Social networks predict the life and death of honey bees

Disruption of collective behaviour correlates with reduced interaction efficiency

Group-living organisms commonly engage in collective behaviour to respond to an ever-changing environment. As animals face environmental change, establishing the mechanisms of information used to collectively behave is critical. Western honeybees (Apis mellifera) are highly social insects that tightly coordinate many individuals to ensure optimum colony function. We used fanning, a collective thermoregulatory behaviour that depends on both social and thermal contexts, as a case study for collective behaviour. To identify potential mechanisms behind the coordination of fanning, we used oxytetracycline, an antibiotic used in apiculture and known environmental pollutant that impairs bee physiology and behaviour. Specifically, we hypothesized that interactions drive the fanning response in honeybees and predicted that oxytetracycline would disrupt social interactions which will lead to a reduced fanning response. We found that longer exposure to antibiotics decreases fanning. Using automated tracking, we show that antibiotic treatment reduces the number of interactions, impeding the social dynamics within these small groups. Our results contribute strong evidence that interactions between individuals may drive the collective fanning response in honeybees. This work emphasizes the importance of understanding the social mechanisms that underlie collective animal coordination and how the effects of pollutants on an individual can scale to affect populations.

Information sharing within a social network is key to behavioral flexibility-Lessons from mice tested under seminaturalistic conditions

Being part of a social structure offers chances for social learning vital for survival and reproduction. Nevertheless, studying the neural mechanisms of social learning under laboratory conditions remains challenging. To investigate the impact of socially transmitted information about rewards on individual behavior, we used Eco-HAB, an automated system monitoring the voluntary behavior of group-housed mice under seminaturalistic conditions. In these settings, male mice spontaneously form social networks, with individuals occupying diverse positions. We show that a rewarded group member's scent affects the ability of conspecifics to search for rewards in familiar and novel environments. The scent's impact depends on the animal's social position. Furthermore, disruption of neuronal plasticity in the prelimbic cortex (PL) disrupts the social networks and animals' interest in social information related to rewards; only the latter is blocked by the acute PL inhibition. This experimental design represents a cutting-edge approach to studying the brain mechanisms of social learning.

Pheromone relay networks in the honeybee: messenger workers distribute the queen's fertility signal throughout the hive

BACKGROUND

The harmonious operation of many insect societies depends upon colony-wide dissemination of a non-volatile pheromone produced by a single queen, which informs workers of her presence. This represents a major challenge in large colonies. Honeybee colonies, which can exceed 60,000 bees, are believed to solve this challenge using 'messenger' workers that actively relay the queen pheromone throughout the hive. However, little is known about the structure and effectiveness of the underlying relay network or the biology of messaging.

RESULTS

Here, we combine automated tracking with modelling to address these outstanding questions. We find that both queen movement and worker messaging play fundamental roles in queen pheromone dissemination. Fine-grained analyses of worker behaviour confirmed the existence of active messaging, as physical contacts with the queen caused workers to move faster and straighter, thereby accelerating pheromone transmission. Finally, we show that messaging follows a stereotypical developmental trajectory, resulting in an age-dependent hierarchical relay network, with the most intense messaging observed between three and five days of age, when workers undergo a suite of physiological changes associated with queen rearing.

CONCLUSIONS

These results suggest that the individuals that contribute most to advertising the presence of the queen are also the ones that control queen production.

Shift in distribution of division of labour in chronically stressed honeybee colonies after perturbation

Division of labour (DOL) in eusocial insects plays an important role in colony fitness. Honeybees face a variety of stressors that compromise the homeostasis of the colony and reduce survival and reproduction. Considering the significance of DOL in colony homeostasis, it is important to understand whether and how DOL may be altered as a result of chronic stress. Therefore, we tested whether honeybee colonies shift DOL in response to high infestation with the parasitic mite Varroa destructor. For this, we monitored chronically stressed and presumably low-stress colonies from April till December 2022. During the experiment, we applied a cold shock to test whether a perturbation resulted in a larger alteration in DOL in chronically stressed colonies. We found that after cold shock, there was a lower proportion of nurses in the chronically stressed colonies. For foragers, we found higher activity post-cold shock in chronically stressed colonies, but no difference between treatments in nectar inflow, suggesting less efficient foragers. Furthermore, we found that there was an accelerated task switch in chronically stressed colonies after the cold shock. The large changes after the perturbation may indicate inefficient task allocation due to chronic stress. Our study contributes to the understanding of social resilience and chronic stress responses in eusocial animals.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

Foraging distance distributions reveal how honeybee waggle dance recruitment varies with landscape

Honeybee (Apis mellifera) colonies use a unique collective foraging system, the waggle dance, to communicate and process the location of resources. Here, we present a means to quantify the effect of recruitment on colony forager allocation across the landscape by simply observing the waggle dance on the dancefloor. We show first, through a theoretical model, that recruitment leaves a characteristic imprint on the distance distribution of foraging sites that a colony visits, which varies according to the proportion of trips driven by individual search. Next, we fit this model to the real-world empirical distance distribution of forage sites visited by 20 honeybee colonies in urban and rural landscapes across South East England, obtained via dance decoding. We show that there is considerable variation in the use of dancing information in colony foraging, particularly in agri-rural landscapes. In our dataset, reliance on dancing increases as arable land gives way to built-up areas, suggesting that dancing may have the greatest impact on colony foraging in the complex and heterogeneous landscapes of forage-rich urban areas. Our model provides a tool to assess the relevance of this extraordinary behaviour across modern anthropogenic landscape types.

