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The evolutionary changes from solitary to eusocial living in vertebrates and invertebrates are associated with the diversification of social interactions and the development of queen and worker castes. Despite strong innate patterns, our understanding of the mechanisms manifesting these sophisticated behaviors is still rudimentary. Here, we show that doublesex (dsx) manifests group-supporting behaviors in the honeybee (Apis mellifera) worker caste. Computer-based individual behavioral tracking of worker bees with biallelic stop mutations in colonies revealed that the dsx gene is required for the rate and duration of group-supporting behavior that scales the relationship between bees and their work. General sensorimotor functions remained unaffected. Unexpectedly, unlike in other insects, the dsx gene is required for the neuronal wiring of the mushroom body in which the gene is spatially restricted expressed. Together, our study establishes dedicated programming for group-supporting behaviors and provides insight into the connection between development in the neuronal circuitry and behaviors regulating the formation of a eusocial society. [ABSTRACT FROM AUTHOR]

The grooming behavior of honeybees serves as a crucial auto-protective mechanism against Varroa mite infestations. Compared to Apis mellifera, Apis cerana demonstrates more effective grooming behavior in removing Varroa mites from the bodies of infested bees. However, the underlying mechanisms regulating grooming behavior remain elusive. In this study, we evaluated the efficacy of the auto-grooming behavior between A. cerana and A. mellifera and employed RNA-sequencing technology to identify differentially expressed genes (DEGs) in bee brains with varying degrees of grooming behavior intensity. We observed that A. cerana exhibited a higher frequency of mite removal between day 5 and day 15 compared to A. mellifera, with day-9 bees showing the highest frequency of mite removal in A. cerana. RNA-sequencing results revealed the differential expression of the HTR2A and SLC17A8 genes in A. cerana and the CCKAR and TpnC47D genes in A. mellifera. Subsequent homology analysis identified the HTR2A gene and SLC17A8 gene of A. cerana as homologous to the HTR2A gene and SLC17A7 gene of A. mellifera. These DEGs are annotated in the neuroactive ligand–receptor interaction pathway, the glutamatergic synaptic pathway, and the calcium signaling pathway. Moreover, CCKAR, TpnC47D, HTR2A, and SLC17A7 may be closely related to the auto-grooming behavior of A. mellifera, conferring resistance against Varroa infestation. Our results further explain the relationship between honeybee grooming behavior and brain function at the molecular level and provide a reference basis for further studies of the mechanism of honeybee grooming behavior. [ABSTRACT FROM AUTHOR]

The article explores how bees, particularly honeybees, exhibit cumulative cultural evolution through social learning and information transmission. Researchers have studied the honeybee dance language, which conveys food location information, and found that social factors influence the accuracy of certain components of this communication system. Additionally, recent research on bumblebees has shown that they can learn complex behaviors through social learning, challenging the notion that such behaviors are unique to humans. The study of honeybee and bumblebee behaviors provides valuable insights into social learning and cumulative cultural evolution in nonhuman animals, highlighting the shared intelligence and adaptability across the animal kingdom. [Extracted from the article]

Honeybees are the most critical pollinators providing key ecosystem services that underpin crop production and sustainable agriculture. Amidst a backdrop of rapid global change, this eusocial insect encounters a succession of stressors during nesting, foraging, and pollination. Ectoparasitic mites, together with vectored viruses, have been recognized as central biotic threats to honeybee health, while the spread of invasive giant hornets and small hive beetles also increasingly threatens colonies worldwide. Cocktails of agrochemicals, including acaricides used for mite treatment, and other pollutants of the environment have been widely documented to affect bee health in various ways. Additionally, expanding urbanization, climate change, and agricultural intensification often result in the destruction or fragmentation of flower‐rich bee habitats. The anthropogenic pressures exerted by beekeeping management practices affect the natural selection and evolution of honeybees, and colony translocations facilitate alien species invasion and disease transmission. In this review, the multiple biotic and abiotic threats and their interactions that potentially undermine bee colony health are discussed, while taking into consideration the sensitivity, large foraging area, dense network among related nestmates, and social behaviors of honeybees. [ABSTRACT FROM AUTHOR]

Simple Summary: Honeybees, Apis melllifera, are the most widely used bees in the world for pollination services. However, in recent years, continuous colony losses have been recorded worldwide. One of the factors behind these losses is associated with diseases specific to this species, such as those caused by viruses. One of the known viruses affecting honeybee populations is the deformed wing virus (DWV). DWV causes physical malformations, decreased olfactory sensitivity, learning difficulties, and behavioral alterations, which can compromise hive behaviors. Thus, we evaluated behavioral response, the expression of antenna-specific odorant-binding protein (OBP) genes, and brain genes related to bee behavior, especially nurse bees, in honeybees inoculated with the DWV variant A. We performed olfactory sensitivity analyses in beehives. We performed behavioral assays with the larvae-emitted alarm pheromone component to stimulate infected nurse bees. We found that high levels of viral replication in both the head and antennae altered the behavioral response, decreasing attraction to the pheromone component, and DWV-A infection decreased the gene expression of OBPs and brain genes. Thus, DWV-A infection in adult bees could compromise internal hive cohesion and A. mellifera nurse bee behaviors. Insect behavior is coordinated mainly by smell through the diverse odor-binding proteins (OBP) that allow them to identify and recognize their environment. Sensory information collected through smell is then analyzed and interpreted in the brain, allowing for correct insect functioning. The behavior of honeybees (Apis mellifera L.) can be affected by different pathogens, such as deformed wing virus (DWV). In particular, the DWV variant A (DWV-A) is capable of altering olfactory sensitivity and reducing the gene expression of different OBPs, including those associated with nursing behavior. The DWV is also capable of replicating itself in the sensory lobes of the brain, further compromising the processing of sensory information. This study evaluated the behavioral response of nurse honeybees exposed to a pheromone compound and the alterations in the gene expression of the pre- and post-synaptic neuronal genes neuroxins-1 and neurogilin-1 in the bee heads and OBP proteins in the antennae of nurse bees inoculated with DWV-A. The behavioral response of nurse bees exposed to the larval pheromone compound benzyl alcohol was analyzed using a Y-tube olfactometer. The viral load, the gene expression of OBP5 and OBP11 in antennae, and neuroxins-1 and neurogilin-1 in the bee heads were analyzed via qPCR. High viral loads significantly reduced the ability of 10- and 15-day-old nurse honeybees to choose the correct pheromone compound. Also, the gene expression of OBP5, OBP11, neuroxin-1, and neurogilin-1 in nurse honeybees decreased when they were highly infected with DWV-A. These results suggest that a DWV-A infection can disturb information processing and cause nursing honeybees to reduce their activity inside the hive, altering internal cohesion. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing concern regarding the impact of pesticide exposure on pollinators, particularly honeybees (Apis mellifera L.). This concern arises from their crucial role in maintaining ecological balance and global food production. Therefore, an extensive investigation has been conducted to explore the intricate relationship between pesticides and the biological development of honey bees in the Rahim Yar Khan region. This study assessed the impact of four pesticides (Emamectin benzoate, Chlorpyrifos, Imidacloprid, and Acetamiprid) on honey bee eggs, larvae, and adult bees under controlled laboratory conditions. The pesticides were applied at concentrations of 10%, 30%, and 50%, as per regional agricultural recommendations. A zero-toxicity control was also included for comparison. Toxicity evaluations were conducted through contact exposure, and Probit and regression analyses were performed using SPSS software to comprehensively assess the toxicity profiles. The study revealed significant adverse effects on the immediate behavioral responses of A. mellifera following pesticide exposure. These effects included heightened agitation, narcotic-like symptoms, audible hovering, crawling, ceased food-sharing behavior, and reduced proboscis extension. Chlorpyrifos exhibited the highest toxicity against adult bees, while Emamectin Benzoate had the least toxicity. Regarding honey bee eggs, Chlorpyrifos, Imidacloprid, and Acetamiprid were notably more toxic, whereas Emamectin Benzoate exhibited the least toxicity. The impact on larvae varied across developmental stages and pesticides, with Imidacloprid, Chlorpyrifos, and Acetamiprid causing significant mortality, while Emamectin Benzoate showed lower toxicity. The study highlights that Emamectin Benzoate demonstrates lower toxicity compared to other insecticides. This emphasizes the importance of balancing effective pest management with the preservation of pollinator health. The findings underscore the need for informed and sustainable approaches to pesticide use, taking into consideration the potential repercussions on honeybee development and behavior. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, numerous researchers are attracted to study new heuristic algorithms and improve traditional algorithms. Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of honeybees, which is one of the most widely applied methods to solve optimization problems. However, the traditional ABC has some shortcomings such as under-exploitation and slow convergence, etc. In this study, a novel variant of ABC named chaotic and neighborhood search-based ABC algorithm (CNSABC) is proposed. The CNSABC contains three improved mechanisms, including Bernoulli chaotic mapping with mutual exclusion mechanism, neighborhood search mechanism with compression factor, and sustained bees. In detail, Bernoulli chaotic mapping with mutual exclusion mechanism is introduced to enhance the diversity and the exploration ability. To enhance the convergence efficiency and exploitation capability of the algorithm, the neighborhood search mechanism with compression factor and sustained bees are presented. Subsequently, a series of experiments are conducted to verify the effectiveness of the three presented mechanisms and the superiority of the proposed CNSABC, the results demonstrate that the proposed CNSABC has better convergence efficiency and search ability. Finally, the CNSABC is applied to solve two engineering optimization problems, experimental results show that CNSABC can produce satisfactory solutions. [ABSTRACT FROM AUTHOR]

