Throughout the animal kingdom, collective behaviours are commonly observed at every scale, ranging from insect swarming to wildebeest migration (de Bono and Bargmann, 1998; Mogilner et al., 2003; Ben Jacob et al., 2004; Buhl et al., 2006; Bazazi et al., 2008; Deisboeck and Couzin, 2009). Assembling into social groups is a crucial survival strategy for many animals. For example, social groups are required for cooperative foraging (Dombrovski et al., 2017; Dombrovski et al., 2019), coordinating individuals’ actions and enhancing vigilance (Couzin, 2009; Nagy et al., 2010; Moussaïd et al., 2011; Jolles et al., 2017).

Numerous investigations have attempted to quantify the collective behaviours of animal groups, often by extracting high-level features of massive structures emerging from seemingly stochastic actions of individuals (Buhl et al., 2006; Nagy et al., 2010; Kim et al., 2012; Schneider et al., 2012; Ramdya et al., 2015; Jolles et al., 2017; Dombrovski et al., 2017; Jezovit et al., 2017). One shared feature of collective behaviours is the maintenance of a certain distance, or social space, between nearby individuals. For example, individuals within a group of tufted ducks (Conder., 1949), cliff swallows (Hutton, 1978) or sheep (King et al., 2012) maintain particular distances from each other within a group, which are also dynamically modulated by environmental stimuli (Bertrand et al., 2004). However, it remains unclear how individuals act together to form a cohesive social group and how social distance is regulated or maintained.

The transmission and perception of social signals within a group also plays an essential role in collective behaviour, particularly in coordinating group-level actions. In locusts, mechanosensation of abdominal tactile stimuli promotes the coordination of mass migration, while vision is required to detect the approach of others from behind (Buhl et al., 2006; Bazazi et al., 2008). In ants, tactile and chemosensory signals play an important role in cooperative foraging (Billen, 2006). Moreover, when traveling in a hierarchical group, pigeons rely on visual input from the left eye to respond quickly to the manoeuvres of others (Nagy et al., 2010). However, a systematic analysis of major sensory modalities to elucidate the functions underlying the assembly and sustainment of a social group has not yet been undertaken, partially due to a lack of suitable model organisms allowing for the effective manipulation of all members in a group with high precision and reproducibility.

Drosophila melanogaster is often overlooked when it comes to investigating group-level behaviours, despite the observation that fruit flies do indeed aggregate (Guo et al., 2017). In addition to assembling on food and oviposition sites (Lihoreau et al., 2016; Dombrovski et al., 2017), flies also exhibit local congregation without external artificial stimuli (Stalker and Harrison, 1961; Navarro and del Solar, 1975; Simon et al., 2012; Burg et al., 2013). The existing group-based behaviours, together with carefully controlled experimental conditions and powerful genetic tools, make fruit flies a promising neuroethological model for studying the mechanisms of collective behaviours (Ramdya et al., 2015; Ramdya et al., 2017; Dombrovski et al., 2017).

There are two major approaches among investigations of collective behaviours in Drosophila. One approach focuses on measuring the static end result, the last ‘snapshot’, of social aggregation and identifying potential sensory inputs (Stalker and Harrison, 1961; Navarro and del Solar, 1975; Simon et al., 2012; Burg et al., 2013), but does not examine the grouping process and its dynamics. The other approach is engaged in the study of patterns of interactions between individuals in a confined space, using networks of social interactions to understand information transmission (Schneider et al., 2012), but whether or how those described network properties contribute to social aggregation is not immediately clear. Interestingly, a study of the panic reactions of a group of flies to an aversive odour revealed features of collective avoidance (Ramdya et al., 2015). Social aggregation, in which individuals come together to eventually form a group, is distinct in many aspects from panic escape, where members of an existing group scatter. To date, there is no mechanistic framework explaining how dynamic social interactions between individuals (as a process) lead to a stable social aggregation (as a result), particularly in the absence of external stimuli.

In the current study, we established a paradigm for investigating connections between the collective actions of flies en masse and the process of social clustering, in which flies form a network with regularity. Analysis of the interaction dynamics of individual flies revealed that they rely on multiple sensory inputs, while collective local interactions drive the initiation and development of a stable, well-structured social cluster.