Ant social network structure is highly conserved across species

The ecological success of social insects makes their colony organization fascinating to scientists studying collective systems. In recent years, the combination of automated behavioural tracking and social network analysis has deepened our understanding of many aspects of colony organization. However, because studies have typically worked with single species, we know little about interspecific variation in network structure. Here, we conduct a comparative network analysis across five ant species from five subfamilies, separated by more than 100 Myr of evolution. We find that social network structure is highly conserved across subfamilies. All species studied form modular networks, with two social communities, a similar distribution of individuals between the two communities, and equivalent mapping of task performance onto the communities. Against this backdrop of organizational similarity, queens of the different species occupied qualitatively distinct network positions. The deep conservation of the two community structure implies that the most fundamental behavioural division of labour in social insects is between workers that stay in the nest to rear brood, and those that leave the nest to forage. This division has parallels across the animal kingdom in systems of biparental care and probably represents the most readily evolvable form of behavioural division of labour.

Individualized social niches in animals: Theoretical clarifications and processes of niche change

What are social niches, and how do they arise and change? Our first goal in the present article is to clarify the concept of an individualized social niche and to distinguish it from related concepts, such as a social environment and a social role. We argue that focal individuals are integral parts of individualized social niches and that social interactions with conspecifics are further core elements of social niches. Our second goal in the present article is to characterize three types of processes-social niche construction, conformance, and choice (social NC3 processes)-that explain how individualized social niches originate and change. Our approach brings together studies of behavior, ecology, and evolution and integrates social niches into the broader concept of an individualized ecological niche. We show how clarifying the concept of a social niche and recognizing the differences between the three social NC3 processes enhance and stimulate empirical research.

How honeybees respond to heat stress from the individual to colony level

A honey bee colony functions as an integrated collective, with individuals coordinating their behaviour to adapt and respond to unexpected disturbances. Nest homeostasis is critical for colony function; when ambient temperatures increase, individuals switch to thermoregulatory roles to cool the nest, such as fanning and water collection. While prior work has focused on bees engaged in specific behaviours, less is known about how responses are coordinated at the colony level, and how previous tasks predict behavioural changes during a heat stress. Using BeesBook automated tracking, we follow thousands of individuals during an experimentally induced heat stress, and analyse their behavioural changes from the individual to colony level. We show that heat stress causes an overall increase in activity levels and a spatial reorganization of bees away from the brood area. Using a generalized framework to analyse individual behaviour, we find that individuals differ in their response to heat stress, which depends on their prior behaviour and correlates with age. Examining the correlation of behavioural metrics over time suggests that heat stress perturbation does not have a long-lasting effect on an individual's future behaviour. These results demonstrate how thousands of individuals within a colony change their behaviour to achieve a coordinated response to an environmental disturbance.

Identifying a developmental transition in honey bees using gene expression data

In many organisms, interactions among genes lead to multiple functional states, and changes to interactions can lead to transitions into new states. These transitions can be related to bifurcations (or critical points) in dynamical systems theory. Characterizing these collective transitions is a major challenge for systems biology. Here, we develop a statistical method for identifying bistability near a continuous transition directly from high-dimensional gene expression data. We apply the method to data from honey bees, where a known developmental transition occurs between bees performing tasks in the nest and leaving the nest to forage. Our method, which makes use of the expected shape of the distribution of gene expression levels near a transition, successfully identifies the emergence of bistability and links it to genes that are known to be involved in the behavioral transition. This proof of concept demonstrates that going beyond correlative analysis to infer the shape of gene expression distributions might be used more generally to identify collective transitions from gene expression data.

Machine learning reveals the waggle drift's role in the honey bee dance communication system

The honey bee waggle dance is one of the most prominent examples of abstract communication among animals: successful foragers convey new resource locations to interested followers via characteristic "dance" movements in the nest, where dances advertise different locations on different overlapping subregions of the "dance floor." To this day, this spatial separation has not been described in detail, and it remains unknown how it affects the dance communication. Here, we evaluate long-term recordings of Apis mellifera foraging at natural and artificial food sites. Using machine learning, we detect and decode waggle dances, and we individually identify and track dancers and dance followers in the hive and at artificial feeders. We record more than a hundred thousand waggle phases, and thousands of dances and dance-following interactions to quantitatively describe the spatial separation of dances on the dance floor. We find that the separation of dancers increases throughout a dance and present a motion model based on a positional drift of the dancer between subsequent waggle phases that fits our observations. We show that this separation affects follower bees as well and results in them more likely following subsequent dances to similar food source locations, constituting a positive feedback loop. Our work provides evidence that the positional drift between subsequent waggle phases modulates the information that is available to dance followers, leading to an emergent optimization of the waggle dance communication system.