Simple Summary: In the last decade, a large decline of honey bee colonies has been observed, caused by multiple factors that may affect them and contribute to their total extinction. Therefore, it is of the utmost importance to find successful ways of extensively monitoring beehives' status in order to detect potential threats. In this context, one of the states of a bee colony that should be monitored is the queenless state, so in the present research, we tried to investigate the behavior of honeybees during their queen's loss through recording and analyzing sound data from beehives over a certain period. Honeybees are reported to be the most vital pollinators of agricultural and horticultural crops. However, their widespread decline has raised great attention to the need to monitor their activity in order to identify the causes and implement countermeasures. The recording and analysis of signals used by honeybees for their communication in their hive can be a very helpful tool to the beekeepers for the remote control of the hives. Thus, in the present study, we used a set of sound recording data taken inside the hives to automatically detect the sounds of the bees over a certain period, to distinguish between queenright and queenless states, and to find the gradual changes in the queenless state. Unlike what was commonly believed, noticeable changes in the sound signals of all experimental colonies were observed just one hour after the queens' removal from the hive, while the sound signals were intensified over a period of 5 h, after which the transmitted signal stabilized to the equivalent of a queenless state. The colonies seemed to return to their normal sounds 9–10 days after the reintroduction of the queens in the hives. Our study concluded that timely intervention of the queen's absence combined with the immediate intervention of the beekeeper may be a determining factor in mitigating the adverse effects that occur from the queen's loss. [ABSTRACT FROM AUTHOR]

The flexibility of insect wings should be considered in the design of bionic micro flapping-wing aircraft. The honeybee is an ideal biomimetic object because its wings are small and possess a concise vein pattern. In this paper, we focus on resilin, an important flexible factor in honeybees' forewings. Both resilin joints and resilin stripes are considered in the finite element model, and their mechanical behaviors are studied comprehensively. Resilin was found to increase the static deflections in chordwise and spanwise directions by 1.4 times and 1.9 times, respectively. In modal analysis, natural frequencies of the first bending and first torsional modes were found to be decreased significantly—especially the latter, which was reduced from 500 Hz to 217 Hz—in terms of resilin joints and stripes, closely approaching flapping frequency. As a result, the rotational angle amplitude in dynamic responses is remarkable, with an amplification ratio of about six. It was also found that resilin joints and stripes together lead to well-cambered sections and improve the stress concentrations in dynamic deformation. As resilin is widespread in insect wings, the study could help our understanding of the flexible mechanism of wing structure and inspire the development of flexible airfoils. [ABSTRACT FROM AUTHOR]

Vairimorpha (=Nosema) ceranae is a widespread pollinator parasite that commonly infects honeybees and wild pollinators, including bumblebees. Honeybees are highly competent V. ceranae hosts and previous work in experimental flight cages suggests V. ceranae can be transmitted during visitation to shared flowers. However, the relationship between floral visitation in the natural environment and the prevalence of V. ceranae among multiple bee species has not been explored. Here, we analyzed the number and duration of pollinator visits to particular components of squash flowers—including the petals, stamen, and nectary—at six farms in southeastern Michigan, USA. We also determined the prevalence of V. ceranae in honeybees and bumblebees at each site. Our results showed that more honeybee flower contacts and longer duration of contacts with pollen and nectar were linked with greater V. ceranae prevalence in bumblebees. Honeybee visitation patterns appear to have a disproportionately large impact on V. ceranae prevalence in bumblebees even though honeybees are not the most frequent flower visitors. Floral visitation by squash bees or other pollinators was not linked with V. ceranae prevalence in bumblebees. Further, V. ceranae prevalence in honeybees was unaffected by floral visitation behaviors by any pollinator species. These results suggest that honeybee visitation behaviors on shared floral resources may be an important contributor to increased V. ceranae spillover to bumblebees in the field. Understanding how V. ceranae prevalence is influenced by pollinator behavior in the shared floral landscape is critical for reducing parasite spillover into declining wild bee populations. [ABSTRACT FROM AUTHOR]