In this study, we established a simple collective behaviour paradigm using Drosophila as a model system to investigate how loosely distributed individuals come together to form a stable and orderly social cluster. Thus, we linked a dynamic progress (collective physical interactions) with its outcome (a structured cluster). We demonstrated that a group of individuals self-organised into a social cluster with topological features of a distributed network. This process relied on multiple sensory modalities as well as internal state and previous social experience. Moreover, the order emerged dynamically from stereotypical responses of numerous asymmetric encounters between pairs of individuals, while, abnormal encounter responses exhibited by mutant flies resulted in a failure of social clustering. These dyadic interactions, particularly near the border of a cluster, contributed to cluster growth, eventually incorporating all individuals. Physical contact in a group induced dramatic increases in the activity of tarsus ppk neurons, the activation of which caused flies to group quickly and form more compact clusters, demonstrating the important roles of ppk neurons in mediating dyadic interactions to promote clustering.

Many species exhibit massive collective behaviour, such as starling flocks (Nagy et al., 2010), schools of blue jack mackerel (Bertrand et al., 2004), stickleback shoals (Jolles et al., 2017), swarms of desert locusts (Buhl et al., 2006), and swarms of C. elegans (de Bono and Bargmann, 1998; de Bono, 2003). While Drosophila are commonly considered ‘solitary’ (Guo et al., 2017), increasing evidence suggests that Drosophila form social groups (Stalker and Harrison, 1961; Navarro and del Solar, 1975; Simon et al., 2012; Schneider et al., 2012; Burg et al., 2013; Ramdya et al., 2017; Guo et al., 2017). The lack of strikingly visible collective behaviour like that exhibited by birds and fish is made up for by the unique advantages of Drosophila for experimentally investigating the neural mechanisms underlying collective actions, including simple brain structure, tractable behaviour and a smaller genome. Additionally, in our paradigm, flies interacted collectively in a defined two-dimensional space, whereas many collective behaviours, including those of starlings and locusts, occur in an open three-dimensional space; thereby, our system reduced the difficulties of modelling. Furthermore, in a laboratory setting, we took into account the location and locomotion of all flies from the beginning, when individuals were still dispersed, to the end of the process, when all individuals joined to form a social cluster. Moreover, working with fruit flies allowed us to control many influential factors, including temperature, time of day, diet, physiological status of the animals (such as genetic background, age, and gender) and previous social experience. It is extremely difficult to control these factors in wild animals, despite the importance of such controls for social behaviour analysis. Homogenous data can help identify principal components by reducing undesirable variation and noise. Drosophila offers a simpler alternative for examining the basic principles of collective behaviour by modelling with data from field observations.

Although neural substrates in the peripheral and central nervous systems for controlling interactions between a pair of flies (such as in typical courtship and aggression behaviour) are increasingly well understood, little is known about the dynamics and structures of social groups in fruit flies. Built upon the investigations of aggregation of a group of Drosophila (Simon et al., 2012; Burg et al., 2013), our improved paradigm generates a relatively naturalistic environment with minimal perturbations to the flies, and reveals the unique structure of fly assemblage. The robust tendency to form organised social clusters provides an interesting entry point to investigate collective behaviours in fruit flies. As rudimentary as a Drosophila cluster appears compared with a swarm-like flock of starlings, the flies were not simply clumped together in a random manner. Rather, the arrangement of flies in clusters strongly resembled that of a distributed network, rather than a centralised or decentralised network (Baran, 1964). Previous studies revealed that maintaining appropriate social space in flies requires visual input (Simon et al., 2012; Burg et al., 2013), and is modulated by previous social experience (Simon et al., 2012) and anaesthetic treatment (Burg et al., 2013). Our quantitative behavioural analysis, in conjunction with neurogenetic approaches and surgical experiments, revealed that cluster formation by groups of flies is a highly dynamic process, requiring integration of visual, olfactory, gustatory, auditory and mechanosensory inputs. Impaired sensory functions resulted in abnormal encounter responses, and, in turn, a failure in cluster formation. Notably, we found sexual dimorphism in olfactory-mediated social aggregation, suggesting that, in male and female flies, different neural circuits regulate the process of social aggregation. Importantly, our data suggested that different sensory pathways are not functionally redundant, and even different mechanosensory neurons play different roles in shaping the formation and spacing of social clusters. Taken together, our results indicate that social clustering behaviour in fruit flies is more sophisticated than previously assumed and a model of dynamic integration of various sensory inputs is required to explain group-level aggregation behaviour.