Mechanisms and adaptations that shape division of labour in stingless bees

Stingless bees are a diverse and ecologically important group of pollinators in the tropics. Division of labour allows bee colonies to meet the various demands of their social life, but has been studied in only ∼3% of all described stingless bee species. The available data suggest that division of labour shows both parallels and striking differences compared with other social bees. Worker age is a reliable predictor of worker behaviour in many species, while morphological variation in body size or differences in brain structure are important for specific worker tasks in some species. Stingless bees provide opportunities to confirm general patterns of division of labour, but they also offer prospects to discover and study novel mechanisms underlying the different lifestyles found in eusocial bees.

Colony size buffers interactions between neonicotinoid exposure and cold stress in bumblebees

Social bees are critical for supporting biodiversity, ecosystem function and crop yields globally. Colony size is a key ecological trait predicted to drive sensitivity to environmental stressors and may be especially important for species with annual cycles of sociality, such as bumblebees. However, there is limited empirical evidence assessing the effect of colony size on sensitivity to environmental stressors or the mechanisms underlying these effects. Here, we examine the relationship between colony size and sensitivity to environmental stressors in bumblebees. We exposed colonies at different developmental stages briefly (2 days) to a common neonicotinoid (imidacloprid) and cold stress, while quantifying behaviour of individuals. Combined imidacloprid and cold exposure had stronger effects on both thermoregulatory behaviour and long-term colony growth in small colonies. We find that imidacloprid's effects on behaviour are mediated by body temperature and spatial location within the nest, suggesting that social thermoregulation provides a buffering effect in large colonies. Finally, we demonstrate qualitatively similar effects in size-manipulated microcolonies, suggesting that group size per se, rather than colony age, drives these patterns. Our results provide evidence that colony size is critical in driving sensitivity to stressors and may help elucidate mechanisms underlying the complex and context-specific impacts of pesticide exposure.

Spontaneous and information-induced bursting activities in honeybee hives

Social entrainment is important for functioning of beehive organization. By analyzing a dataset of approximately 1000 honeybees (Apis mellifera) tracked in 5 trials, we discovered that honeybees exhibit synchronized activity (bursting behavior) in their locomotion. These bursts occurred spontaneously, potentially as a result of intrinsic bee interactions. The empirical data and simulations demonstrate that physical contact is one of the mechanisms for these bursts. We found that a subset of honeybees within a hive which become active before the peak of each burst, and we refer to these bees as "pioneer bees." Pioneer bees are not selected randomly, but rather, are linked to foraging behavior and waggle dancing, which may help spread external information in the hive. By using transfer entropy, we found that information flows from pioneer bees to non-pioneer bees, which suggest that the bursting behavior is caused by foraging behavior and spreading the information through the hive and promoting integrated group behavior among individuals.

Biomonitoring of polycyclic aromatic hydrocarbons, heavy metals, and plasticizers residues: role of bees and honey as bioindicators of environmental contamination

Polycyclic aromatic hydrocarbons (PAHs), heavy metals, and plasticizer residues are continuously released into the environment. The use of living organisms, such as Apis mellifera L. and honey, is advantageous as bioindicator of the environmental health status, instead of traditional monitoring methods, showing the ability to record spatial and temporal pollutant variations. The PAHs and heavy metal presence were determined in two sampling years (2017 and 2018) in five different locations in the Molise region (Italy), characterized by different pollution levels. During 2017, most PAHs in all samples were lower than limit of detection (LOD), while in 2018, their mean concentration in bee and honey samples was of 3 μg kg^-1 and 35 μg kg^-1, respectively. For heavy metals, lower values were detected in 2017 (Be, Cd, and V below LOD), while in 2018, the mean concentrations were higher, 138 μg kg^-1 and 69 μg kg^-1, in bees and honey, respectively. Honey has been used as indicator of the presence of phthalate esters and bisphenol A in the environment. The satisfactory results confirmed that both bees and honey are an important tool for environmental monitoring. The chemometric analysis highlighted the differences in terms of pollutant concentration and variability in the different areas, validating the suitability of these matrices as bioindicators.

Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome

Chromosomes in the eukaryotic nucleus are highly compacted. However, for many functional processes, including transcription initiation, the 3D pair-wise motion of distal chromosomal elements, such as enhancers and promoters, is essential and necessitates dynamic fluidity. Therefore, the interplay of chromosome organization and dynamics is crucial for gene regulation. Here, we use a live imaging assay to simultaneously measure the positions of pairs of enhancers and promoters and their transcriptional output in the developing fly embryo while systematically varying the genomic separation between these two DNA loci. Our analysis reveals a combination of a compact globular organization and fast subdiffusive dynamics. These combined features cause an anomalous scaling of polymer relaxation times with genomic separation and lead to long-ranged correlations compared to existing polymer models. This scaling implies that encounter times of DNA loci are much less dependent on genomic separation than predicted by existing polymer models, with potentially significant consequences for eukaryotic gene expression.

