 of 60 bee bread samples presented one to five pesticide residues, being the most frequent antifungal agrochemicals.

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This study aims to determine the prevalence of microorganisms and antibiotic-resistant microorganisms in beehives located on different plantations in Thailand. Seventeen swabs immersed in transport media were utilized for samples from different zones within beehives. Traditional microbial culture-based methods, biochemical tests, MALDI-TOF MS (VITEK® MS, bioMerieux, Marcy-l'Étoile, France), and antibiotic drug susceptibility (disk-diffusion) tests were used to detect microorganism and antimicrobial resistance bacteria. The results from 16 beehive swabs found Gram-positive bacteria at 59.5%, Gram-negative bacteria at 35.1%, and fungi (yeast) at 5.4%. These organisms are classified as 11, 11, and 2 types of Gram-positive bacteria, Gram-negative bacteria, and fungi (yeast), respectively. Furthermore, no organism showed resistance to vancomycin or cefoxitin for antibiotic drug susceptibility testing. In contrast, all Acinetobacter spp. were susceptible to ciprofloxacin, levofloxacin, ceftazidime, cefotaxime, imipenem, and meropenem, except for Acinetobacter schindleri, which was resistant to ceftazidime and cefotaxime. For other organisms, due to the limitations of tests to identify some environmental microbial species, the antimicrobial susceptibility test results cannot be interpreted as resistant or susceptible to the drug for these organisms. The study's findings will support prevention, healthcare services, and public health systems.

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Geopropolis has been used in traditional medicine for centuries. In this study, the botanical origin, physicochemical profile, and biological activities of geopropolis from Melipona scutellaris harvested during rainy and dry seasons were investigated. Palynological analysis identified over 50 pollen types, with Schinus terebinthifolius and Cecropia being the predominant types. The analytical results were in line with those reported in the literature. Rainy-season geopropolis exhibited higher total phenol and flavonoid content (determined using High Performance Liquid Chromatography-25.13% and 3.92%, respectively) compared to the dry season (19.30% and 2.09%); the major peaks (naringin, gallic acid, and catechin) were similar among samples. Antioxidant capacity was assessed via DPPH, reducing power, and ß-carotene/linoleic acid discoloration assays. Rainy-season samples displayed superior antioxidant activity across methods. Antimicrobial effects were determined using microdilution, while the impact on the cholinesterase enzyme was quantified using 5-thio-2-nitrobenzoic acid accumulation. Anti-inflammatory and antimutagenic activities were assessed through hyaluronidase enzyme inhibition and by utilizing Saccharomyces cerevisiae ATCC-20113 cells. Both samples exhibited anti-inflammatory and antimutagenic properties. Moreover, a significant inhibition of acetylcholinesterase was observed, with IC50 values of 0.35 µg/mL during the rainy season and 0.28 µg/mL during the dry season. Additionally, the geopropolis displayed antimicrobial activity, particularly against Staphylococcus aureus. These findings suggest the therapeutic potential of M. scutellaris geopropolis in the context of inflammatory, oxidative, and infectious diseases.

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The sounds present inside a beehive originate from the overlap of honeybee buzzes with external sounds. They reveal patterns that support the hypothesis that the sonic context of the beehive may be utilized by honeybees as a source of ecoacoustic codes for communication and the coordination of social activity. Patterns were observed in a data series of acoustic files sampled at a frequency of 48 kHz during the period May-July 2023 in a beehive of Apis mellifera ligustica (Spinola, 1806). The acoustic information was extracted using the acoustic complexity index (ACItf) algorithm applied to a fast Fourier transform matrix. Data series, aggregated in 1368 min × 512 frequency bins × 61 days, were tentatively classified according to three temporal classes of aggregation (eight, six, and four clusters, respectively) using the hierarchical K-means clustering algorithm. The clusters obtained at these three resolutions were considered potential ecoacoustic codes (PECs) belonging to each minute of the data series. The number of discontinuities along the 24-h PEC sequence, the coefficient of variation of the number of PECs at daily and seasonal scales, and the PEC sample entropy confirmed a patterned distribution of PECs across the 24 h, modulated at a monthly scale. A significant correlation was found between these indices and the daily average wind speed, and temperature. Honeybee buzz is an informative medium used by honeybees to develop survival strategies.

