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.

1. 1. Adams CM
   2. Anderson MG
   3. Motto DG
   4. Price MP
   5. Johnson WA
   6. Welsh MJ

   (1998) Ripped pocket and pickpocket, novel Drosophila DEG/ENaC subunits expressed in early development and in mechanosensory neurons

   The Journal of Cell Biology 140:143–152.

   https://doi.org/10.1083/jcb.140.1.143

   • PubMed
   • Google Scholar
2. 1. Ahn J-E
   2. Chen Y
   3. Amrein H

   (2017) Molecular basis of fatty acid taste in Drosophila

   eLife 6:e30115.

   https://doi.org/10.7554/eLife.30115

   • Google Scholar
3. 1. Awasaki T
   2. Kimura K

   (1997) pox-neuro is required for development of chemosensory bristles in Drosophila

   Journal of Neurobiology 32:707–721.

   https://doi.org/10.1002/(SICI)1097-4695(19970620)32:7<707::AID-NEU6>3.0.CO;2-8

   • PubMed
   • Google Scholar
4. 1. Baddeley A
   2. Rubak E
   3. Turner R

   (2015)

    Methodology and Applications with R

   Spatial Point Patterns,  Methodology and Applications with R, CRC Press.

   • Google Scholar
5. 1. Baran P

   (1964) On distributed communications networks

   IEEE Transactions on Communications 12:1–9.

   https://doi.org/10.1109/TCOM.1964.1088883

   • Google Scholar
6. 1. Battesti M
   2. Moreno C
   3. Joly D
   4. Mery F

   (2012) Spread of social information and dynamics of social transmission within Drosophila groups

   Current Biology 22:309–313.

   https://doi.org/10.1016/j.cub.2011.12.050

   • PubMed
   • Google Scholar
7. 1. Bazazi S
   2. Buhl J
   3. Hale JJ
   4. Anstey ML
   5. Sword GA
   6. Simpson SJ
   7. Couzin ID

   (2008) Collective motion and cannibalism in locust migratory bands

   Current Biology 18:735–739.

   https://doi.org/10.1016/j.cub.2008.04.035

   • PubMed
   • Google Scholar
8. 1. Ben Jacob E
   2. Becker I
   3. Shapira Y
   4. Levine H

   (2004) Bacterial linguistic communication and social intelligence

   Trends in Microbiology 12:366–372.

   https://doi.org/10.1016/j.tim.2004.06.006

   • PubMed
   • Google Scholar
9. 1. Bertrand A
   2. Barbieri MA
   3. Córdova J
   4. Hernández C
   5. Gómez F
   6. Leiva F

   (2004) Diel vertical behaviour, predator–prey relationships, and occupation of space by jack mackerel (Trachurus murphyi) off Chile

   ICES Journal of Marine Science 61:1105–1112.

   https://doi.org/10.1016/j.icesjms.2004.06.010

   • Google Scholar
10. 1. Billen J

    (2006)

    Signal variety and communication in social insects

    Proc. Neth. Entomol. Soc. Meet 17:9–25.

    • Google Scholar
11. 1. Buhl J
    2. Sumpter DJT
    3. Couzin ID
    4. Hale JJ
    5. Despland E
    6. Miller ER
    7. Simpson SJ

    (2006) From disorder to order in marching locusts

    Science 312:1402–1406.

    https://doi.org/10.1126/science.1125142

    • Google Scholar
12. 1. Buhl J
    2. Sword GA
    3. Clissold FJ
    4. Simpson SJ

    (2011) Group structure in locust migratory bands

    Behavioral Ecology and Sociobiology 65:265–273.

    https://doi.org/10.1007/s00265-010-1041-x

    • Google Scholar
13. 1. Burg ED
    2. Langan ST
    3. Nash HA

    (2013) Drosophila social clustering is disrupted by anesthetics and in narrow abdomen ion channel mutants

    Genes, Brain, and Behavior 12:338–347.

