The initial fitness benefits of group living are considered to be the greatest hurdle to the evolution of sociality1, and evolutionary theory predicts that these benefits need to arise at very small group sizes2. Such benefits are thought to emerge partly from scaling effects that increase efficiency as group size increases3,4,5. In social insects and other taxa, the benefits of group living have been proposed to stem from division of labour5,6,7,8, which is characterized by between-individual variability and within-individual consistency (specialization) in task performance. However, at the onset of sociality groups were probably small and composed of similar individuals with potentially redundant—rather than complementary—function1. Self-organization theory suggests that division of labour can emerge even in relatively small, simple groups9,10. However, empirical data on the effects of group size on division of labour and on fitness remain equivocal6. Here we use long-term automated behavioural tracking in clonal ant colonies, combined with mathematical modelling, to show that increases in the size of social groups can generate division of labour among extremely similar workers, in groups as small as six individuals. These early effects on behaviour were associated with large increases in homeostasis—the maintenance of stable conditions in the colony11—and per capita fitness. Our model suggests that increases in homeostasis are primarily driven by increases in group size itself, and to a smaller extent by a higher division of labour. Our results indicate that division of labour, increased homeostasis and higher fitness can emerge naturally in social groups that are small and homogeneous, and that scaling effects associated with increasing group size can thus promote social cohesion at the incipient stages of group living.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Bumblebee cognitive abilities are robust to changes in colony size
Behavioral Ecology and Sociobiology Open Access 16 February 2023
The pupal moulting fluid has evolved social functions in ants
Nature Open Access 30 November 2022
Resource sharing is sufficient for the emergence of division of labour
Nature Communications Open Access 24 November 2022
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
Queller, D. C. Cooperators since life began. Q. Rev. Biol. 72, 184–188 (1997).
Nowak, M. A., Tarnita, C. E. & Wilson, E. O. The evolution of eusociality. Nature 466, 1057–1062 (2010).
Berdahl, A., Torney, C. J., Ioannou, C. C., Faria, J. J. & Couzin, I. D. Emergent sensing of complex environments by mobile animal groups. Science 339, 574–576 (2013).
Morand-Ferron, J. & Quinn, J. L. Larger groups of passerines are more efficient problem solvers in the wild. Proc. Natl Acad. Sci. USA 108, 15898–15903 (2011).
Waters, J. S., Holbrook, C. T., Fewell, J. H. & Harrison, J. F. Allometric scaling of metabolism, growth, and activity in whole colonies of the seed-harvester ant Pogonomyrmex californicus. Am. Nat. 176, 501–510 (2010).
Dornhaus, A., Powell, S. & Bengston, S. Group size and its effects on collective organization. Annu. Rev. Entomol. 57, 123–141 (2012).
Brahma, A., Mandal, S. & Gadagkar, R. Emergence of cooperation and division of labor in the primitively eusocial wasp Ropalidia marginata. Proc. Natl Acad. Sci. USA 115, 756–761 (2018).
Fewell, J. H. & Harrison, J. F. Scaling of work and energy use in social insect colonies. Behav. Ecol. Sociobiol. 70, 1047–1061 (2016).
Jeanson, R., Fewell, J. H., Gorelick, R. & Bertram, S. M. Emergence of increased division of labor as a function of group size. Behav. Ecol. Sociobiol. 62, 289–298 (2007).
Gautrais, J., Theraulaz, G., Deneubourg, J. L. & Anderson, C. Emergent polyethism as a consequence of increased colony size in insect societies. J. Theor. Biol. 215, 363–373 (2002).
Oldroyd, B. P. & Fewell, J. H. Genetic diversity promotes homeostasis in insect colonies. Trends Ecol. Evol. 22, 408–413 (2007).
Jeanson, R. & Weidenmüller, A. Interindividual variability in social insects - proximate causes and ultimate consequences. Biol. Rev. Camb. Philos. Soc. 89, 671–687 (2014).
Ravary, F. & Jaisson, P. Absence of individual sterility in thelytokous colonies of the ant Cerapachys biroi Forel (Formicidae, Cerapachyinae). Insectes Soc. 51, 67–73 (2004).
Ravary, F., Jahyny, B. & Jaisson, P. Brood stimulation controls the phasic reproductive cycle of the parthenogenetic ant Cerapachys biroi. Insectes Soc. 53, 20–26 (2006).