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The current study sought to determine the anxiolytic, antidepressant, and anti-inflammatory properties of distilled water-soluble extract of beehive (WSE-BH). Gas chromatography-mass spectrometry (GC-MS) studies were used to characterize the chemical compositions obtained from beehives extracted in water and methanol (also fractions). The GC-MS analysis identified 19 compounds in WSE-BH, including high total phenol and flavonoid contents, compared with the methanol extract (21 compounds), ethyl acetate fraction (9 compounds), and CCl4 fraction (27 compounds). The oral administration of WSE-BH (50 and 150 mg/kg) showed significant anxiolytic activities assessed by time spent in (30.80% and 39.47%, respectively) and entry into (47.49% and 55.93%, respectively) the open arms of the elevated plus-maze (EPM). Only the 150 mg/kg dose resulted in a significant effect on the number of head-dipping events in the hole-board test (HBT) (40.2 ± 2.33; p < 0.01) vs. diazepam (64.33 ± 3.16; p < 0.001). Both the 50 and 150 mg/kg doses resulted in significant (p < 0.001) decreases in immobility in the forced swim test (FST) and tail suspensions test (TST), corresponding to the effect of fluoxetine. WSE-BH inhibited histamine-induced paw edema significantly beginning at 60 min, with the 150 mg/kg dose having the highest effect at 180 min. The current findings suggested that WSE-BH had anxiolytic, antidepressant, and anti-inflammatory properties.

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Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive's vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, UT, USA. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 linear and 3,703,200 non-linear (random forest and support vector machine) regressors to predict bee motion counts from time, weather, and electromagnetic radiation. In all regressors, electromagnetic radiation was as good a predictor of traffic as weather. Both weather and electromagnetic radiation were better predictors than time. On the 13,412 time-aligned weather, electromagnetic radiation, and bee traffic records, random forest regressors had higher maximum R2 scores and resulted in more energy efficient parameterized grid searches. Both types of regressors were numerically stable.

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We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 min allows us to form a multivariable time series that serves in two ways: (a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and (b) based on historical data predict future levels that are correlated with hive's health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers.

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In precision beekeeping, the automatic recognition of colony states to assess the health status of bee colonies with dedicated hardware is an important challenge for researchers, and the use of machine learning (ML) models to predict acoustic patterns has increased attention. In this work, five classification ML algorithms were compared to find a model with the best performance and the lowest computational cost for identifying colony states by analyzing acoustic patterns. Several metrics were computed to evaluate the performance of the models, and the code execution time was measured (in the training and testing process) as a CPU usage measure. Furthermore, a simple and efficient methodology for dataset prepossessing is presented; this allows the possibility to train and test the models in very short times on limited resources hardware, such as the Raspberry Pi computer, moreover, achieving a high classification performance (above 95%) in all the ML models. The aim is to reduce power consumption and improves the battery life on a monitor system for automatic recognition of bee colony states.

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Honey bees (Apis spp.) are often used as biological indicators of environmental changes. Recently, bees have been explored to monitor air contaminants by listening to the beehive sound. The beehive sound is believed to encode information on bee responses to chemicals outside their hives. Here we conducted an experiment to address this. First, we randomly fed colonies with pure syrup (PS), acetone-laced syrup (AS), or ethyl acetate-laced syrup (ES) in front of the beehives and collect the beehive sound. Based on the audio data, we build machine learning (ML) models to identify the types of syrup. The result shows that ML models achieved over 90% accuracy for identifying syrup types, indicating that the bees inside their hives emitted the sound associated with the chemicals outside their hives. Then, we sequentially fed the colonies in the order of PS, ES, and AS (the first session) and again in the reverse order (the second session), but did not remove the accumulated ES or AS in the alternative feeding experiment. Based on the audio data, the identification accuracy of both ES and AS by the ML model built on the randomly feeding experiment was different, indicating that the accumulated chemical residuals could confuse the ML models. Therefore, the beehive sound-based environmental monitoring should simultaneously consider the responses of bees to the chemicals outside their hives and their responses to those accumulated inside their hives, which may act simultaneously.

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Bees are precious living beings for our planet. Thanks to their essential service of pollination, these insects allow the maintenance of biodiversity and the variety and amount of food available. Unfortunately, we are observing an increasingly devastating reduction of bee families and other pollinating insects for factors related to human activities, environmental pollution, diseases and parasites, compromise of natural habitats, and climate change. We show that probiotics can protect bees from element pollution. We collected bees, beeswax, honey, pollen, and propolis directly from hives in a rural area of central Italy to investigate the content of 41 elements in control (not supplemented with probiotics) and experimental (supplemented with probiotics) groups. Our data show a significantly lower concentration of some elements (Ba, Be, Cd, Ce, Co, Cu, Pb, Sn, Tl, and U) in experimental bees than in control groups, indicating a possible beneficial effect of probiotics in reducing the absorption of chemicals. This study presents the first data on element levels after probiotics have been fed to bees and provides the basis for future research in several activities relating to the environment, agriculture, economy, territory, and medicine.