    https://doi.org/10.1111/gbb.12025

    • PubMed
    • Google Scholar
14. 1. Chabaud MA
    2. Isabel G
    3. Kaiser L
    4. Preat T

    (2009) Social facilitation of long-lasting memory retrieval in Drosophila

    Current Biology 19:1654–1659.

    https://doi.org/10.1016/j.cub.2009.08.017

    • PubMed
    • Google Scholar
15. 1. Conder. PJ

    (1949) Individual distance

    Ibis 91:649–655.

    https://doi.org/10.1111/j.1474-919X.1949.tb02315.x

    • Google Scholar
16. 1. Couzin ID

    (2009) Collective cognition in animal groups

    Trends in Cognitive Sciences 13:36–43.

    https://doi.org/10.1016/j.tics.2008.10.002

    • PubMed
    • Google Scholar
17. 1. Dambly-Chaudière C
    2. Jamet E
    3. Burri M
    4. Bopp D
    5. Basler K
    6. Hafen E
    7. Dumont N
    8. Spielmann P
    9. Ghysen A
    10. Noll M

    (1992) The paired box gene pox neuro: a determinant of poly-innervated sense organs in Drosophila

    Cell 69:159–172.

    https://doi.org/10.1016/0092-8674(92)90127-X

    • PubMed
    • Google Scholar
18. 1. de Bono M

    (2003) Molecular approaches to aggregation behavior and social attachment

    Journal of Neurobiology 54:78–92.

    https://doi.org/10.1002/neu.10162

    • PubMed
    • Google Scholar
19. 1. de Bono M
    2. Bargmann CI

    (1998) Natural variation in a neuropeptide Y receptor homolog modifies social behavior and food response in C. elegans

    Cell 94:679–689.

    https://doi.org/10.1016/S0092-8674(00)81609-8

    • PubMed
    • Google Scholar
20. 1. Deisboeck TS
    2. Couzin ID

    (2009) Collective behavior in Cancer cell populations

    BioEssays 31:190–197.

    https://doi.org/10.1002/bies.200800084

    • PubMed
    • Google Scholar
21. 1. Detrain C
    2. Deneubourg J

    (2006) Self-organized structures in a superorganism: do ants “behave” like molecules?

    Physics of Life Reviews 3:162–187.

    https://doi.org/10.1016/j.plrev.2006.07.001

    • Google Scholar
22. 1. Dombrovski M
    2. Poussard L
    3. Moalem K
    4. Kmecova L
    5. Hogan N
    6. Schott E
    7. Vaccari A
    8. Acton S
    9. Condron B

    (2017) Cooperative behavior emerges among Drosophila larvae

    Current Biology 27:2821–2826.

    https://doi.org/10.1016/j.cub.2017.07.054

    • PubMed
    • Google Scholar
23. 1. Dombrovski M
    2. Kim A
    3. Poussard L
    4. Vaccari A
    5. Acton S
    6. Spillman E
    7. Condron B
    8. Yuan Q

    (2019) A plastic visual pathway regulates cooperative behavior in Drosophila larvae

    Current Biology 29:1866–1876.

    https://doi.org/10.1016/j.cub.2019.04.060

    • Google Scholar
24. 1. Gong Z
    2. Son W
    3. Chung YD
    4. Kim J
    5. Shin DW
    6. McClung CA
    7. Lee Y
    8. Lee HW
    9. Chang DJ
    10. Kaang BK
    11. Cho H
    12. Oh U
    13. Hirsh J
    14. Kernan MJ
    15. Kim C

    (2004) Two interdependent TRPV channel subunits, inactive and Nanchung, mediate hearing in Drosophila

    Journal of Neuroscience 24:9059–9066.

    https://doi.org/10.1523/JNEUROSCI.1645-04.2004

    • PubMed
    • Google Scholar
25. 1. Guo Y
    2. Wang Y
    3. Wang Q
    4. Wang Z

    (2014) The role of PPK26 in Drosophila larval mechanical nociception

    Cell Reports 9:1183–1190.

    https://doi.org/10.1016/j.celrep.2014.10.020

    • Google Scholar
26. Book

    1. Guo A
    2. Gong Z
    3. Li H
    4. Li Y
    5. Liu L
    6. Liu Q
    7. Lu H
    8. Pan Y
    9. Ren Q
    10. Wu Z
    11. Zhang K
    12. Zhu Y

    (2017)

    1.27 - Vision, Memory, and Cognition in Drosophila

    In: Byrne J ohnH, editors. Learning and Memory: A Comprehensive Reference (Second). Oxford: Academic Press. pp. 483–503.