Oxley, P. R. et al. The genome of the clonal raider ant Cerapachys biroi. Curr. Biol. 24, 451–458 (2014).
Sendova-Franks, A. B. & Franks, N. R. Spatial relationships within nests of the ant Leptothorax unifasciatus (Latr) and their implications for the division of labor. Anim. Behav. 50, 121–136 (1995).
Gordon, D. M. Dynamics of task switching in harvester ants. Anim. Behav. 38, 194–204 (1989).
Mersch, D. P., Crespi, A. & Keller, L. Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science 340, 1090–1093 (2013).
Heyman, Y., Shental, N., Brandis, A., Hefetz, A. & Feinerman, O. Ants regulate colony spatial organization using multiple chemical road-signs. Nat. Commun. 8, 15414 (2017).
Crall, J. D. et al. Spatial fidelity of workers predicts collective response to disturbance in a social insect. Nat. Commun. 9, 1201 (2018).
Weidenmüller, A. The control of nest climate in bumblebee (Bombus terrestris) colonies: interindividual variability and self reinforcement in fanning response. Behav. Ecol. 15, 120–128 (2004).
Campos, D., Bartumeus, F., Méndez, V., Andrade, J. S. Jr & Espadaler, X. Variability in individual activity bursts improves ant foraging success. J. R. Soc. Interface 13, 20160856 (2016).
Bonabeau, E., Theraulaz, G. & Deneubourg, J.-L. Quantitative study of the fixed threshold model for the regulation of division of labour in insect societies. Proc. R. Soc. Lond. B 263, 1565–1569 (1996).
Pacala, S. W., Gordon, D. M. & Godfray, H. C. J. Effects of social group size on information transfer and task allocation. Evol. Ecol. 10, 127–165 (1996).
Franks, N. R. & Tofts, C. Foraging for work: how tasks allocate workers. Anim. Behav. 48, 470–472 (1994).
Gorelick, R., Bertram, S. M., Killeen, P. R. & Fewell, J. H. Normalized mutual entropy in biology: quantifying division of labor. Am. Nat. 164, 677–682 (2004).
Teseo, S., Châline, N., Jaisson, P. & Kronauer, D. J. C. Epistasis between adults and larvae underlies caste fate and fitness in a clonal ant. Nat. Commun. 5, 3363 (2014).
Crall, J. D. et al. Social context modulates idiosyncrasy of behaviour in the gregarious cockroach Blaberus discoidalis. Anim. Behav. 111, 297–305 (2016).
Freund, J. et al. Emergence of individuality in genetically identical mice. Science 340, 756–759 (2013).
Holbrook, C. T., Kukuk, P. F. & Fewell, J. H. Increased group size promotes task specialization in a normally solitary halictine bee. Behaviour 150, 1449–1466 (2013).
Ravary, F. & Jaisson, P. The reproductive cycle of thelytokous colonies of Cerapachys biroi Forel (Formicidae, Cerapachyinae). Insectes Soc. 49, 114–119 (2002).
R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, 2008).
Dodds, P. S. & Watts, D. J. Universal behavior in a generalized model of contagion. Phys. Rev. Lett. 92, 218701 (2004).
Bonabeau, E., Theraulaz, G. & Deneubourg, J.-L. Fixed response thresholds and the regulation of division of labor in insect societies. Bull. Math. Biol. 60, 753–807 (1998).
We thank A. Gal for advice on data analysis, O. Feinerman and M. Liu for contributions to the tracking algorithms, S. Leibler, Z. Frentz, and D. Jordan for helpful discussions. This work was supported by grant 1DP2GM105454-01 from the NIH, a Searle Scholar Award, a Klingenstein-Simons Fellowship Award in the Neurosciences, and a Pew Biomedical Scholar Award to D.J.C.K.; Swiss National Science Foundation Early Postdoc.Mobility (PBEZP3-140156) and Advanced Postdoc.Mobility (P300P3-147900) fellowships, and a Rockefeller University Women & Science fellowship to Y.U.; a Kravis Fellowship to J.S.; the National Science Foundation Graduate Research Fellowship under Grant No. DGE1656466 to C.K.T. This is Clonal Raider Ant Project paper number 8.