We modelled how a fly behaves at different distances in response to influences from others, using a force field computed from putative attractive and repulsive forces. The attraction, likely mediated by vision and olfaction, is generally weak over a broad range of distances, whereas repulsion, likely through mechanosensation and gustation, is strong at short distances. The simulation results suggested that the antagonistic interaction between these forces is sufficient to generate the required social distance for cluster formation. Notably, Turing first proposed a theoretical model utilizing local activation and a long-range inhibition to explain a variety of patterns in biology (Turing, 1952). Similar models were developed to account for the self-organing pattern formation from cells to organisms, suggesting that diverse patterns of social structures do not necessitate the assumption of complex behavior (Detrain and Deneubourg, 2006). However, without the perspective of dynamic processes, our static models cannot account for the active process of pair-wise encounters. Our results demonstrated that a large number of random dyadic interactions can serve as a driving force for the emergence of an orderly social cluster. Generalized into a system of self-propelled particles (SPP), flies are likely to achieve clustering by a parallel process involving all individuals, each of which has only a strong local view, without a clear sense of other individuals or the final structure of the cluster.

Although the timing and locations of pair-wise encounters are highly variable, the types and consequences of these encounters are stereotypic, and, importantly, the net outcome of these collective events over time is the growth of a cluster to include all flies in the arena. Interestingly, in wild-type flies, the frequency of encounters decreases after the initiation of the social cluster, indicating that an increasing number of flies settle down through dyadic interactions. In addition, a large proportion of flies gradually joined the nascent cluster (nucleus), despite its sporadic occurrence and erratic location. Similar approaches have been observed in ‘food searching-aggregation’ behaviour of adult flies, in which signals were emitted from ‘primers’ for the subsequent arrival of ‘followers’ (Tinette et al., 2004), and in cooperative digging behaviour of larvae (Ben Jacob et al., 2004; Dombrovski et al., 2017; Dombrovski et al., 2019). Besides foraging, grouping together benefits individuals in other aspects of Drosophila life. For example, evidence suggests that grouping in flies can synchronise circadian rhythms (Levine et al., 2002), modulate gene expression (Krupp et al., 2008), change pheromone profiles (Krupp et al., 2008; Liu et al., 2011), facilitate social learning (Chabaud et al., 2009; Battesti et al., 2012) and generate a heightened awareness of environmental stressors (Ramdya et al., 2015). Therefore, our data support the existence of a generalised assembling strategy for a group of flies to form a well-structured social network with or without external stimuli.

Although flies also exhibit pairwise interactions during courtship and aggression, the dyadic actions of encounter events during social clustering are relatively distinct in several aspects. First, the duration of each encounter event is brief, lasting for less than 1 s. Second, two flies from an encounter event are unlikely to meet again within a short period. This might be partially due to the sheer number of flies in the arena. However, in settings with many flies, individuals during their aggression or courtship exhibit a strong tendency for object fixation, as the pairs chase and interact repeatedly despite surrounding flies. Third, the frequent mode of dyadic encounter is between a walking fly and a stationary fly. The typical output of encounter is one moving-away while the other staying. During courtship and aggression, however, it is more common for both flies to be moving before and after the interaction. The distinct behavioural characteristics of encounter events suggest that, during social clustering, flies are likely exhibiting a unique ‘mental’ state.

Social clustering is not the only instance in which a fly group uses pairwise interactions to convey information between individuals. Benton and colleagues demonstrated that enhanced CO₂-avoidance of a group of flies over individual performance also depends on pairwise interactions (Ramdya et al., 2015). It is likely that additional situations exist in which Drosophila exhibit stereotypic pairwise interactions that serve as general driving force for the emergence of order from an initially disordered group. In ants, spontaneously mobilized individuals in nests can contact and excite other ants thereby spreading rhythmic activity across the colony (Couzin, 2009). In swarms of insects, schools of fish and flocks of birds, once the entire group accomplishes pattern formation and moves cohesively with high dynamics, an individual gathers information from near neighbors to adjust its own speed and direction (Sumpter et al., 2008; Buhl et al., 2011; Katz et al., 2011). In these examples to maintain a relatively uniformed distance between moving individuals, spreading information depends on non-contact pairwise interactions, unlike those seen in Drosophila mediated via physical touches. Previous studies have suggested that an individual engaged in collective movements ‘averages’ its pairwise interactions with near neighbors, although features of residual three-body interactions are also present (Katz et al., 2011; Herbert-Read, 2016). Furthermore, it is possible that pairwise interactions play roles before collective movements unfold, such as to accumulate a sufficient number of individuals into a small space to escalate the group density beyond a threshold. or to influence the collective mood or decision-making of the group for departure (Petit and Bon, 2010). Overall, there appear to be common rules in the collective behaviors of flies and other animals. Information is processed and exchanged at a local scale between near neighbors, and individuals do not (and do not need to) have a global view (Buhl et al., 2011). Furthermore, ordered organization only emerges when the group reaches to a certain size (Buhl et al., 2006).