    • Google Scholar
27. 1. Herbert-Read JE

    (2016) Understanding how animal groups achieve coordinated movement

    The Journal of Experimental Biology 219:2971–2983.

    https://doi.org/10.1242/jeb.129411

    • PubMed
    • Google Scholar
28. 1. Höltje M
    2. Hustert R

    (2003) Rapid mechano-sensory pathways code leg impact and elicit very rapid reflexes in insects

    Journal of Experimental Biology 206:2715–2724.

    https://doi.org/10.1242/jeb.00492

    • PubMed
    • Google Scholar
29. 1. Hutton AE

    (1978)

    Spatial relationships in perching barn and cliff swallows

    The Wilson Bulletin 93:396–403.

    • Google Scholar
30. 1. Jenett A
    2. Rubin GM
    3. Ngo TT
    4. Shepherd D
    5. Murphy C
    6. Dionne H
    7. Pfeiffer BD
    8. Cavallaro A
    9. Hall D
    10. Jeter J
    11. Iyer N
    12. Fetter D
    13. Hausenfluck JH
    14. Peng H
    15. Trautman ET
    16. Svirskas RR
    17. Myers EW
    18. Iwinski ZR
    19. Aso Y
    20. DePasquale GM
    21. Enos A
    22. Hulamm P
    23. Lam SC
    24. Li HH
    25. Laverty TR
    26. Long F
    27. Qu L
    28. Murphy SD
    29. Rokicki K
    30. Safford T
    31. Shaw K
    32. Simpson JH
    33. Sowell A
    34. Tae S
    35. Yu Y
    36. Zugates CT

    (2012) A GAL4-driver line resource for Drosophila neurobiology

    Cell Reports 2:991–1001.

    https://doi.org/10.1016/j.celrep.2012.09.011

    • PubMed
    • Google Scholar
31. 1. Jezovit JA
    2. Levine JD
    3. Schneider J

    (2017) Phylogeny, environment and sexual communication across the Drosophila genus

    The Journal of Experimental Biology 220:42–52.

    https://doi.org/10.1242/jeb.143008

    • PubMed
    • Google Scholar
32. 1. Jiao Y
    2. Moon SJ
    3. Wang X
    4. Ren Q
    5. Montell C

    (2008) Gr64f is required in combination with other gustatory receptors for sugar detection in Drosophila

    Current Biology 18:1797–1801.

    https://doi.org/10.1016/j.cub.2008.10.009

    • PubMed
    • Google Scholar
33. 1. Jolles JW
    2. Boogert NJ
    3. Sridhar VH
    4. Couzin ID
    5. Manica A

    (2017) Consistent individual differences drive collective behavior and group functioning of schooling fish

    Current Biology 27:2862–2868.

    https://doi.org/10.1016/j.cub.2017.08.004

    • PubMed
    • Google Scholar
34. 1. Karak S
    2. Jacobs JS
    3. Kittelmann M
    4. Spalthoff C
    5. Katana R
    6. Sivan-Loukianova E
    7. Schon MA
    8. Kernan MJ
    9. Eberl DF
    10. Göpfert MC

    (2015) Diverse roles of axonemal dyneins in Drosophila auditory neuron function and mechanical amplification in hearing

    Scientific Reports 5:17085.

