Analysis of Honeybee Drone Activity during the Mating Season in Northwestern Argentina

Non-Intrusive System for Honeybee Recognition Based on Audio Signals and Maximum Likelihood Classification by Autoencoder

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.

Ecological comparison of native (Apis mellifera mellifera) and hybrid (Buckfast) honeybee drones in southwestern Sweden indicates local adaptation

Honeybee drones' only known task is to mate with a virgin queen. Apart from their mating behaviour, their ecology has been little studied, especially in comparison to honeybee females. Previous knowledge is primarily based on short-term direct observations at single experimental hives, rarely, if ever, addressing the effect of drones' genetic origin. Here, Radio Frequency Identification Technology was utilised to gather drone and worker bee lifetime data of Apis mellifera mellifera and Apis mellifera x (hybrid Buckfast) colonies over one mating season (spring and summer) with the ultimate goal to investigate differences at subspecies level. This technique enabled continuous monitoring of tagged bees at the hive entrance and recording of individuals' movement directions. The results confirmed that spring-born drones survive longer than summer-born drones and that they generally live longer than worker bees. Drones' peak activity occurred in the afternoon while worker bees showed more even activity levels throughout the day. Earlier orientation flights than usually reported for drones were observed. In summer, mating flights were practiced before reaching sexual maturity (at 12 days of age). Differences were found between Apis m. mellifera and Buckfast drones, where Apis m. mellifera showed later drone production in spring, but significantly earlier first activities outside the hive in summer and a later peak in diurnal activity. Additionally, Apis m. mellifera flew more in higher light intensities and windy conditions and performed significantly longer flights than Buckfast drones. The observed differences in drone ecology indicate the existence of a local adaptation of the native subspecies Apis m. mellifera to environmental conditions in southwestern Sweden.

Ontogeny of immunity and natural viral infection in Apis mellifera drones and workers

The most common viral diseases affecting honey bees (Apis mellifera) in Israel include deformed wing viruses (DWV-A and DWV-B) and acute paralysis viruses (ABPV and IAPV). These viruses are transmitted within and between colonies, both horizontally and vertically. All members of the colony contribute to this transmission, on the other hand individual and social immunity, particularly hygienic behaviour, may affect the outcome of the process. In this study, we evaluated the ontogeny of natural infections of DWV-A, DWV-B, ABPV and IAPV, their prevalence and loads, in workers and drones from high (H) and low (L) hygienic colonies. In parallel, we evaluated the expression of two immune genes: peptidoglycan recognition protein S2(PGRP-S2) and hymenoptaecin. The prevalence of DWV-B and IAPV increased with age and was higher in workers than in drones. ABPV was not detected in drones. The expression of both immune genes was significantly affected by age and sex. Drones from H colonies had higher expression of these genes. The increased expression of immune genes with drones' age, particularly in hygienic colonies, suggest additional value of honey bee breeding for hygienic behaviour for sustainable beekeeping.

Where Is the Honey Bee Queen Flying? The Original Case of a Foraging Queen

Simple Summary

Reproduction is the only task normally attributed to the queen due to its specific morpho-functional characteristics, while foraging activities are exclusively carried out by workers bees in the honey bee colony. For example, the queen’s proboscis is shorter than that of workers and, therefore, less suitable for exploring the inside of flowers to collect nectar. Olfactory and visual detection is also less developed in the queen than in workers, and it is well known how important these stimuli are in order to search for appropriate flowers and find the food source within the flower. In the countryside of northern Sardinia, a honey bee queen (Apis mellifera L.) was detected for the first time while foraging on a flower (a borage flower), most likely during an orientation flight before mating. The open, shallow corolla, and the excellent nectar secretion of the borage flower might have facilitated the queen bee activity. This new queen behaviour was based on the morphological traits of the specimen collected and photos taken in that moment. The observed foraging activity opens new and yet unexplored perspectives on the behaviour of queen bees outside the nest (or the hive), which could occasionally include tasks usually attributed only to workers.

Abstract

During a bee fauna survey in the countryside of northern Sardinia, a honey bee queen (Apis mellifera L.) was detected while foraging on a borage (Borago officinalis L.) flower in Uri, Province of Sassari, Italy, most likely during an orientation flight before mating. Morphological details, detectable from photos with the naked eye and stereomicroscopic observations, confirmed that the honey bee queen was sucking nectar from a flower. The enormous development of the abdomen, lack of pollen-collecting structures in the legs and other characteristics such as the typical distally bilobed shape of the mandibles, with long hairs on their outer surface, proved the structural differences between the queen specimen and the other castes of bees. The queen’s proboscis, which is shorter compared to the workers, may have been counterbalanced by the shape and nectar production of the borage flower. This new observation proves that the queen can feed herself under natural conditions, likely to obtain the energy required for flying. Although we cannot exclude disturbing factors that could explain this foraging behaviour of a queen observed for the first time, this note opens a new scenario and discusses this new finding in the context of the available literature on the queen’s behaviour and questions to be answered.

RFID Technology Serving Honey Bee Research: A Comprehensive Description of a 32-Antenna System to Study Honey Bee and Queen Behavior

The fields of electronics and information technology have witnessed rapid development during the last decades, providing significant technical support to the field of biological sciences. Radio-Frequency Identification (RFID) technology has been used to automate the monitoring of animal location and behaviors in a wide range of vertebrate and invertebrate species, including social insects such as ants and honey bees (Apis mellifera L.). This technology relies on electromagnetic fields to identify and track transponders attached to objects automatically. Implementing new technologies to serve research purposes could be time consuming and require technical expertise from entomologists and researchers. Herein, we present a detailed description on how to harness RFID technology to serve honey bee research effectively. We describe how to build and operate a 32-antenna RFID system used to monitor various honey bee behaviors such as foraging, robbing, and queen and drone mating, which can be used in other social insects as well. Preliminary data related to queen nuptial flights were obtained using this unit and presented in this study. Virgin queens labeled with ≈5 mg transponders performed multiple (one to four) nuptial/orientation flights a day (9 a.m. to 5 p.m.) ranging from 8 to 145 s each. Contrary to virgin queens, no hive exit was recorded for mated queens. At full capacity, this unit can monitor up to 32 honey bee colonies concurrently and is self-sustained by a solar panel to work in remote areas. All materials, hardware, and software needed to build and operate this unit are detailed in this study, offering researchers and beekeepers a practical solution and a comprehensive source of information enabling the implementation of RFID technology in their research perspective.