Nature thanks J. O’Dwyer and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Ant detection algorithm.
a, Example cropped frame showing one 16-worker colony after image correction. b, Colour-tag detection. The highlighted numbered zones are image regions containing pixels that were assigned a high probability for tag colours (green, blue, orange or pink) by a Bayesian classifier. c, Candidate ant detection. The highlighted numbered zones correspond to contiguous regions containing colour tags and pixels that were assigned a high probability for ant colour (that is, cuticle) by the classifier. d, Candidate ant orientation. Candidate ants are aligned using the segment connecting the two colour tags, and oriented (head down versus head up) on the basis of the relative position of cuticle- and tag-probability maxima (black stars on brown and blue lines, respectively) along the main axis of each region. e, Final IDs after using Munkres’ variant of the Hungarian assignment algorithm. Labels indicate colour IDs (thorax–abdomen; G, green; B, blue; O, orange; P, pink). Ants shown as examples in d are labelled in red. All assignments shown are correct, but one ant is missed (arrow). This panel is identical to Fig. 1a. f, Correlation between r.m.s.d. calculated from automated versus manual assignments for one 16-worker colony. The r.m.s.d. was computed from a subset of frames in the brood-care phase. n = (number of automated detections)/(number of manual detections). Pearson’s r = 0.95, P < 0.001.
Extended Data Fig. 2 The r.m.s.d. and fitness in genotype B.
a, Individual r.m.s.d. values for all workers of genotype B. Ants from the same colony are vertically aligned. The dashed line represents the expected r.m.s.d. assuming a uniform distribution of an ant’s positions. b, The dynamics of brood development as a function of group size in genotype B. The proportion of the brood in successive developmental stages (colours) in colonies of sizes 1–16 is shown. Wide and narrow bars indicate first and second brood generations, respectively. Black line, worker survival (mean ± s.e.m.).
Extended Data Fig. 3 The r.m.s.d. and spatial fidelity to the nest.
a, Correlation between individual r.m.s.d. and individual distance to the brood (mean ± s.e.m.) over the first brood-care phase in one colony of 16 workers (Spearman’s r = 0.93, P = 0; n = 16 ants). Behavioural traits are based on 209 manually tracked frames. Sample sizes (n) indicate the number of frames in which each ant was manually identified. Manual tracking was used here because the automated tracking algorithm does not allow us to locate the brood. b, Individuals with low r.m.s.d. (r.m.s.d. < 12 in Fig. 1b) have high spatial fidelity to the nest area. Each circle represents the spatial distribution of an ant (grey dots) with respect to the brood pile (shaded blue areas) in the brood-care phase. Panel titles indicate colony identity (for example, A8_3 is the third replicate colony of genotype A and size 8), ant identity (for example, BO for blue–orange) and individual r.m.s.d. In each colony, the brood pile was manually annotated every three days (that is, if the brood-care phase lasted nine days, three brood piles zones were manually annotated; zones could overlap or not, depending on how much the brood pile moved).
Extended Data Fig. 4 Effect of the larvae-to-workers ratio on behaviour and brood developmental time.
The number of workers was constant at 4, and the number of larvae varied between 4 and 16, so as to obtain larvae-to-workers ratios of 1, 2 or 4. a, Mean colony r.m.s.d. increased with the larvae-to-workers ratio (log-transformed r.m.s.d.: χ2 = 5.00, P = 0.03). b, Larval time to eclosion was unaffected by the larvae-to-workers ratio (time to eclosion transformed by (time to eclosion)5: χ2 = 0.17, P = 0.68). Sample sizes indicate the number of colonies in which at least one larva reached adulthood. In both panels, box plots represent the median (thick horizontal line); the lower and upper hinges correspond to the first and third quartile, respectively. The upper whiskers extend from the upper hinge to the largest value no further than 1.5× interquartile range from the hinge; the lower whiskers extend from the lower hinge to the smallest value no further than 1.5× interquartile range from the hinge.
Extended Data Fig. 5 Methods for behavioural analyses.
a, Resampling scheme. Ninety-five per cent confidence intervals were generated by resampling individual r.m.s.d. values from one colony of size 16 at a time. In the example here, the generation of confidence intervals for behavioural variation (standard deviation of r.m.s.d.) in colonies of size 6 is shown. The same method was used to generate confidence intervals for mean colony behaviour (mean r.m.s.d.). b–e, Computing specialization. b, Daily individual r.m.s.d. values in one colony of size 8. c, Daily individual r.m.s.d. ranks. d, Pairwise rank correlation matrix between days of the first brood-care phase. Values highlighted in red indicate rank correlations (Spearman, n = 16 ants) between consecutive days, which are averaged to compute short-term behavioural consistency. e, Mean r.m.s.d. ranks per brood-care phase used to compute long-term behavioural consistency.