    https://doi.org/10.1038/srep17085

    • Google Scholar
35. 1. Karkali K
    2. Martin-Blanco E

    (2017) Mechanosensing in the Drosophila nervous system

    Seminars in Cell & Developmental Biology 71:22–29.

    https://doi.org/10.1016/j.semcdb.2017.06.014

    • PubMed
    • Google Scholar
36. 1. Katz Y
    2. Tunstrøm K
    3. Ioannou CC
    4. Huepe C
    5. Couzin ID

    (2011) Inferring the structure and dynamics of interactions in schooling fish

    PNAS 108:18720–18725.

    https://doi.org/10.1073/pnas.1107583108

    • PubMed
    • Google Scholar
37. 1. Kent C
    2. Azanchi R
    3. Smith B
    4. Formosa A
    5. Levine JD

    (2008) Social context influences chemical communication in D. Melanogaster males

    Current Biology 18:1384–1389.

    https://doi.org/10.1016/j.cub.2008.07.088

    • PubMed
    • Google Scholar
38. 1. Kim SE
    2. Coste B
    3. Chadha A
    4. Cook B
    5. Patapoutian A

    (2012) The role of Drosophila piezo in mechanical nociception

    Nature 483:209–212.

    https://doi.org/10.1038/nature10801

    • PubMed
    • Google Scholar
39. 1. King AJ
    2. Wilson AM
    3. Wilshin SD
    4. Lowe J
    5. Haddadi H
    6. Hailes S
    7. Morton AJ

    (2012) Selfish-herd behaviour of sheep under threat

    Current Biology 22:R561–R562.

    https://doi.org/10.1016/j.cub.2012.05.008

    • PubMed
    • Google Scholar
40. 1. Klapoetke NC
    2. Murata Y
    3. Kim SS
    4. Pulver SR
    5. Birdsey-Benson A
    6. Cho YK
    7. Morimoto TK
    8. Chuong AS
    9. Carpenter EJ
    10. Tian Z
    11. Wang J
    12. Xie Y
    13. Yan Z
    14. Zhang Y
    15. Chow BY
    16. Surek B
    17. Melkonian M
    18. Jayaraman V
    19. Constantine-Paton M
    20. Wong GK
    21. Boyden ES

    (2014) Independent optical excitation of distinct neural populations

    Nature Methods 11:338–346.

    https://doi.org/10.1038/nmeth.2836

    • PubMed
    • Google Scholar
41. 1. Krupp JJ
    2. Kent C
    3. Billeter JC
    4. Azanchi R
    5. So AK
    6. Schonfeld JA
    7. Smith BP
    8. Lucas C
    9. Levine JD

    (2008) Social experience modifies pheromone expression and mating behavior in male Drosophila Melanogaster

    Current Biology 18:1373–1383.

    https://doi.org/10.1016/j.cub.2008.07.089

    • PubMed
    • Google Scholar
42. 1. Larsson MC
    2. Domingos AI
    3. Jones WD
    4. Chiappe ME
    5. Amrein H
    6. Vosshall LB

    (2004) Or83b encodes a broadly expressed odorant receptor essential for Drosophila olfaction

    Neuron 43:703–714.

    https://doi.org/10.1016/j.neuron.2004.08.019

    • PubMed
    • Google Scholar
43. 1. Levine JD
    2. Funes P
    3. Dowse HB
    4. Hall JC

    (2002) Resetting the circadian clock by social experience in Drosophila melanogaster

    Science 298:2010–2012.

    https://doi.org/10.1126/science.1076008

    • PubMed
    • Google Scholar
44. 1. Li J
    2. Zhang W
    3. Guo Z
    4. Wu S
    5. Jan LY
    6. Jan YN

    (2016) A defensive kicking behavior in response to mechanical stimuli mediated by Drosophila wing margin bristles

    The Journal of Neuroscience 36:11275–11282.

    https://doi.org/10.1523/JNEUROSCI.1416-16.2016

    • PubMed
    • Google Scholar
45. 1. Lihoreau M
    2. Clarke IM
    3. Buhl J
    4. Sumpter DJ
    5. Simpson SJ