Vision plays a crucial role in the ability of compound-eyed insects to perceive the characteristics of their surroundings. Compound-eyed insects (such as the honeybee) can change the optical flow input of the visual system by autonomously controlling their behavior, and this is referred to as visual–motor coordination (VMC). To analyze an insect's VMC mechanism in dynamic scenes, we developed a platform for studying insects that actively shape the optic flow of visual stimuli by adapting their flight behavior. Image-processing technology was applied to detect the posture and direction of insects' movement, and automatic control technology provided dynamic scene stimulation and automatic acquisition of perceptual insect behavior. In addition, a virtual mapping technique was used to reconstruct the visual cues of insects for VMC analysis in a dynamic obstacle scene. A simulation experiment at different target speeds of 1–12 m/s was performed to verify the applicability and accuracy of the platform. Our findings showed that the maximum detection speed was 8 m/s, and triggers were 95% accurate. The outdoor experiments showed that flight speed in the longitudinal axis of honeybees was more stable when facing dynamic barriers than static barriers after analyzing the change in geometric optic flow. Finally, several experiments showed that the platform can automatically and efficiently monitor honeybees' perception behavior, and can be applied to study most insects and their VMC. [ABSTRACT FROM AUTHOR]

The wild honeybees, Apis dorsata Fabricius, 1793 and Apis florea Fabricius, 1787 constitute a large proportion of pollinators' abundance on most crop species in Punjab, Pakistan. In the present study, we studied the foraging behavior of both the honeybee species and their comparative role in the reproductive performance of the strawberry at the research farm of The Islamia University of Bahawalpur. Both the species comprised 53% of the total pollinators' abundance. Apis florea had high visitation frequency (0.449±0.03 individuals per 120 seconds) than A. dorsata (0.211±0.02). The activity of A. florea was the maximum at 12:00 pm while A. dorsata remained active in low abundance throughout the day. Apis florea exhibited a higher stay time but a lower visitation rate than A. dorsata. Apis dorsata proved to be an effective pollinator of the strawberry rather than A. florea in terms of fruit length, weight and diameter. There is a need to conserve wild honeybees under the current scenario of anthropogenic land use changes and intensification in agriculture. [ABSTRACT FROM AUTHOR]

The use of agricultural neonicotinoid insecticides has sub-lethal chronic effects on bees that are more prevalent than acute toxicity. Among these insecticides, thiacloprid, a commonly used compound with low toxicity, has attracted significant attention due to its potential impact on the olfactory and learning abilities of honeybees. The effect of sub-lethal larval exposure to thiacloprid on the antennal activity of adult honeybees (Apis mellifera L.) is not yet fully understood. To address this knowledge gap, laboratory-based experiments were conducted in which honeybee larvae were administered thiacloprid (0.5 mg/L and 1.0 mg/L). Using electroantennography (EAG), the impacts of thiacloprid exposure on the antennal selectivity to common floral volatiles were evaluated. Additionally, the effects of sub-lethal exposure on odor-related learning and memory were also assessed. The results of this study reveal, for the first time, that sub-lethal larval exposure to thiacloprid decreased honeybee antenna EAG responses to floral scents, leading to increased olfactory selectivity in the high-dose (1.0 mg/L) group compared to the control group (0 mg/L vs. 1.0 mg/L: p = 0.042). The results also suggest that thiacloprid negatively affected odor-associated paired learning acquisition, as well as medium-term (1 h) (0 mg/L vs. 1.0 mg/L: p = 0.019) and long-term memory (24 h) (0 mg/L vs. 1.0 mg/L: p = 0.037) in adult honeybees. EAG amplitudes were dramatically reduced following R-linalool paired olfactory training (0 mg/L vs. 1.0 mg/L: p = 0.001; 0 mg/L vs. 0.5 mg/L: p = 0.027), while antennal activities only differed significantly in the control between paired and unpaired groups. Our results indicated that exposure to sub-lethal concentrations of thiacloprid may affect olfactory perception and learning and memory behaviors in honeybees. These findings have important implications for the safe use of agrochemicals in the environment. [ABSTRACT FROM AUTHOR]

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Simple Summary: Honeybees are known for their ability to communicate about resources in their environment. They inform the other foragers by performing specific dance sequences according to the spatial characteristics of the resource. The purpose of our study is to provide a new tool for honeybees dances recording, usable in the field, in a practical and fully automated way, without condemning the harvest of honey. We designed and equipped an outdoor prototype hive, later called "GeoDanceHive", allowing the continuous recording of honeybees' dances and their analysis. The use of the GeoDanceHive is designed for a wide range of users, who can meet different objectives, such as researchers or professional beekeepers. Thus, our hive is a powerful tool for honeybees studies in the field and could highly contribute to facilitating new research approaches and better understanding landscape ecology of key pollinators. Honeybees are known for their ability to communicate about resources in their environment. They inform the other foragers by performing specific dance sequences according to the spatial characteristics of the resource. The purpose of our study is to provide a new tool for honeybees dances recording, usable in the field, in a practical and fully automated way, without condemning the harvest of honey. We designed and equipped an outdoor prototype of a production hive, later called "GeoDanceHive", allowing the continuous recording of honeybees' behavior such as dances and their analysis. The GeoDanceHive is divided into two sections, one for the colony and the other serving as a recording studio. The time record of dances can be set up from minutes to several months. To validate the encoding and sampling quality, we used an artificial feeder and visual decoding to generate maps with the vector endpoints deduced from the dance information. The use of the GeoDanceHive is designed for a wide range of users, who can meet different objectives, such as researchers or professional beekeepers. Thus, our hive is a powerful tool for honeybees studies in the field and could highly contribute to facilitating new research approaches and a better understanding landscape ecology of key pollinators. [ABSTRACT FROM AUTHOR]

Honeybees (Apis mellifera) are critical to maintaining ecological balance and are important pollinators. The oviposition behavior in honeybees is important and complex. Circular RNAs (circRNAs) are found to form circRNA-miRNA crosstalk and play important roles in reproduction processes. Here, dual luciferase reporter was used to confirm the crosstalk between ame_circ_2015 and ame_miR-14-3p. Functional experiments in vitro and in vivo were performed to investigate the biological functions of ame_circ_2015 in egg-laying of queens. The results showed that ame_circ_2015 directly target ame_miR-14-3p, and the expression of ame_circ_2015 was negatively correlated with ame_miR-14-3p expression. Overexpression results showed that ame_circ_2015 promoted the number of eggs laid and knockdown of ame_circ_2015 suppressed the number of eggs laid. It demonstrates that up-regulated ame_circ_2015 promotes the number of eggs laid by sponging ame_miR-14-3p. The study will provide information towards a better understanding of circRNA-miRNA crosstalk in egg-laying in honeybees. [ABSTRACT FROM AUTHOR]

Assessing the porosity in Selective Laser Melting (SLM) parts is a challenging issue, and the drawback of using the existing gray value analysis method to assess the porosity is the difficulty and subjectivity in selecting a uniform grayscale threshold to convert a single slice to binary image to highlight the porosity. This paper proposes a new approach based on the use of a Regression Convolutional Neural Network (RCNN) algorithm to predict the percent of porosity in CT scans of finished SLM parts, without the need for subjective difficult thresholding determination to convert a single slice to a binary image. In order to test the algorithm, as the training of the RCNN would require a large amount of experimental data, this paper proposed a new efficient approach of creating artificial porosity images mimicking the real CT scan slices of the finished SLM part with a similarity index of 0.9976. Applying RCNN improved porosity prediction accuracy from 68.60% for image binarization method to 75.50% using the RCNN. The algorithm was then further developed by optimizing its parameters using Bees Algorithm (BA), which is known to mimic the behavior of honeybees, and the hybrid Bees Regression Convolutional Neural Network (BA-RCNN) produced better prediction accuracy with a value of 85.33%. [ABSTRACT FROM AUTHOR]