Automated monitoring of honey bees with barcodes and artificial intelligence reveals two distinct social networks from a single affiliative behavior

Barcode-based tracking of individuals is revolutionizing animal behavior studies, but further progress hinges on whether in addition to determining an individual's location, specific behaviors can be identified and monitored. We achieve this goal using information from the barcodes to identify tightly bounded image regions that potentially show the behavior of interest. These image regions are then analyzed with convolutional neural networks to verify that the behavior occurred. When applied to a challenging test case, detecting social liquid transfer (trophallaxis) in the honey bee hive, this approach yielded a 67% higher sensitivity and an 11% lower error rate than the best detector for honey bee trophallaxis so far. We were furthermore able to automatically detect whether a bee donates or receives liquid, which previously required manual observations. By applying our trophallaxis detector to recordings from three honey bee colonies and performing simulations, we discovered that liquid exchanges among bees generate two distinct social networks with different transmission capabilities. Finally, we demonstrate that our approach generalizes to detecting other specific behaviors. We envision that its broad application will enable automatic, high-resolution behavioral studies that address a broad range of previously intractable questions in evolutionary biology, ethology, neuroscience, and molecular biology.

PolyAMiner-Bulk: A Machine Learning Based Bioinformatics Algorithm to Infer and Decode Alternative Polyadenylation Dynamics from bulk RNA-seq data

More than half of human genes exercise alternative polyadenylation (APA) and generate mRNA transcripts with varying 3' untranslated regions (UTR). However, current computational approaches for identifying cleavage and polyadenylation sites (C/PASs) and quantifying 3'UTR length changes from bulk RNA-seq data fail to unravel tissue- and disease-specific APA dynamics. Here, we developed a next-generation bioinformatics algorithm and application, PolyAMiner-Bulk, that utilizes an attention-based machine learning architecture and an improved vector projection-based engine to infer differential APA dynamics accurately. When applied to earlier studies, PolyAMiner-Bulk accurately identified more than twice the number of APA changes in an RBM17 knockdown bulk RNA-seq dataset compared to current generation tools. Moreover, on a separate dataset, PolyAMiner-Bulk revealed novel APA dynamics and pathways in scleroderma pathology and identified differential APA in a gene that was identified as being involved in scleroderma pathogenesis in an independent study. Lastly, we used PolyAMiner-Bulk to analyze the RNA-seq data of post-mortem prefrontal cortexes from the ROSMAP data consortium and unraveled novel APA dynamics in Alzheimer's Disease. Our method, PolyAMiner-Bulk, creates a paradigm for future alternative polyadenylation analysis from bulk RNA-seq data.

Impact of cross-ancestry genetic architecture on GWAS in admixed populations

Genome-wide association studies (GWAS) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWAS in admixed populations, such as the need to correctly adjust for population stratification to balance type I error with statistical power. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes we find that modeling HetLanc in its absence reduces statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs HetLanc is not large enough for GWAS to benefit from modeling heterogeneity.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6 800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

Emergence and maintenance of stable coexistence during a long-term multicellular evolution experiment

The evolution of multicellular life spurred evolutionary radiations, fundamentally changing many of Earth’s ecosystems. Yet little is known about how early steps in the evolution of multicellularity transform eco-evolutionary dynamics, e.g., via niche expansion processes that may facilitate coexistence. Using long-term experimental evolution in the snowflake yeast model system, we show that the evolution of multicellularity drove niche partitioning and the adaptive divergence of two distinct, specialized lineages from a single multicellular ancestor. Over 715 daily transfers, snowflake yeast were subject to selection for rapid growth in rich media, followed by selection favoring larger group size. Both small and large cluster-forming lineages evolved from a monomorphic ancestor, coexisting for over ~4,300 generations. These small and large sized snowflake yeast lineages specialized on divergent aspects of a trade-off between growth rate and survival, mirroring predictions from ecological theory. Through modeling and experimentation, we demonstrate that coexistence is maintained by a trade-off between organismal size and competitiveness for dissolved oxygen. Taken together, this work shows how the evolution of a new level of biological individuality can rapidly drive adaptive diversification and the expansion of a nascent multicellular niche, one of the most historically-impactful emergent properties of this evolutionary transition.

Promiscuous feeding on multiple adult honey bee hosts amplifies the vectorial capacity of Varroa destructor

Varroa destructor is a cosmopolitan pest and leading cause of colony loss of the European honey bee. Historically described as a competent vector of honey bee viruses, this arthropod vector is the cause of a global pandemic of Deformed wing virus, now endemic in honeybee populations in all Varroa-infested regions. Our work shows that viral spread is driven by Varroa actively switching from one adult bee to another as they feed. Assays using fluorescent microspheres were used to indicate the movement of fluids in both directions between host and vector when Varroa feed. Therefore, Varroa could be in either an infectious or naïve state dependent upon the disease status of their host. We tested this and confirmed that the relative risk of a Varroa feeding depended on their previous host's infectiousness. Varroa exhibit remarkable heterogeneity in their host-switching behavior, with some Varroa infrequently switching while others switch at least daily. As a result, relatively few of the most active Varroa parasitize the majority of bees. This multiple-feeding behavior has analogs in vectorial capacity models of other systems, where promiscuous feeding by individual vectors is a leading driver of vectorial capacity. We propose that the honeybee-Varroa relationship offers a unique opportunity to apply principles of vectorial capacity to a social organism, as virus transmission is both vectored and occurs through multiple host-to-host routes common to a crowded society.