    (2016) Collective selection of food patches in Drosophila

    The Journal of Experimental Biology 219:668–675.

    https://doi.org/10.1242/jeb.127431

    • PubMed
    • Google Scholar
46. 1. Liu W
    2. Liang X
    3. Gong J
    4. Yang Z
    5. Zhang YH
    6. Zhang JX
    7. Rao Y

    (2011) Social regulation of aggression by pheromonal activation of Or65a olfactory neurons in Drosophila

    Nature Neuroscience 14:896–902.

    https://doi.org/10.1038/nn.2836

    • PubMed
    • Google Scholar
47. 1. Liu Q
    2. Yang X
    3. Tian J
    4. Gao Z
    5. Wang M
    6. Li Y
    7. Guo A

    (2016) Gap junction networks in mushroom bodies participate in visual learning and memory in Drosophila

    eLife 5:e13238.

    https://doi.org/10.7554/eLife.13238

    • PubMed
    • Google Scholar
48. 1. Lu B
    2. LaMora A
    3. Sun Y
    4. Welsh MJ
    5. Ben-Shahar Y

    (2012) ppk23-Dependent chemosensory functions contribute to courtship behavior in Drosophila melanogaster

    PLOS Genetics 8:e1002587.

    https://doi.org/10.1371/journal.pgen.1002587

    • PubMed
    • Google Scholar
49. 1. Mamiya A
    2. Gurung P
    3. Tuthill JC

    (2018) Neural coding of leg proprioception in Drosophila

    Neuron 100:636–650.

    https://doi.org/10.1016/j.neuron.2018.09.009

    • PubMed
    • Google Scholar
50. 1. Masuyama K
    2. Zhang Y
    3. Rao Y
    4. Wang JW

    (2012) Mapping neural circuits with activity-dependent nuclear import of a transcription factor

    Journal of Neurogenetics 26:89–102.

    https://doi.org/10.3109/01677063.2011.642910

    • PubMed
    • Google Scholar
51. 1. McGuire SE
    2. Mao Z
    3. Davis RL

    (2004) Spatiotemporal gene expression targeting with the TARGET and Gene-Switch systems in Drosophila

    Science Signaling 2004:pl6.

    https://doi.org/10.1126/stke.2202004pl6

    • Google Scholar
52. 1. Miyamoto T
    2. Amrein H

    (2008) Suppression of male courtship by a Drosophila pheromone receptor

    Nature Neuroscience 11:874–876.

    https://doi.org/10.1038/nn.2161

    • PubMed
    • Google Scholar
53. 1. Mogilner A
    2. Edelstein-Keshet L
    3. Bent L
    4. Spiros A

    (2003) Mutual interactions, potentials, and individual distance in a social aggregation

    Journal of Mathematical Biology 47:353–389.

    https://doi.org/10.1007/s00285-003-0209-7

    • PubMed
    • Google Scholar
54. 1. Moon SJ
    2. Lee Y
    3. Jiao Y
    4. Montell C

    (2009) A Drosophila gustatory receptor essential for aversive taste and inhibiting male-to-male courtship

    Current Biology 19:1623–1627.

    https://doi.org/10.1016/j.cub.2009.07.061

    • PubMed
    • Google Scholar
55. 1. Moussaïd M
    2. Helbing D
    3. Theraulaz G

    (2011) How simple rules determine pedestrian behavior and crowd disasters

    PNAS 108:6884–6888.

    https://doi.org/10.1073/pnas.1016507108

    • PubMed
    • Google Scholar
56. 1. Nagy M
    2. Akos Z
    3. Biro D
    4. Vicsek T

    (2010) Hierarchical group dynamics in pigeon flocks

    Nature 464:890–893.

    https://doi.org/10.1038/nature08891

    • PubMed
    • Google Scholar
57. 1. Navarro J
    2. del Solar E

    (1975) Pattern of spatial distribution in Drosophila melanogaster

    Behavior Genetics 5:9–16.