Vision plays a crucial role in the ability of compound-eyed insects to perceive the characteristics of their surroundings. Compound-eyed insects (such as the honeybee) can change the optical flow input of the visual system by autonomously controlling their behavior, and this is referred to as visual–motor coordination (VMC). To analyze an insect’s VMC mechanism in dynamic scenes, we developed a platform for studying insects that actively shape the optic flow of visual stimuli by adapting their flight behavior. Image-processing technology was applied to detect the posture and direction of insects’ movement, and automatic control technology provided dynamic scene stimulation and automatic acquisition of perceptual insect behavior. In addition, a virtual mapping technique was used to reconstruct the visual cues of insects for VMC analysis in a dynamic obstacle scene. A simulation experiment at different target speeds of 1–12 m/s was performed to verify the applicability and accuracy of the platform. Our findings showed that the maximum detection speed was 8 m/s, and triggers were 95% accurate. The outdoor experiments showed that flight speed in the longitudinal axis of honeybees was more stable when facing dynamic barriers than static barriers after analyzing the change in geometric optic flow. Finally, several experiments showed that the platform can automatically and efficiently monitor honeybees’ perception behavior, and can be applied to study most insects and their VMC.

The division of labor is a dominant characteristic of honeybees and is accompanied by behavioral specialization and cognitive enhancement. As the central nervous system to control the labor-specific behaviors of honeybee, the brain is richest in lipid in terms of both diversity and abundance. In this study, an in-depth LC-MS/MS-based lipidomic method was applied to systematically characterize the brain lipid compositions of worker bees with three labor stages: newly emerged bee (NEB), nurse bee (NB), and forager bee (FB). A total number of 337 lipid species that assigned to 20 lipid classes were analyzed. The association of the brain lipidomes with the division of labors was suggested by the results of both the unsupervised and supervised multivariate pattern recognition analysis. More than 68% of the identified lipid species were found to be significantly changed in at least one comparison between NEB, NB, and FB. A total of 81 lipid species were identified as the potential labor-featured molecules with VIP > 1 and p-adj < 0.05. The labor-featured lipids of FA(18:2), FA(18:3), FA(26:0), PC(18:0_18:3), PS(18:1_18:1), SM(d38:1), CoQ10, and CoQ9, as well as their interactions with 12 behavior-related genes, including AmEST-6, AmFABP, AmE75, AmDGAT2, AmLSD1, AmNPC1, AmABCA1, AmNMDAR1, AmHTT, AmNOS, etc., were revealed by the further IPA analysis. These findings demonstrate for the first time that the brain lipidomes of worker bees are associated with the stable differences in their labors, which help understand the function of brain lipids on the labor-dependent behaviors of honeybees. [ABSTRACT FROM AUTHOR]

• Landscape composition modulates foraging behavior of faba bean pollinators. • Landscape composition affects bee pollinator densities in bean fields. • High semi-natural habitat land covers increase bee densities in bean fields. • Insect pollination increases number of beans per pod in faba beans. • Bee density effects on yield components depend on landscape composition. Wildlife-friendly management practices promote pollinators and pollination services in agricultural landscapes. Wild bee densities are driven by landscape composition, as they benefit from an increased availability of nesting and foraging resources at landscape scale. However, effects of landscape composition on bee foraging decisions and consequences for crop pollination have rarely been studied. We investigated, how landscape composition affects bee densities and foraging behavior in faba bean (Vicia faba L.) fields and how this impacts faba bean yield. We recorded densities and nectar robbing behavior of honeybees, long- tongued and short-tongued bumblebees in faba bean fields in eleven landscapes with varying landscape composition (e.g. land cover of oilseed rape, faba bean and semi-natural habitats). Moreover, we assessed yield components of faba beans via pollinator exclusion experiments. Increasing covers of faba bean and semi-natural habitats positively influenced bumblebee densities, while high oilseed rape covers negatively affected short-tongued bumblebee densities in bean fields. Increased faba bean covers enhanced the proportion of nectar-robbing short-tongued bumblebees. The number of beans per pod was increased by insect pollination, while the number of pods was decreased; these effects however depended on variety. Landscape composition interacted with bee densities in shaping yield components in V. faba. Our study emphasizes the importance of considering landscape management to maximize crop yields, as shown for the case of faba beans. The composition of agricultural landscape can modulate bee densities in crop fields, bees foraging behavior and pollination services. [ABSTRACT FROM AUTHOR]

Heart disease is a noteworthy global health concern, profoundly affecting public health and individual well-being. Early prediction and diagnosis are crucial for reducing its negative impact. This work focuses on developing an effectual heart disease detection employing advanced optimization approaches and data processing. The proposed system incorporates a hybrid segmentation algorithm, Hybrid K-means-Fuzzy-C-means (HKMFCM) clustering, which assigns membership degrees to data points, enabling soft clustering and accommodating data uncertainty. Additionally, the Artificial Bee Colony (ABC) Optimization method is applied for feature selection, mimicking the foraging behavior of honeybees to identify the most discriminative attributes from the dataset. This optimization algorithm iteratively explores the feature space to select features that enhance the model's predictive accuracy. Furthermore, a novel classification architecture, termed the Attention-infused BiRecurrenTwin Network, is introduced to accurately predict heart disease based on segmented and extracted patient data profiles. This classifier leverages both Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks, with their bidirectional nature capturing both past and future contexts, thus increasing the classifier's capability to detect subtle temporal patterns in patient data. In addition, the proposed system addresses traditional approaches limitations through its advanced components like HKMFCM (effectively manages data uncertainty by enabling soft clustering), ABC Optimization technique (enabling an iterative, global search for the most discriminative attributes) Attention-infused BiRecurrenTwin Network (surpasses conventional classifiers by capturing both past and future temporal patterns in patient data). The simulation outcomes demonstrate that the developed system attains improved performance, with accuracy, ROC, F-measure, precision, recall, sensitivity, and specificity values of 96.09%, 97%, 95%, 96.6%, 94.3%, 95.14%, and 97.05%, respectively which is higher than the baseline models with an average of 7.75 % increase in accuracy. These results indicate that the developed predictive models show promise in accurately classifying individuals based on their extracted features, thereby facilitating the early recognition of heart disease. [ABSTRACT FROM AUTHOR]