Both age and social environment shape the phenotype of ant workers

Position within the social group has consequences on individual lifespans in diverse taxa. This is especially obvious in eusocial insects, where workers differ in both the tasks they perform and their aging rates. However, in eusocial wasps, bees and ants, the performed task usually depends strongly on age. As such, untangling the effects of social role and age on worker physiology is a key step towards understanding the coevolution of sociality and aging. We performed an experimental protocol that allowed a separate analysis of these two factors using four groups of black garden ant (Lasius niger) workers: young foragers, old foragers, young nest workers, and old nest workers. We highlighted age-related differences in the proteome and metabolome of workers that were primarily related to worker subcaste and only secondarily to age. The relative abundance of proteins and metabolites suggests an improved xenobiotic detoxification, and a fuel metabolism based more on lipid use than carbohydrate use in young ants, regardless of their social role. Regardless of age, proteins related to the digestive function were more abundant in nest workers than in foragers. Old foragers were mostly characterized by weak abundances of molecules with an antibiotic activity or involved in chemical communication. Finally, our results suggest that even in tiny insects, extended lifespan may require to mitigate cancer risks. This is consistent with results found in eusocial rodents and thus opens up the discussion of shared mechanisms among distant taxa and the influence of sociality on life history traits such as longevity.

Sociability in a non-captive macaque population is associated with beneficial gut bacteria

The relationship between social behaviour and the microbiome is known to be reciprocal. Research in wild animal populations, particularly in primate social groups, has revealed the role that social interactions play in microbial transmission, whilst studies in laboratory animals have demonstrated that the gut microbiome can affect multiple aspects of behaviour, including social behaviour. Here we explore behavioural variation in a non-captive animal population with respect to the abundance of specific bacterial genera. Social behaviour based on grooming interactions is assessed in a population of rhesus macaques (Macaca mulatta), and combined with gut microbiome data. We focus our analyses on microbiome genera previously linked to sociability and autistic behaviours in rodents and humans. We show in this macaque population that some of these genera are also related to an individual's propensity to engage in social interactions. Interestingly, we find that several of the genera positively related to sociability, such as Faecalibacterium, are well known for their beneficial effects on health and their anti-inflammatory properties. In contrast, the genus Streptococcus, which includes pathogenic species, is more abundant in less sociable macaques. Our results indicate that microorganisms whose abundance varies with individual social behaviour also have functional links to host immune status. Overall, these findings highlight the connections between social behaviour, microbiome composition, and health in an animal population.

A Data-Driven Simulation of the Trophallactic Network and Intranidal Food Flow Dissemination in Ants

Food sharing can occur in both social and non-social species, but it is crucial in eusocial species, in which only some group members collect food. This food collection and the intranidal (i.e., inside the nest) food distribution through trophallactic (i.e., mouth-to-mouth) exchanges are fundamental in eusocial insects. However, the behavioural rules underlying the regulation and the dynamics of food intake and the resulting networks of exchange are poorly understood. In this study, we provide new insights into the behavioural rules underlying the structure of trophallactic networks and food dissemination dynamics within the colony. We build a simple data-driven model that implements interindividual variability and the division of labour to investigate the processes of food accumulation/dissemination inside the nest, both at the individual and collective levels. We also test the alternative hypotheses (no variability and no division of labour). The division of labour, combined with inter-individual variability, leads to predictions of the food dynamics and exchange networks that run, contrary to the other models. Our results suggest a link between the interindividual heterogeneity of the trophallactic behaviours, the food flow dynamics and the network of trophallactic events. Our results show that a slight level of heterogeneity in the number of trophallactic events is enough to generate the properties of the experimental networks and seems to be crucial for the creation of efficient trophallactic networks. Despite the relative simplicity of the model rules, efficient trophallactic networks may emerge as the networks observed in ants, leading to a better understanding of the evolution of self-organisation in such societies.

Effects of shady environments on fish collective behavior

Despite significant efforts devoted to understanding the underlying complexity and emergence of collective movement in animal groups, the role of different external settings on this type of movement remains largely unexplored. Here, by combining time series analysis and complex network tools, we present an extensive investigation of the effects of shady environments on the behavior of a fish species (Silver Carp Hypophthalmichthys molitrix) within earthen ponds. We find that shade encourages fish residence during daylight hours, but the degree of preference for shade varies substantially among trials and ponds. Silver Carp are much slower and exhibit lower persistence in their speeds when under shade than out of it during daytime and nighttime, with fish displaying the highest persistence degree and speeds at night. Furthermore, our research shows that shade affects fish schooling behavior by reducing their polarization, number of interactions among individuals, and the stability among local neighbors; however, fish keep a higher local degree of order when under shade compared to nighttime positions.