    https://doi.org/10.1007/BF01067577

    • PubMed
    • Google Scholar
58. 1. Nottebohm E
    2. Usui A
    3. Therianos S
    4. Kimura K
    5. Dambly-Chaudière C
    6. Ghysen A

    (1994) The gene poxn controls different steps of the formation of chemosensory organs in Drosophila

    Neuron 12:25–34.

    https://doi.org/10.1016/0896-6273(94)90149-X

    • PubMed
    • Google Scholar
59. 1. Olds WH
    2. Xu T

    (2014) Regulation of food intake by mechanosensory ion channels in enteric neurons

    eLife 3:e04402.

    https://doi.org/10.7554/eLife.04402

    • Google Scholar
60. 1. Pak WL
    2. Grossfield J
    3. Arnold KS

    (1970) Mutants of the visual pathway of Drosophila Melanogaster

    Nature 227:518–520.

    https://doi.org/10.1038/227518b0

    • PubMed
    • Google Scholar
61. 1. Petit O
    2. Bon R

    (2010) Decision-making processes: the case of collective movements

    Behavioural Processes 84:635–647.

    https://doi.org/10.1016/j.beproc.2010.04.009

    • PubMed
    • Google Scholar
62. 1. Ramdya P
    2. Lichocki P
    3. Cruchet S
    4. Frisch L
    5. Tse W
    6. Floreano D
    7. Benton R

    (2015) Mechanosensory interactions drive collective behaviour in Drosophila

    Nature 519:233–236.

    https://doi.org/10.1038/nature14024

    • PubMed
    • Google Scholar
63. 1. Ramdya P
    2. Schneider J
    3. Levine JD

    (2017) The neurogenetics of group behavior in Drosophila Melanogaster

    The Journal of Experimental Biology 220:35–41.

    https://doi.org/10.1242/jeb.141457

    • PubMed
    • Google Scholar
64. 1. Schneider J
    2. Dickinson MH
    3. Levine JD

    (2012) Social structures depend on innate determinants and chemosensory processing in Drosophila

    PNAS 109:17174–17179.

    https://doi.org/10.1073/pnas.1121252109

    • PubMed
    • Google Scholar
65. 1. Seeds AM
    2. Ravbar P
    3. Chung P
    4. Hampel S
    5. Midgley FM
    6. Mensh BD
    7. Simpson JH

    (2014) A suppression hierarchy among competing motor programs drives sequential grooming in Drosophila

    eLife 3:e02951.

    https://doi.org/10.7554/eLife.02951

    • PubMed
    • Google Scholar
66. 1. Simon AF
    2. Chou MT
    3. Salazar ED
    4. Nicholson T
    5. Saini N
    6. Metchev S
    7. Krantz DE

    (2012) A simple assay to study social behavior in Drosophila: measurement of social space within a group

    Genes, Brain, and Behavior 11:243–252.

    https://doi.org/10.1111/j.1601-183X.2011.00740.x

    • PubMed
    • Google Scholar
67. 1. Stalker OJS
    2. Harrison D

    (1961)

    Spacing patterns of female Drosophila paramelanica

    Animal Behaviour 9:77–78.

    • Google Scholar
68. 1. Sumpter D
    2. Buhl J
    3. Biro D
    4. Couzin I

    (2008) Information transfer in moving animal groups

    Theory in Biosciences 127:177–186.

    https://doi.org/10.1007/s12064-008-0040-1

    • PubMed
    • Google Scholar
69. 1. Sweeney ST
    2. Broadie K
    3. Keane J
    4. Niemann H
    5. O'Kane CJ

    (1995) Targeted expression of tetanus toxin light chain in Drosophila specifically eliminates synaptic transmission and causes behavioral defects

    Neuron 14:341–351.

    https://doi.org/10.1016/0896-6273(95)90290-2

    • PubMed
    • Google Scholar
70. 1. Tinette S
    2. Zhang L
    3. Robichon A

    (2004) Cooperation between Drosophila flies in searching behavior

    Genes, Brain and Behavior 3:39–50.