In fact, less than 1% of applied pesticides reach their target pests, while the remainder pollute the neighboring environment and adversely impact human health as well as non-target organisms in agricultural ecosystem. Pesticides can contribute to the loss of agrobiodiversity, which are essential to maintaining the agro-ecosystem's structure and functioning in order to produce and secure enough food. This review article examines the negative effects of pesticides on non-target invertebrates including earthworms, honeybees, predators, and parasitoids. It also highlights areas where further research is needed to address unresolved issues related to pesticide exposure, aiming to improve conservation efforts for these crucial species. These organisms play crucial roles in ecosystem functioning, such as soil health, pollination, and pest control. Both lethal and sub-lethal effects of pesticides on the selected non-target invertebrates were discussed. Pesticides affect DNA integrity, enzyme activity, growth, behavior, and reproduction of earthworms even at low concentrations. Pesticides could also induce a reduction in individual survival, disruption in learning performance and memory, as well as a change in the foraging behavior of honeybees. Additionally, pesticides adversely affect population growth indices, reproduction, development, longevity, and consumption of predators and parasitoids. As a result, pesticides must pass adequate ecotoxicological risk assessment to be enlisted by regulatory authorities. Therefore, it is important to adopt integrated pest management (IPM) strategies that minimize pesticide use and promote the conservation of beneficial organisms in order to maintain agrobiodiversity and sustainable agricultural systems. Furthermore, adopting precision agriculture and organic farming lessen these negative effects as well.less than [Display omitted] • The detrimental effects of pesticides on non-target invertebrates were discussed. • Pesticides affect the DNA, growth, behavior, and reproduction of earthworms. • Pesticides induce cognitive, flight, immunity, and foraging disorders in honeybees. • Pesticides affect growth, reproduction, and longevity of predators and parasitoids. • IPM and precision agriculture should be adopted to lessen these negative effects. [ABSTRACT FROM AUTHOR]

Honeybees are highly social insects with a rich behavioral repertoire and are a versatile model for neurobiological research. Their gut microbiota comprises a limited number of host-restricted bacterial phylotypes that are important for honeybee health. However, it remains unclear how specific gut members affect honeybee behaviors. Here, we find that antibiotic exposure disturbs the gut community and influences honeybee phenotypes under field conditions. Using laboratory-generated gnotobiotic bees, we show that a normal gut microbiota is required for olfactory learning and memory abilities. Brain transcriptomic profiling reveals distinct brain gene expression patterns between microbiota-free and conventional bees. Subsequent metabolomic analyses of both hemolymph and gut samples show that the microbiota mainly regulates tryptophan metabolism. Our results indicate that host-specific Lactobacillus strains promote memory behavior by transforming tryptophan to indole derivatives that activate the host aryl hydrocarbon receptor. Our findings highlight the contributions of specific gut members to honeybee neurological processes, thus providing a promising model to understand host-microbe interactions. Eusocial honeybee is a versatile model for neurobiological research. Here, the authors established a link between honeybee gut Lactobacillus in bee learning and memory ability via indole derivatives that activate host's aryl hydrocarbon receptor. [ABSTRACT FROM AUTHOR]

This review focuses on the potential effects of a desert locust infestation on Ethiopian honeybees. Data on the country's infestation, locust activity, honeybee foraging behavior, pesticide kinds, and application rates were collected and analyzed in connection to honeybee life and performance. Desert locust has damaged a considerable number of plants of various kinds, possibly causing pollen and nectar loss. As a result, honeybees are likely to produce less brood, less honey, suffer from poor health, and abscond. Besides, studies suggested that the use of pesticides to control the locust could directly harm honeybees. The pesticide was used for 21 days in a row to cover a huge region infected with locusts, which could have had a severe effect on honeybees. The probability of an influence is also indicated by the overlap of pesticide administration with honeybee foraging seasons and hours. Furthermore, forager bees leave their hive 1 to 13 times per day, spending about 3 hours outside each time, indicating higher chemical exposure. Malathion is one of the pesticides that could harm honeybees, while there is no comprehensive list of the chemicals used on the internet or anywhere else. Finally, the current desert locust invasion and eradication operation in Ethiopia may have caused substantial damage to honeybees as a result of bee forage loss and pesticide hazard, emphasizing the need for future precautions. Because this is speculative work based on evidence, detailed survey research is recommended to determine the actual impact imposed on honeybees. [ABSTRACT FROM AUTHOR]

Simple Summary: The most important honeydew-producing insect in Greece is Marchalina hellenica (Coccoidea: Marchalinidae), which is a parasite on pine trees. The current work is part of an ongoing research project aiming to provide knowledge on honeydew-producing insects and the impact of critical factors (climate, beekeeping manipulations, honeydew-producing insect phenology) on honeydew honey production. Empirical evidence indicates that among the weather factors, the most important one, at least for spring honeydew secretions, appears to be temperature and, more specifically, the existence of cold winter days. Presently, we investigate the effect of cold periods in February on the life cycle of Marchalina hellenica. Our primary goal is to help beekeepers plan the timely exploitation of honeydew secretions of pine trees. Such a potential will be beneficial for beekeepers, the rural economy, and forest protection. It should be noted that current results highlight the impact of climate change in the field of entomology, and they indicate that the life cycle of Marchalina hellenica is expected to be drastically shorter. Climate change is considered a major factor affecting honeybees' behavior and productivity with major consequences in both honey and agricultural production. Many research studies have expressed serious concerns about the mass losses of bee colonies and the role of bees as pollinators, while others have underlined important issues for the impact of the increase in temperature on honeybee abundance and honey yields. In the present work, we draw our attention to Marchalina hellenica, which is the most important honeydew-producing insect in Greece. A statistically significant forecasting model for the effect of cold periods in February on the life cycle of the insect is constructed, with the aid of the Cumulative Logit Model and the theory of runs. The forecasting model may help beekeepers plan the timely exploitation of honeydew secretions of pine trees, which will be beneficial for beekeepers, the rural economy, and forest protection. The new suggested model also indicates that, in view of the climate change scenarios seen in the literature, the life cycle of M. hellenica is expected to be drastically shorter. [ABSTRACT FROM AUTHOR]

Insects change their stimulus-response through the perception of associating these stimuli with important survival events such as rewards, threats, and mates. Insects develop strong associations and relate them to their experiences through several behavioral procedures. Among the insects, Apis species, Apis mellifera ligustica are known for their outstanding ability to learn with tremendous economic importance. Apis mellifera ligustica has a strong cognitive ability and promising model species for investigating the neurobiological basis of remarkable olfactory learning abilities. Here we evaluated the olfactory learning ability of A. mellifera by using the proboscis extension reflex (PER) protocol. The brains of the learner and failed-learner bees were examined for comparative transcriptome analysis by RNA-Seq to explain the difference in the learning capacity. In this study, we used an appetitive olfactory learning paradigm in the same age of A. mellifera bees to examine the differential gene expression in the brain of the learner and failed-learner. Bees that respond in 2nd and 3rd trials or only responded to 3rd trials were defined as learned bees, failed-learner individuals were those bees that did not respond in all learning trials The results indicate that the learning ability of learner bees was significantly higher than failed-learner bees for 12 days. We obtained approximately 46.7 and 46.4 million clean reads from the learner bees failed-learner bees, respectively. Gene expression profile between learners' bees and failed-learners bees identified 74 differentially expressed genes, 57 genes up-regulated in the brains of learners and 17 genes were down-regulated in the brains of the bees that fail to learn. The qRT-PCR validated the differently expressed genes. Transcriptome analyses revealed that specific genes in learner and failed-learner bees either down-regulated or up-regulated play a crucial role in brain development and learning behavior. Our finding suggests that down-regulated genes of the brain involved in the integumentary system, storage proteins, brain development, sensory processing, and neurodegenerative disorder may result in reduced olfactory discrimination and olfactory sensitivity in failed-learner bees. This study aims to contribute to a better understanding of the olfactory learning behavior and gene expression information, which opens the door for understanding of the molecular mechanism of olfactory learning behavior in honeybees. [ABSTRACT FROM AUTHOR]