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The relationship between beehive weight and traffic is a fundamental open research problem for electronic beehive monitoring and digital apiculture, because weight and traffic affect many aspects of honeybee (Apis mellifera) colony dynamics. An investigation of this relationship was conducted with a nondisruptive two-sensor (scale and camera) system on the weight and video data collected on six Apis mellifera colonies in Langstroth hives at the USDA-ARS Carl Hayden Bee Research Center in Tucson, Arizona, USA, from 15 May to 15 August 2021. Three hives had positive and two hives had negative correlations between weight and traffic. In one hive, weight and traffic were uncorrelated. The strength of the correlation between weight and traffic was stronger for longer time intervals. The traffic spread and mean, when taken separately, did not affect the correlation between weight and traffic more significantly than the exact traffic counts from videos. Lateral traffic did not have a significant impact on weight.

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The perceptions and definitions of healthy indoor environments have changed significantly throughout architectural history. Today, molecular biology teaches us that microbes play important roles in human health, and that isolation from them puts not only us but also other inhabitants of urban landscapes, at risk. In order to provide an environment that makes honeybees more resilient to environmental changes, we aim for combining the thermal insulation functionality of mycelium materials with bioactive therapeutic properties within beehive constructions. By identifying mycelial fungi's interactions with nest-related materials, using digital methods to design a hive structure, and engaging in additive manufacturing, we were able to develop a set of methods for designing and fabricating a fully grown hive. We propose two digital methods for modelling 3D scaffolds for micro-super organism co-occupation scenarios: "variable-offset" and "iterative-subtraction", followed by two inoculation methods for the biofabrication of scaffolded fungal composites. The HIVEOPOLIS project aims to diversify and complexify urban ecological niches to make them more resilient to future game changers such as climate change. The combined functions of mycelium materials have the potential to provide a therapeutic environment for honeybees and, potentially, humans in the future.

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The honeybee Apis mellifera is highly appreciated worldwide because of its products, but also as it is a pollinator of crops and wild plants. The beehive is vulnerable to infections due to arthropods, fungi, protozoa, bacteria and/or viruses that manage to by-pass the individual and social immune mechanisms of bees. Due to the close proximity of bees in the beehive and their foraging habits, infections easily spread within and between beehives. Moreover, international trade of bees has caused the global spread of infections, several of which result in significant losses for apiculture. Only in a few cases can infections be diagnosed with the naked eye, by direct observation of the pathogen in the case of some arthropods, or by pathogen-associated distinctive traits. Development of molecular methods based on the amplification and analysis of one or more genes or genomic segments has brought significant progress to the study of bee pathogens, allowing for: (i) the precise and sensitive identification of the infectious agent; (ii) the analysis of co-infections; (iii) the description of novel species; (iv) associations between geno- and pheno-types and (v) population structure studies. Sequencing of bee pathogen genomes has allowed for the identification of new molecular targets and the development of specific genotypification strategies.

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The World Health Organization warns that the alarming increase in antibiotic resistant bacteria will lead to 2.7 million deaths annually due to the lack of effective antibiotic therapies. Clearly, there is an urgent need for short-term alternatives that help to alleviate these alarming figures. In this respect, the scientific community is exploring neglected ecological niches from which the prototypical antibiotic-producing bacteria Streptomycetes are expected to be present. Recent studies have reported that honeybees and their products carry Streptomyces species that possess strong antibacterial activity. In this study, we have investigated the antibiotic profile of two Streptomycetes strains that were isolated from beehives. One of the isolates is the strain Streptomyces albus AN1, which derives from pollen, and shows potent antimicrobial activity against Candida albicans. The other isolate is the strain Streptomyces griseoaurantiacus AD2, which was isolated from honey, and displays a broad range of antimicrobial activity against different Gram-positive bacteria, including pathogens such as Staphylococcus aureus and Enterococus faecalis. Cultures of S. griseoaurantiacus AD2 have the capacity to produce the antibacterial compounds undecylprodigiosin and manumycin, while those of S. albus AN1 accumulate antifungal compounds such as candicidins and antimycins. Furthermore, genome and dereplication analyses suggest that the number of putative bioactive metabolites produced by AD2 and AN1 is considerably high, including compounds with anti-microbial and anti-cancer properties. Our results postulate that beehives are a promising source for the discovery of novel bioactive compounds that might be of interest to the agri-food sector and healthcare pharmaceuticals.