Behaviour Real-Time Spatial Tracking Identification (BeRSTID) used for Cat Behaviour Monitoring in an Animal Shelter

Efficiently tracking animal behaviour in an animal shelter has direct lifesaving applications. Individualized care and early recognition of distress in cats are often missed. However, monitoring behaviour is a challenge as time and financial resources are often limited, and the size and needs of animal populations within shelters are commonly in flux. Our research required a method of behavioural observation that was simple, accessible, used limited human and computer resources and allowed for real-time feedback. Here, we present BeRSTID, an open-source behaviour real-time spatial tracking identification system demonstrated on six cats in an animal shelter using unique 2D fiducial markers. The markers were attached to custom veterinary paper identification collars for feedback on individual animal behaviour over time. Our findings show that BeRSTID correlated closely to human-coded data in both real-time and post-event processing modes of eating and drinking behaviours of cats in naturalistic shelter environments. By building upon a lateral concept of marker tracking for direct applied use in a new context, we present a low-barrier user-friendly solution using common technologies that can track animals for research and, with further development, may help improve welfare in animal care facilities such as shelters. Extensions of BeRSTID may be generalized to track unique subjects in varied environments for multiple use cases.

Behavioral variation across the days and lives of honey bees

In honey bee colonies, workers generally change tasks with age (from brood care, to nest work, to foraging). While these trends are well established, our understanding of how individuals distribute tasks during a day, and how individuals differ in their lifetime behavioral trajectories, is limited. Here, we use automated tracking to obtain long-term data on 4,100+ bees tracked continuously at 3 Hz, across an entire summer, and use behavioral metrics to compare behavior at different timescales. Considering single days, we describe how bees differ in space use, detection, and movement. Analyzing the behavior exhibited across their entire lives, we find consistent inter-individual differences in the movement characteristics of individuals. Bees also differ in how quickly they transition through behavioral space to ultimately become foragers, with fast-transitioning bees living the shortest lives. Our analysis framework provides a quantitative approach to describe individual behavioral variation within a colony from single days to entire lifetimes.

Effect of interaction network structure in a response threshold model

Task allocation is a characteristic feature of social insects. This phenomenon is self-organized by workers in a colony without central instructions. Workers perform the necessary tasks while communicating and determining their colony’s local situations or outside environments. They can also allocate the workload of the task to finish quickly. To explain the self-organized phenomenon, we develop a theoretical model that includes the local interaction mechanism among the workers. We extend the fixed response threshold model by introducing the interaction network among workers. The new model is called the interaction network response threshold model. This model analyzes the effects of the interaction network structure on the workload allocation mechanism using the Gini coefficient. We find that the network structure affects the information diffusion process, and the network features affect workload allocation. Our results indicate that communication among the workers in a colony is an essential system for workload allocation.

From dyads to collectives: a review of honeybee signalling

The societies of honeybees (Apis spp.) are microcosms of divided labour where the fitness interests of individuals are so closely aligned that, in some contexts, the colony behaves as an entity in itself. Self-organization at this extraordinary level requires sophisticated communication networks, so it is not surprising that the celebrated waggle dance, by which bees share information about locations outside the hive, evolved here. Yet bees within the colony respond to several other lesser-known signalling systems, including the tremble dance, the stop signal and the shaking signal, whose roles in coordinating worker behaviour are not yet fully understood. Here, we firstly bring together the large but disparate historical body of work that has investigated the “meaning” of such signals for individual bees, before going on to discuss how network-based approaches can show how such signals function as a complex system to control the collective foraging effort of these remarkable social insect societies.

Frontiers in quantifying wildlife behavioural responses to chemical pollution

Animal behaviour is remarkably sensitive to disruption by chemical pollution, with widespread implications for ecological and evolutionary processes in contaminated wildlife populations. However, conventional approaches applied to study the impacts of chemical pollutants on wildlife behaviour seldom address the complexity of natural environments in which contamination occurs. The aim of this review is to guide the rapidly developing field of behavioural ecotoxicology towards increased environmental realism, ecological complexity, and mechanistic understanding. We identify research areas in ecology that to date have been largely overlooked within behavioural ecotoxicology but which promise to yield valuable insights, including within- and among-individual variation, social networks and collective behaviour, and multi-stressor interactions. Further, we feature methodological and technological innovations that enable the collection of data on pollutant-induced behavioural changes at an unprecedented resolution and scale in the laboratory and the field. In an era of rapid environmental change, there is an urgent need to advance our understanding of the real-world impacts of chemical pollution on wildlife behaviour. This review therefore provides a roadmap of the major outstanding questions in behavioural ecotoxicology and highlights the need for increased cross-talk with other disciplines in order to find the answers.

Long-term tracking and quantification of individual behavior in bumble bee colonies

Social insects are ecologically dominant and provide vital ecosystem services. It is critical to understand collective responses of social insects such as bees to ecological perturbations. However, studying behavior of individual insects across entire colonies and across timescales relevant for colony performance (i.e., days or weeks) remains a central challenge. Here, we describe an approach for long-term monitoring of individuals within multiple bumble bee (Bombus spp.) colonies that combines the complementary strengths of multiple existing methods. Specifically, we combine (a) automated monitoring, (b) fiducial tag tracking, and (c) pose estimation to quantify behavior across multiple colonies over a 48 h period. Finally, we demonstrate the benefits of this approach by quantifying an important but subtle behavior (antennal activity) in bumble bee colonies, and how this behavior is impacted by a common environmental stressor (a neonicotinoid pesticide).