Focus of this study is to design an automated image processing pipeline for handling uncontrolled acquisition conditions of images acquired in the field. The pipeline has been tested on the automated identification and count of uncapped brood cells in honeybee (Apis Mellifera) comb images to reduce the workload of beekeepers during the study of the hygienic behavior of honeybee colonies. The images used to develop and test the model were acquired by beekeepers on different days and hours in summer 2020 and under uncontrolled conditions. This resulted in images differing for background noise, illumination, color, comb tilts, scaling, and comb sizes. All the available 127 images were manually cropped to approximately include the comb area. To obtain an unbiased evaluation, the cropped images were randomly split into a training image set (50 images), which was used to develop and tune the proposed model, and a test image set (77 images), which was solely used to test the model. To reduce the effects of varied illuminations or exposures, three image enhancement algorithms were tested and compared followed by the Hough Transform, which allowed identifying individual cells to be automatically counted. All the algorithm parameters were automatically chosen on the training set by grid search. When applied to the 77 test images the model obtained a correlation of 0.819 between the automated counts and the experts' counts. To provide an assessment of our model with publicly available images acquired by a different equipment and under different acquisition conditions, we randomly extracted 100 images from a comb image dataset made available by a recent literature work. Though it has been acquired under controlled exposure, the images in this new set have varied illuminations; anyhow, our pipeline obtains a correlation between automatic and manual counts equal to 0.997. In conclusion, our tests on the automatic count of uncapped honey bee comb cells acquired in the field and on images extracted from a publicly available dataset suggest that the hereby generated pipeline successfully handles varied noise artifacts, illumination, and exposure conditions, therefore allowing to generalize our method to different acquisition settings. Results further improve when the acquisition conditions are controlled. [ABSTRACT FROM AUTHOR]

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Introduction: Insect cyborg is a kind of novel robot based on insect–machine interface and principles of neurobiology. The key idea is to stimulate live insects by specific stimuli; thus, the flight trajectory of insects could be controlled as anticipated. However, the neuroregulatory mechanism of insect flight has not been elucidated completely at present. Methods: To explore the neuro‐mechanism of insect flight behaviors, a series of electrical stimulation was applied on the optic lobes of semi‐constrained honeybees. Times of flight initiation, flapping frequency, and duration were recorded by a high‐speed camera. In addition, flapping and steering initiation experiments of the cyborg honeybee were verified. Moreover, series of local field potential signals of optic lobes during flapping were collected, pre‐processed to remove baseline wander and DC components, then analyzed by power spectrum estimation. Results: A quantitative optimization method and optimal stimulation parameters of flight initiation were presented. Stimulation results showed that the flapping duration differed greatly while the flapping frequency varied with little difference among different individuals. Moreover, there was always a fluctuation peak around 20–30 Hz in power spectral density (PSD) curves during flapping, distinguishing from calm state, which indicated some brain activity changes during flapping. Conclusions: Our study presented a range of relatively optimal electrical parameters to initiate honeybee flight behavior. Meanwhile, the regularity of flapping duration and flapping frequency under electrical stimulations with different parameters were given. The feasibility of controlling a honeybee's flight behavior by brain electrical stimulation was verified through the flapping and steering initiation experiment of honeybees under semi‐constrained state. PSD fluctuations reflected changes in brain activity during flapping and that those fluctuation characteristics at the specific frequency band could be sensitive determinants to distinguish whether the honeybee was flying or not, which benefits our understanding of honeybee's flapping behavior and furthers the study of honeybee cyborgs. [ABSTRACT FROM AUTHOR]

In Mexico, beekeeping is an activity of economic, social and ecological importance that faces various problems; two of the most important problems are the high defensive behavior of honeybee colonies (Apis mellifera L.) caused by the africanization and varroosis caused by the mite Varroa destructor. The high defensive behavior of the colonies has made beekeeping more complex and less profitable. Varroa destructor affects honey production and is a factor that has been associated with honeybee colony losses worldwide. To address these problems, IN1FAP conducts research on honeybee genetics and breeding. The objective of this article was to make a review of the research conducted at INIFAP in honeybee genetics that has created scientific knowledge about the genetic, genomic, and epigenetic factors that regulate the expression of honeybee defensive behavior, guarding behavior, stinging behavior, grooming behavior and hygienic behavior. To review the results of research conducted by INIFAP in honeybee breeding to reduce the defensive behavior of honeybee colonies, this research has created methods to evaluate and to select this trait, and has generated honeybee lines with low defensive behavior, from which queens has been transferred to beekeepers. As well as to review the work conducted by INIFAP to preserve genetic material of European origins that has led to the establishment of a honeybee germplasm bank. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) is a population-based optimization algorithm that mimics the foraging behavior of honeybees. Here, we focus on the parameter setting that ensures the ABC algorithm stability. Therefore, this paper introduces a matrix-iterative model, taking into account the coupling within bees. Moreover, the model considered the difference between update modes, i.e., synchronous or sequential. The necessary conditions for absolute stability were derived under the quasi-deterministic assumption. We further investigated the criteria for first and second-order stochastic stability. These criteria report on the ranges for setting the uniform distributions of the ABC algorithm. Finally, some supporting simulations were carried out on CEC 2017 benchmark functions in different search space dimensions (10, 30, and 50) and on twenty real-world problems (CEC 2011). Six considered ABC variants tested the derived and state-of-the-art criteria in different update modes. Moreover, ABC algorithms were compared with some state-of-the-art metaheuristics (Particle Swarm Optimization, Gravitational Search Algorithm, and Grey Wolf Optimizer). The overall results show that stochastic stability proffers ABC competitiveness. The two update modes may alter the ABC performance only slightly or drastically, depending on the problem, with no evidence on the supremacy of one of them. [ABSTRACT FROM AUTHOR]

The bounded diameter minimum spanning tree (BD-MST) problem seeks a spanning tree (T) of minimum weight on a given connected, undirected and edge-weighted graph subject to the diameter of T does not exceed D ≥ 2 , where D is a given positive integer. The BD-MST problem is N P -hard problem and finds many real-world applications. In this paper, we propose an artificial bee colony (ABC) algorithm for the BD-MST problem. ABC algorithm is a swarm-based metaheuristic technique based on the intelligent foraging behavior of honeybees. The proposed ABC algorithm employs permutation encoding. To exploit this encoding structure, two neighborhood strategies that help ABC algorithm in faster convergence towards finding high quality solutions are applied. On a set of Euclidean and non-Euclidean benchmark instances for various diameter bounds, the proposed approach has been compared with state-of-the-art approaches. Computational results demonstrate the effectiveness of the proposed approach to the other extant approaches in the literature. [ABSTRACT FROM AUTHOR]