    https://doi.org/10.1046/j.1601-183x.2003.0046.x

    • PubMed
    • Google Scholar
71. 1. Toda H
    2. Zhao X
    3. Dickson BJ

    (2012) The Drosophila female aphrodisiac pheromone activates ppk23(+) sensory neurons to elicit male courtship behavior

    Cell Reports 1:599–607.

    https://doi.org/10.1016/j.celrep.2012.05.007

    • PubMed
    • Google Scholar
72. 1. Turing AM

    (1952) The chemical basis of morphogenesis

    Phil Trans Roy Soc London 237:37–72.

    https://doi.org/10.1098/rstb.1952.0012

    • Google Scholar
73. Book

    1. Turner AB

    (2015)

    Spatial point patterns: methodology and applications with R

    Chapman & Hall/CRC Interdisciplinary Statistics.

    • Google Scholar
74. 1. Tuthill JC
    2. Wilson RI

    (2016) Parallel transformation of tactile signals in central circuits of Drosophila

    Cell 164:1046–1059.

    https://doi.org/10.1016/j.cell.2016.01.014

    • PubMed
    • Google Scholar
75. 1. Vosshall LB
    2. Stocker RF

    (2007) Molecular architecture of smell and taste in Drosophila

    Annual Review of Neuroscience 30:505–533.

    https://doi.org/10.1146/annurev.neuro.30.051606.094306

    • PubMed
    • Google Scholar
76. 1. Walker RG
    2. Willingham AT
    3. Zuker CS

    (2000) A Drosophila mechanosensory transduction channel

    Science 287:2229–2234.

    https://doi.org/10.1126/science.287.5461.2229

    • PubMed
    • Google Scholar
77. 1. Xie T
    2. Ho MCW
    3. Liu Q
    4. Horiuchi W
    5. Lin CC
    6. Task D
    7. Luan H
    8. White BH
    9. Potter CJ
    10. Wu MN

    (2018) A genetic toolkit for dissecting dopamine circuit function in Drosophila

    Cell Reports 23:652–665.

    https://doi.org/10.1016/j.celrep.2018.03.068

    • PubMed
    • Google Scholar
78. 1. Yan Z
    2. Zhang W
    3. He Y
    4. Gorczyca D
    5. Xiang Y
    6. Cheng LE
    7. Meltzer S
    8. Jan LY
    9. Jan YN

    (2013) Drosophila NOMPC is a mechanotransduction channel subunit for gentle-touch sensation

    Nature 493:221–225.

    https://doi.org/10.1038/nature11685

    • PubMed
    • Google Scholar
79. 1. Zhan YP
    2. Liu L
    3. Zhu Y

    (2016) Taotie neurons regulate appetite in Drosophila

    Nature Communications 7:13633.

    https://doi.org/10.1038/ncomms13633

    • PubMed
    • Google Scholar
80. 1. Zhang YV
    2. Ni J
    3. Montell C

    (2013a) The molecular basis for attractive salt-taste coding in Drosophila

    Science 340:1334–1338.

    https://doi.org/10.1126/science.1234133

    • PubMed
    • Google Scholar
81. 1. Zhang Z
    2. Li X
    3. Guo J
    4. Li Y
    5. Guo A

    (2013b) Two clusters of GABAergic ellipsoid body neurons modulate olfactory labile memory in Drosophila

    Journal of Neuroscience 33:5175–5181.

    https://doi.org/10.1523/JNEUROSCI.5365-12.2013

    • PubMed
    • Google Scholar
82. 1. Zhong L
    2. Hwang RY
    3. Tracey WD

    (2010) Pickpocket is a DEG/ENaC protein required for mechanical nociception in Drosophila larvae

    Current Biology 20:429–434.

    https://doi.org/10.1016/j.cub.2009.12.057

    • PubMed
    • Google Scholar

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

• 4,832

  views

• 589

  downloads

• 29

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.