Context-dependent influence of threat on honey bee social network dynamics and brain gene expression

Adverse social experience affects social structure by modifying the behavior of individuals, but the relationship between an individual's behavioral state and its response to adversity is poorly understood. We leveraged naturally occurring division of labor in honey bees and studied the biological embedding of environmental threat using laboratory assays and automated behavioral tracking of whole colonies. Guard bees showed low intrinsic levels of sociability compared with foragers and nurse bees, but large increases in sociability following exposure to a threat. Threat experience also modified the expression of caregiving-related genes in a brain region called the mushroom bodies. These results demonstrate that the biological embedding of environmental experience depends on an individual's societal role and, in turn, affects its future sociability.

Stereoselective toxicity mechanism of neonicotinoid dinotefuran in honeybees: New perspective from a spatial metabolomics study

Development of stereoisomeric neonicotinoid pesticides with lower toxicity is key to preventing global population declines of honeybees, whereas little is known about the in situ metabolic regulation of honeybees in response to stereoisomeric pesticides. Herein, we demonstrate an integrated mass spectrometry imaging (MSI) and untargeted metabolomics method to disclose disturbed metabolic expression levels and spatial differentiation in honeybees (Apis cerana) associated with stereoisomeric dinotefuran. This method affords a metabolic network mapping capability regarding a wide range of metabolites involved in multiple metabolic pathways in honeybees. Metabolomics results indicate more metabolic pathways of honeybees can be significantly affected by S-(+)-dinotefuran than R-(-)-dinotefuran, such as tricarboxylic acid (TCA) cycle, glyoxylate and dicarboxylate metabolism, and various amino acid metabolisms. MSI results demonstrate the cross-regulation and spatial differentiation of crucial metabolites involved in the TCA cycle, purine, glycolysis, and amino acid metabolisms within honeybees. Taken together, the integrated MSI and metabolomics results indicated the higher toxicity of S-(+)-dinotefuran arises from metabolic pathway disturbance and its inhibitory role in the energy metabolism, resulting in significantly reduced degradation rates of detoxification mechanisms. From the view of spatial metabolomics, our findings provide novel perspectives for the development and applications of pure chiral agrochemicals.

Semi-automatic detection of honeybee brood hygiene—an example of artificial learning to facilitate ethological studies on social insects

Machine-learning techniques are shifting the boundaries of feasibility in many fields of ethological research. Here, we describe an application of machine learning to the detection/measurement of hygienic behaviour, an important breeding trait in the honey bee (Apis mellifera). Hygienic worker bees are able to detect and destroy diseased brood, thereby reducing the reproduction of economically important pathogens and parasites such as the Varroa mite (Varroa destructor). Video observation of this behaviour on infested combs has many advantages over other methods of measurement, but analysing the recorded material is extremely time-consuming. We approached this problem by combining automatic tracking of bees in the video recordings, extracting relevant features, and training a multi-layer discriminator on positive and negative examples of the behaviour of interest. Including expert knowledge into the design of the features lead to an efficient model for identifying the uninteresting parts of the video which can be safely skipped. This algorithm was then used to semiautomatically identify individual worker bees involved in the behaviour. Application of the machine-learning method allowed to save 70% of the time required for manual analysis, and substantially increased the number of cell openings correctly identified. It thereby turns video-observation of individual cell opening events into an economically competitive method for selecting potentially resistant bees. This method presents an example of how machine learning can be used to boost ethological research, and how it can generate new knowledge by explaining the learned decision rule in form of meaningful parameters.

The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions

Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists of trajectory data of social interactions, recorded from videos of freely behaving mice in a standard resident-intruder assay. To help accelerate behavioral studies, the CalMS21 dataset provides benchmarks to evaluate the performance of automated behavior classification methods in three settings: (1) for training on large behavioral datasets all annotated by a single annotator, (2) for style transfer to learn inter-annotator differences in behavior definitions, and (3) for learning of new behaviors of interest given limited training data. The dataset consists of 6 million frames of unlabeled tracked poses of interacting mice, as well as over 1 million frames with tracked poses and corresponding frame-level behavior annotations. The challenge of our dataset is to be able to classify behaviors accurately using both labeled and unlabeled tracking data, as well as being able to generalize to new settings.