During recent years, deep convolutional neural networks (CNNs) have become increasingly popular in sensor-based human activity recognition (HAR), due to powerful feature extraction capacity. However, the required high computational cost has hindered their practical deployment in real-world applications. To address this issue, this paper presents a novel approach named ABCSearch to prune channels through an artificial bee colony (ABC) algorithm, which is a meta-heuristic optimization algorithm inspired by the foraging behavior of honeybees. Unlike traditional pruning strategies that prioritize channel importance, the proposed method seeks to identify the optimal number of channels at each layer, i.e., the optimal pruning structure. To lessen human interference, we transform the search problem of the optimal pruning structure into a simple optimization problem and solve it automatically through the ABC algorithm, which can significantly decrease both model size and computational cost without compromising performance. Specifically, we validate the effectiveness of ABCSearch for a standard five-layer CNN backbone architecture, which achieves 97.05%, 98.45%, 91.71%, 78.89% classification accuracies on four HAR benchmarks, i.e., UCI-HAR, WISDM, PAMAP2, UniMiB-SHAR. Our approach can yield 87.44%, 89.41%, 89.06%, 89.59% FLOPs reductions, and 89.54%, 88.70%, 88.38%, 90.45% less memory consumptions, as well as 4.4 × , 5.0 × , 5.8 × , 6.1 × practical speedups on real hardware devices, while maintaining comparable accuracy. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm is well known and widely used optimization method based on swarm intelligence, and it is inspired by the behavior of honeybees searching for a high amount of nectar from the flower. However, this algorithm has not been exploited sufficiently. This research paper proposes a novel method to analyze the exploration and exploitation of ABC. In ABC, the scout bee searches for a source of random food for exploitation. Along with random search, the scout bee is guided by a modified genetic algorithm approach to locate a food source with a high nectar value. The proposed algorithm is applied for the design of a nonlinear controller for a continuously stirred tank reactor (CSTR). The statistical analysis of the results confirms that the proposed modified hybrid artificial bee colony (HMABC) achieves consistently better performance than the traditional ABC algorithm. The results are compared with conventional ABC and nonlinear PID (NLPID) to show the superiority of the proposed algorithm. The performance of the HMABC algorithm-based controller is competitive with other state-of-the-art meta-heuristic algorithm-based controllers in the literature. [ABSTRACT FROM AUTHOR]

As a canary in a coalmine warns of dwindling breathable air, the honeybee can indicate the health of an ecosystem. Honeybees are the most important pollinators of fruit-bearing flowers, and share similar ecological niches with many other pollinators; therefore, the health of a honeybee colony can reflect the conditions of a whole ecosystem. The health of a colony may be mirrored in social signals that bees exchange during their sophisticated body movements such as the waggle dance. To observe these changes, we developed an automatic system that records and quantifies social signals under normal beekeeping conditions. Here, we describe the system and report representative cases of normal social behavior in honeybees. Our approach utilizes the fact that honeybee bodies are electrically charged by friction during flight and inside the colony, and thus they emanate characteristic electrostatic fields when they move their bodies. These signals, together with physical measurements inside and outside the colony (temperature, humidity, weight of the hive, and activity at the hive entrance) will allow quantification of normal and detrimental conditions of the whole colony. The information provided instructs how to setup the recording device, how to install it in a normal bee colony, and how to interpret its data. [ABSTRACT FROM AUTHOR]

Efficient and robust motion perception systems are important pre-requisites for achieving visually guided flights in future micro air vehicles. As a source of inspiration, the visual neural networks of flying insects such as honeybee and Drosophila provide ideal examples on which to base artificial motion perception models. In this paper, we have used this approach to develop a novel method that solves the fundamental problem of estimating angular velocity for visually guided flights. Compared with previous models, our elementary motion detector (EMD) based model uses a separate texture estimation pathway to effectively decode angular velocity, and demonstrates considerable independence from the spatial frequency and contrast of the gratings. Using the Unity development platform the model is further tested for tunnel centering and terrain following paradigms in order to reproduce the visually guided flight behaviors of honeybees. In a series of controlled trials, the virtual bee utilizes the proposed angular velocity control schemes to accurately navigate through a patterned tunnel, maintaining a suitable distance from the undulating textured terrain. The results are consistent with both neuron spike recordings and behavioral path recordings of real honeybees, thereby demonstrating the model's potential for implementation in micro air vehicles which have only visual sensors. [ABSTRACT FROM AUTHOR]

Hygienic behavior is a social defense mechanism against parasites and pathogens in honeybees. We studied the genetic basis of hygienic behavior in African-derived Apis mellifera by performing RNA sequencing on brains of individual honeybee workers observed performing hygienic behavior, in order to identify expression changes linked with this behavior. We also used the transcriptome data to search for single nucleotide polymorphism (SNP) variation in genes previously associated with the trait in other A. mellifera populations. The analysis revealed 49 differentially expressed genes (DEG), most of them upregulated during hygienic acts. One DEG, Apidaecin, also showed SNP variation across samples, providing a promising candidate gene for both expression-based and heritable variation in hygienic behavior. We also identified 27 additional SNPs in the coding regions of five candidate genes previously linked with honeybee hygienic behavior. These results provide a useful starting place to analyze the genetic basis of hygienic behavior in African-derived honeybees. [ABSTRACT FROM AUTHOR]

The unicellular spore-forming parasites Nosema apis and Nosema ceranae are considered to be one of the causes of increased honey bee mortality in recent years. These pathogens attack their honey bee hosts through their gut, causing changes in behavioral stress responses and possibly resulting in decreased honey yield and increased honey bee mortality. The present study aimed to determine the prevalence of Nosema spp. (nosemosis) in Estonia and Latvia, as well as the persistence of the disease in previously infected hives. Currently, N. ceranae is considered the most virulent species and is predominant worldwide. However, in some regions, usually with colder climates, N. apis is still prevalent. To achieve better disease control, it is important to determine the species distribution. For this purpose, we selected 30 apiaries in Estonia and 60 in Latvia that were positive for Nosema spp. in the EPILOBEE (2012-2014) study, which was 5 years prior to the present study. The results show that, while both species are present in Estonia and Latvia, N. apis is dominant in Estonia (43%), and N. ceranae is dominant in Latvia (47%). We also found that the pathogens are very persistent, since 5 years later, only 33% of infected apiaries in Estonia and 20% of infected apiaries in Latvia, we could not detect any pathogens at the time of sampling. [ABSTRACT FROM AUTHOR]

