Flow-mediated olfactory communication in honeybee swarms

Collective synchrony of mating signals modulated by ecological cues and social signals in bioluminescent sea fireflies

Individuals often employ simple rules that can emergently synchronize behaviour. Some collective behaviours are intuitively beneficial, but others like mate signalling in leks occur across taxa despite theoretical individual costs. Whether disparate instances of synchronous signalling are similarly organized is unknown, largely due to challenges observing many individuals simultaneously. Recording field collectives and ex situ playback experiments, we describe principles of synchronous bioluminescent signals produced by marine ostracods (Crustacea; Luxorina) that seem behaviorally convergent with terrestrial fireflies, and with whom they last shared a common ancestor over 500 Mya. Like synchronous fireflies, groups of signalling males use visual cues (intensity and duration of light) to decide when to signal. Individual ostracods also modulate their signal based on the distance to nearest neighbours. During peak darkness, luminescent 'waves' of synchronous displays emerge and ripple across the sea floor approximately every 60 s, but such periodicity decays within and between nights after the full moon. Our data reveal these bioluminescent aggregations are sensitive to both ecological and social light sources. Because the function of collective signals is difficult to dissect, evolutionary convergence, like in the synchronous visual displays of diverse arthropods, provides natural replicates to understand the generalities that produce emergent group behaviour.

The role of hydrodynamics in collective motions of fish schools and bioinspired underwater robots

Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of social forces, which approximate the behaviour of individual animals. But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy, which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.

Population Structure, Demographic History, and Adaptation of Giant Honeybees in China Revealed by Population Genomic Data

There have been many population-based genomic studies on human-managed honeybees (Apis mellifera and Apis cerana), but there has been a notable lack of analysis with regard to wild honeybees, particularly in relation to their evolutionary history. Nevertheless, giant honeybees have been found to occupy distinct habitats and display remarkable characteristics, which are attracting an increased amount of attention. In this study, we de novo sequenced and then assembled the draft genome sequence of the Himalayan giant honeybee, Apis laboriosa. Phylogenetic analysis based on genomic information indicated that A. laboriosa and its tropical sister species Apis dorsata diverged ∼2.61 Ma, which supports the speciation hypothesis that links A. laboriosa to geological changes throughout history. Furthermore, we re-sequenced A. laboriosa and A. dorsata samples from five and six regions, respectively, across their population ranges in China. These analyses highlighted major genetic differences for Tibetan A. laboriosa as well as the Hainan Island A. dorsata. The demographic history of most giant honeybee populations has mirrored glacial cycles. More importantly, contrary to what has occurred among human-managed honeybees, the demographic history of these two wild honeybee species indicates a rapid decline in effective population size in the recent past, reflecting their differences in evolutionary histories. Several genes were found to be subject to selection, which may help giant honeybees to adapt to specific local conditions. In summary, our study sheds light on the evolutionary and adaptational characteristics of two wild giant honeybee species, which was useful for giant honeybee conservation.

A matter of the beehive sound: Can honey bees alert the pollution out of their hives?

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.

Rheomergy : collective behaviour mediated by active flow-based recruitment

The physics of signal propagation in a collection of organisms that communicate with each other both enables and limits how active excitations at the individual level reach, recruit and lead to collective patterning. Inspired by the spatio-temporal patterns in a planar swarm of bees that use pheromones and fanning flows to recruit additional bees, we develop a theoretical framework for patterning via active flow-based recruitment. Our model generalizes the well-known Patlak–Keller–Segel model of diffusion dominated aggregation and leads to an enhanced phase space of patterns spanned by two dimensionless parameters that measure the scaled stimulus/activity and the scaled chemotactic response. Together these determine the efficacy of signal communication via fluid flow (which we dub rheomergy ) that leads to a variety of migration and aggregation patterns, consistent with observations.

Honey bees find the shortest path: a collective flow-mediated approach

Honey bees (Apis mellifera L.) are social insects that makes frequent use of volatile pheromone signals to collectively navigate unpredictable and unknown environments. Ants have been shown to effectively use pheromone trails to find the shortest path between two points, the nest and the food source. The ant pheromone trails are accomplished by depositing pheromones which are then diffused passively, creating isotropic (i.e., non-directional and axi-symmetric) signals. In this study, we report the first instance of the honey bees’ ability to solve the shortest path problem to localize the queen and aggregate around her by using a collective flow-mediated scenting strategy. In this strategy, individual bees not only emit pheromones but also fan their wings to actively direct the flow of the signals, providing colony members with directional messages to the queen’s location. We use computer vision and deep learning approaches to perform automatic and accurate image analysis. As a result, we quantify the number of bees in the short and long paths, and show that the short path is frequented by significantly more bees over time. We also reconstruct attractive surfaces using the positions and directions of scenting bees, and show that this surface is more “attractive” along the short path and around the queen as scenting bees send out directional messages and the swarm makes their way to the queen. Overall, we show that honey bees can effectively use the collective scenting behavior to overcome local and volatile pheromone communication and find the shortest path to the queen.

A Minimally Invasive Approach Towards “Ecosystem Hacking” With Honeybees

Honey bees live in colonies of thousands of individuals, that not only need to collaborate with each other but also to interact intensively with their ecosystem. A small group of robots operating in a honey bee colony and interacting with the queen bee, a central colony element, has the potential to change the collective behavior of the entire colony and thus also improve its interaction with the surrounding ecosystem. Such a system can be used to study and understand many elements of bee behavior within hives that have not been adequately researched. We discuss here the applicability of this technology for ecosystem protection: A novel paradigm of a minimally invasive form of conservation through “Ecosystem Hacking”. We discuss the necessary requirements for such technology and show experimental data on the dynamics of the natural queen’s court, initial designs of biomimetic robotic surrogates of court bees, and a multi-agent model of the queen bee court system. Our model is intended to serve as an AI-enhanceable coordination software for future robotic court bee surrogates and as a hardware controller for generating nature-like behavior patterns for such a robotic ensemble. It is the first step towards a team of robots working in a bio-compatible way to study honey bees and to increase their pollination performance, thus achieving a stabilizing effect at the ecosystem level.

Morphology of Nasonov and Tergal Glands in Apis mellifera Rebels

Social insect societies are characterized by a high level of organization. This is made possible through a remarkably complex array of pheromonal signals produced by all members of the colony. The queen's pheromones signal the presence of a fertile female and induce daughter workers to remain sterile. However, the lack of the queen mandibular pheromone leads to the emergence of rebels, i.e., workers with increased reproductive potential. We suggested that the rebels would have developed tergal glands and reduced Nasonov glands, much like the queen but contrary to normal workers. Our guess turned out to be correct and may suggest that the rebels are more queen-like than previously thought. The tergal gland cells found in the rebels were numerous but they did not adhere as closely to one another as they did in queens. In the rebels, the number of Nasonov gland cells was very limited (from 38 to 53) and there were fat body trophocytes between the glandular cells. The diameters of the Nasonov gland cell nuclei were smaller in the rebels than in the normal workers. These results are important for understanding the formation of the different castes of Apis mellifera females, as well as the division of labor in social insect societies.