Inspection of all dyadic encounters in our analysis demonstrated that touches of peripheral appendages between a pair of flies evoke stay-or-go responses, which is an important element during the initiation, growth and maintenance of the SIN. Similarly, cascades of appendage-touch elicited interactions between fly pairs are reported to be responsible for the collective avoidance of CO₂ (Ramdya et al., 2015). Drosophila possess an extensive mechanosensation system involving different types of mechanosensory neurons (Karkali and Martin-Blanco, 2017). Remarkably, our results demonstrated that manipulating neural activities of mechanosensory neurons caused aberrant social clustering, suggesting an important role of mechanosensation in regulating social networks in adult fruit flies. Considering the broad expression of mechanosensory genes, it is possible that multiple mechanosensory pathways mediate inter-fly tactile interactions in a coordinated and/or redundant way. Ants are also reported to use antennae to communicate with each other via tactile signals and chemosensation (Billen, 2006). In addition, crowding of solitary locusts induces transformation into the gregarious phase. Tactile signals via appendage touches between individuals play an important role in this process (Höltje and Hustert, 2003; Buhl et al., 2006; Bazazi et al., 2008). Interestingly, we found that in grouped flies, the activity of ppk neurons was elevated via a contact-dependent mechanism, suggesting a conserved role of tactile perception in social grouping. Taken together, our study not only revealed a novel role of appendage-mediated tactile signals in the dynamic formation of social networks in flies, but also provides evidence of common features of collective behaviours among different species.

Our modelling and observations indicated that animals use attractive signals to assemble a large group, whereas repulsive signals between individuals prevent them from forming a jumbled mass. Although this study did not pinpoint the attractive signal(s), the regular pattern of social clusters and the ubiquity of appendage interactions between pairs of flies strongly suggest that a stereotypic contact-mediated repulsive force keeps the nearby flies apart at a ‘hard-core’ distance. Mechanosensation is likely to be necessary for flies to both exert and receive physical forces during a dyadic interaction. The tarsal ppk neurons are activated during direct social contact, suggesting the possibility that they may sense social tactile cues. The expression patterns of ppk-positive neurons in the appendages suggest the possibility that the normal function of these neurons is to perceive touches from other flies. However, it is also possible that ppk-neurons are also required on the force-exerting side, for example, to gauge or control the level of force generated to repel other flies. Activating ppk neurons leads to decreased repulsion between flies supports the second hypothesis. Detailed dissection of the roles of each type of ppk-neuron necessitates manipulating the subclasses with high spatial and temporal precision, which should be possible with future genetic tools (Jenett et al., 2012).

In conclusion, we developed a self-organised aggregation paradigm as a model for studying the emergence of ordered structures from collective interactions of individuals. Our findings highlight the importance of stereotypic dyadic interactions and ppk neurons in social clustering and spacing. Further, comprehensive exploration of the dynamics and guiding principles in social clustering in Drosophila could enable the development of a novel framework for understanding the molecular and neural bases of collective behaviour in other animals.

All data generated or analysed during this study are included in the manuscript and supporting files.

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Author details

1. Lifen Jiang

   1. School of Life Science, University of Science and Technology of China, Hefei, China
   2. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

   Contribution

   Conceptualization, Investigation, Methodology, Data analysis, Validation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3498-9481

2. Yaxin Cheng

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Data analysis, Software

   Competing interests

   No competing interests declared

3. Shan Gao

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Data analysis, Software

   Competing interests

   No competing interests declared

4. Yincheng Zhong

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Data analysis, Software

   Competing interests

   No competing interests declared

5. Chengrui Ma

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Data analysis, Software

   Competing interests

   No competing interests declared

6. Tianyu Wang

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China

   Contribution

   Data analysis, Software

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4169-8268

7. Yan Zhu

   1. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
   2. University of Chinese Academy of Sciences, Beijing, China
   3. Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China

   Contribution

   Conceptualization, Software, Analysis, Visualization, Methodology, Supervision, Funding acquisition

   For correspondence

   zhuyan@ibp.ac.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9858-9129

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