Various types of pesticides are commonly used in modern agriculture. Honey bees (Apis mellifera) are sensitive indicators of environmental contamination with these substances. Exposure of honey bees to pesticides can lead to changes in their behaviour and increase mortality, so it is important to develop alternative formulations to common ('chemical') pesticides. The recently developed preparation Siltac EC (patent no. WO 2016/061259) shows promise as an effective substitute. It is based on a physical interaction with the pest and does not contain chemicals classified as pesticides. The aim of the current study was to evaluate the toxicity of Siltac EC to adult honey bee workers. The experiments showed that both contact and oral acute toxicity were very low, and that the preparation can preliminarily be considered safe for honey bees. [ABSTRACT FROM AUTHOR]

Apis mellifera is an important provider of ecosystem services, and during flight and foraging behaviour is exposed to environmental pollutants including airborne particulate matter (PM). While exposure to insecticides, antibiotics, and herbicides may compromise bee health through alterations of the gut microbial community, no data are available on the impacts of PM on the bee microbiota. Here we tested the effects of ultrapure Titanium dioxide (TiO2) submicrometric PM (i.e., PM1, less than 1 µm in diameter) on the gut microbiota of adult bees. TiO2 PM1 is widely used as a filler and whitening agent in a range of manufactured objects, and ultrapure TiO2 PM1 is also a common food additive, even if it has been classified by the International Agency for Research on Cancer (IARC) as a possible human carcinogen in Group 2B. Due to its ubiquitous use, honey bees may be severely exposed to TiO2 ingestion through contaminated honey and pollen. Here, we demonstrated that acute and chronic oral administration of ultrapure TiO2 PM1 to adult bees alters the bee microbial community; therefore, airborne PM may represent a further risk factor for the honey bee health, promoting sublethal effects against the gut microbiota. [ABSTRACT FROM AUTHOR]

The hygienic behavior of honeybees (Apis mellífera) is a mechanism highly influenced by genetic effects that confers resistance against diseases and parasites. The objective of this study was to determine whether climatic, regional and brood quantity influences are related to hygienic behavior in beehives in Jalisco state, Mexico. Freezing pupae with liquid nitrogen in two climatic zones (mountain and valley) evaluated 142 colonies. Colonies located in the municipalities of the low zone removed significantly more frozen brood than the colonies located in the municipalities of the higher zone (p <0.001), and these differences were significantly correlated with environmental temperature (r = 0.25, p <0.01) and altitude above sea level (r = - 0.27, p <0.001). It was also found that in the warmer, lower altitude region, one third of the colonies were highly hygienic (> 80 %). This degree of cleanliness is acceptable for initiating selective breeding projects for highly hygienic bees to improve colony health, which would benefit the beekeeping industry. [ABSTRACT FROM AUTHOR]

The polyandrous mating behavior of the honeybee queen increases the genetic variability among her worker offspring and the workers of particular subfamilies tend to have a genetic predisposition for tasks preference. In this study, we intended to understand whether there is nepotism in dance communication of honeybees during natural conditions. Microsatellite DNA analyses revealed a total of fourteen and twelve subfamilies in two colonies. The subfamily composition of the dancer and the followers did not deviate from random. The majority of the subfamilies did not show kin recognition in dance-recruit communication in honeybee colonies, but some subfamilies showed significant nepotism for workers to follow their super-sister dancer. Because it seems unlikely that honeybee would change the tendency to follow dancers due to the degree of relatedness, we conclude that honeybees randomly follow a dancer in order to e benefit colony gain and development. [ABSTRACT FROM AUTHOR]

Background: Varroa destructor is considered a major cause of honeybee (Apis mellifera) colony losses worldwide. Although V. destructor mites exhibit preference behavior for certain honeybee lifecycle stages, the mechanism underlying host finding and preference remains largely unknown. Results: By using a de novo transcriptome assembly strategy, we sequenced the mature daughter V. destructor mite transcriptome during infestation of different stages of honeybees (brood cells, newly emerged bees and adult bees). A total of 132 779 unigenes were obtained with an average length of 2745 bp and N50 of 5706 bp. About 63.1% of the transcriptome could be annotated based on sequence homology to the predatory mite Metaseiulus occidentalis proteins. Expression analysis revealed that mature daughter mites had distinct transcriptome profiles after infestation of different honeybee stages, and that the majority of the differentially expressed genes (DEGs) of mite infesting adult honeybees were down‐regulated compared to that infesting the sealed brood cells. Gene ontology and KEGG pathway enrichment analyses showed that a large number of DEGs were involved in cellular process and metabolic process, suggesting that Varroa mites undergo metabolic adjustment to accommodate the cellular, molecular and/or immune response of the honeybees. Interestingly, in adult honeybees, some mite DEGs involved in neurotransmitter biosynthesis and transport were identified and their levels of expression were validated by quantitative polymerase chain reaction (qPCR). Conclusion: These results provide evidence for transcriptional reprogramming in mature daughter Varroa mites during infestation of honeybees, which may be relevant to understanding the mechanism underpinning adaptation and preference behavior of these mites for honeybees. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Honey bee queens undergo dramatic behavioral (e.g., reduced sexual receptivity), physiological (e.g., ovary activation, ovulation, and modulation of pheromone production) and transcriptional changes after they complete mating. To elucidate how queen post-mating changes are influenced by seminal fluid, the non-spermatozoa-containing component of semen, we injected queens with semen or seminal fluid alone. We assessed queen sexual receptivity (as measured by likelihood to take mating flights), ovary activation, worker retinue response (which is influenced by queen pheromone production), and transcriptional changes in queen abdominal fat body and brain tissues. Injection with either seminal fluid or semen resulted in decreased sexual receptivity, increased attractiveness of queens to workers, and altered expression of several genes that are also regulated by natural mating in queens. The post-mating and transcriptional changes of queens receiving seminal fluid were not significantly different from queens injected with semen, suggesting that components in seminal fluid, such as seminal fluid proteins, are largely responsible for stimulating post-mating changes in queens. [ABSTRACT FROM AUTHOR]

Oil peony (Paeonia spp.) is a new type of woody oil crop in China with a large cultivation area. Inadequate pollination is one of the main reasons for low seed yield. A pollination net room was built at an oil tree peony base, the numbers of honeybees (Apis mellifera L.) and ground bumblebees (Bombus terrestris L.) were artificially increased and the foraging behaviors and daily activities of the two bees on the plants were observed. Four different pollination methods (honeybee pollination, ground bumblebee pollination, natural field pollination and pollination without insects) were applied. The visit interval, single-flower visit time, number of single-flower visits, number of flowers visited per minute and number of stigma contacts were compared. Meanwhile, the effects of honeybee and bumblebee pollination on the oil yield and quality of peony seeds were compared. There were noticeable differences in daily activity between honeybees and ground bumblebees. Significant differences in the single-flower visit time, visit interval and visit frequency were also observed; honeybee and ground bumblebee pollination increased the seed yield of oil tree peony by 78.74% and 31.88%, respectively. Therefore, both honeybees and ground bumblebees are effective pollinators of oil tree peony. These results provide a theoretical basis for further utilization of bee resources for oil tree peony pollination. [ABSTRACT FROM AUTHOR]