Biomarkers in a socially exchanged /fluid reflect colony maturity, behavior, and distributed metabolism

In cooperative systems exhibiting division of labor, such as microbial communities, multicellular organisms, and social insect colonies, individual units share costs and benefits through both task specialization and exchanged materials. Socially exchanged fluids, like seminal fluid and milk, allow individuals to molecularly influence conspecifics. Many social insects have a social circulatory system, where food and endogenously produced molecules are transferred mouth-to-mouth (stomodeal trophallaxis), connecting all the individuals in the society. To understand how these endogenous molecules relate to colony life, we used quantitative proteomics to investigate the trophallactic fluid within colonies of the carpenter ant Camponotus floridanus. We show that different stages of the colony life cycle circulate different types of proteins: young colonies prioritize direct carbohydrate processing; mature colonies prioritize accumulation and transmission of stored resources. Further, colonies circulate proteins implicated in oxidative stress, ageing, and social insect caste determination, potentially acting as superorganismal hormones. Brood-caring individuals that are also closer to the queen in the social network (nurses) showed higher abundance of oxidative stress-related proteins. Thus, trophallaxis behavior could provide a mechanism for distributed metabolism in social insect societies. The ability to thoroughly analyze the materials exchanged between cooperative units makes social insect colonies useful models to understand the evolution and consequences of metabolic division of labor at other scales.

Division of labor is essential for cooperation, because groups can achieve more when individuals specialize in different tasks. This happens across the natural world, from different cells in organisms performing specific roles, to the individuals in an ant colony carrying out diverse duties. In both of these systems, individuals work together to ensure the survival of the collective unit – the body or the colony – instead of competing against each other. One of the main ways division of labor is evident within these two systems is regarding reproduction. Both in the body and in an ant colony, only one or a few individual units can reproduce, while the rest provide support. In the case of ant colonies, only queens and males reproduce, while the young workers nurse the brood and older workers forage for food.

This intense cooperation requires close communication between individual units – in the case of some species of ants, by sharing fluids mouth-to-mouth. These fluids contain food but also many molecules produced by the ants themselves, including proteins. Given that both individuals and the colony as a whole change as they age – with workers acquiring new roles, and new queens and males only reared once the colony is mature – it is likely that the proteins transmitted in the fluid also change.

To better understand whether the lifecycles of individuals and the age of the colony affect the fluids shared by carpenter ants Camponotus floridanus, Hakala et al. examined the ant-produced proteins in these fluids. This revealed differences in the proteins shared by young and mature colonies, and young nurse ants and older forager ants. In young colonies, the fluids contained proteins involved in fast sugar processing; while in mature colonies, the fluids contained more proteins to store nutrients, which help insect larvae grow into larger individuals, like queens. Young worker ants, who spend their time nursing the brood, produced more anti-aging proteins. This may be because these ants are in close contact with the queen, who lives much longer than the rest of the ants in the colony. Taken together, these observations suggest that ants divide the labor of metabolism, as well as work and reproduction.

Dividing the labor of metabolism among individuals is one more similarity between ants and the cells of a multicellular organism, like a fly or a human. Division of labor allows the sharing of burden, with some individuals lightening the load of others. Understanding how ants achieve this by sharing fluids could shed new light on this complex exchange at other scales or in other organisms. By matching proteins to life stages, researchers have a starting point to examine individual molecules in more detail.

Resolving the Rules of Robustness and Resilience in Biology Across Scales

Why do some biological systems and communities persist while others fail? Robustness, a system's stability, and resilience, the ability to return to a stable state, are key concepts that span multiple disciplines within and outside the biological sciences. Discovering and applying common rules that govern the robustness and resilience of biological systems is a critical step toward creating solutions for species survival in the face of climate change, as well as the for the ever-increasing need for food, health, and energy for human populations. We propose that network theory provides a framework for universal scalable mathematical models to describe robustness and resilience and the relationship between them, and hypothesize that resilience at lower organization levels contribute to robust systems. Insightful models of biological systems can be generated by quantifying the mechanisms of redundancy, diversity, and connectivity of networks, from biochemical processes to ecosystems. These models provide pathways towards understanding how evolvability can both contribute to and result from robustness and resilience under dynamic conditions. We now have an abundance of data from model and non-model systems and the technological and computational advances for studying complex systems. Several conceptual and policy advances will allow the research community to elucidate the rules of robustness and resilience. Conceptually, a common language and data structure that can be applied across levels of biological organization needs to be developed. Policy advances such as cross-disciplinary funding mechanisms, access to affordable computational capacity, and the integration of network theory and computer science within the standard biological science curriculum will provide the needed research environments. This new understanding of biological systems will allow us to derive ever more useful forecasts of biological behaviors and revolutionize the engineering of biological systems that can survive changing environments or disease, navigate the deepest oceans, or sustain life throughout the solar system.

Social insects: Stochastic switches and behavioral maturation in ants

Changes in behavior with age are widespread in social insects. Lifetime behavioral tracking of individual ants shows these changes are fast and stereotyped, yet stochastically timed. The finding may help shed light on the mechanisms of behavioral maturation and their role in division of labor in social insects.

TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields

Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms’ sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video-streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously using background-subtraction with real-time (60 Hz) tracking performance for up to approximately 256 individuals and estimates 2D visual-fields, outlines, and head/rear of bilateral animals, both in open and closed-loop contexts. Additionally, TRex offers highly accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5 and 46.7 times faster, and requires 2–10 times less memory, than comparable software (with relative performance increasing for more organisms/longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user-interface.