Extended Data Fig. 6 Behaviour of the fixed threshold model.
One hundred replicates were simulated per group size. Parameterization: m = 2, η = 7, μ = 10, σ = 0.1, τ = 0.2 and δ = 0.6, corresponding to the filled circle symbol in Fig. 3b. a, Frequency of task 1 performance (measured across a simulation run) by individual ants at different group sizes; each point represents an ant and ants from the same colony are vertically aligned. b, Behavioural variation (standard deviation of individual task performance frequencies) across all 100 replicates for each group size, averaged over both tasks and shown relative to group size 16. c, Specialization in task performance relative to group size. Each point represents one colony, and the line represents the mean value (±s.e.m.) across all 100 replicates for each group size. In b, c, model output is in black and experimental data are in red. d. Mean values (±s.e.m.) of the rank correlation, task entropy and task consistency metrics across all 100 replicates at each group size.
Extended Data Fig. 7 The effect of stochasticity and specialization on proxies for fitness.
One hundred replicates were simulated per group size. Parameter settings for the deterministic model can be found in the Supplementary Methods; departures from deterministic model parameters are as in Extended Data Fig. 6. a, Short-term (single time step) stimulus fluctuations averaged across both tasks are shown across group sizes and for all models. b, Short-term (single time step) fluctuations in task performance frequency (measured by the proportion of the colony performing each task), averaged across both tasks, are shown across group sizes and for all models. c, Task neglect averaged across both tasks is shown across group sizes and for all models. In a–c, points represent the described averages, which have been further averaged (mean ± s.e.m.) across n = 100 replicate colonies of a given size. d, e, Relationship between specialization and short-term stimulus fluctuations (d) or short-term fluctuations in task performance frequency (e), in the full model when controlling for group size. Each point represents one simulated colony.
Extended Data Fig. 8 Behavioural homeostasis increases with group size.
a, Day-to-day fluctuations in colony mean r.m.s.d. (mean ± s.e.m.) decrease with group size (χ2 = 21.30, P = 3.93 × 10−6). Asterisks represent colony-level data. b, Mean spatial fidelity increases with group size. Black, colony mean r.m.s.d. as a function of group size (mean ± s.e.m.). Blue, 95% confidence intervals under the null hypothesis of no group-size effect on individual behaviour, generated by resampling individuals from colonies of size 16 (Extended Data Fig. 5a). Sample sizes are as in a. In both panels, data for genotypes A and B are pooled.
Extended Data Fig. 9 Task neglect.
a, Manually annotated nest area (blue) and control area (red) generated by rotating the nest area by 180° around the centre of the Petri dish. b, Task neglect (mean ± s.e.m.) decreases with group size. The proportion of frames in which no ant was found near the brood as a function of group size. Black, observed task neglect. Red, expected task neglect. c, Effective task neglect (mean ± s.e.m.) decreases with group size (χ2 = 13.36, P = 2.57 × 10−4). The difference between observed and expected task neglect is shown as a function of group size. Sample sizes are as in b. In b, c, data for genotypes A and B are pooled and asterisks represent colonies.
Extended Data Fig. 10 Control experiments.
a, b, Paint-marking did not disproportionately affect small colonies. Red asterisks indicate control colonies composed of unmarked ants; otherwise, data are as in Fig. 4b, c. a, Growth in colonies of unmarked ants (mean ± s.e.m.). Colony growth was unaffected by paint-marking (χ2 = 2.71, P = 0.10), the interaction of paint-marking with group size (χ2 = 0.31, P = 0.58) or the interaction of paint-marking with genotype (χ2 = 0.17, P = 0.68). b, Larval time-to-eclosion in colonies of unmarked ants (mean ± s.e.m.). Time to eclosion of larvae was increased by paint-marking of the workers (square-root-transformed time to eclosion: χ2 = 8.98, P = 0.003), but paint-marking did not interact with group size (χ2 = 0.09, P = 0.77) or genotype (χ2 = 0.22, P = 0.64). c–g, Effects of density on behaviour and fitness. Colonies consisted of 4 or 16 workers (and a matching number of larvae) in small or large Petri dishes (SB and LB, respectively), corresponding to 3 densities (shades of blue). c, Mean spatial fidelity (mean ± s.e.m.) was affected by group size (χ2 = 6.49, P = 0.01), box size (χ2 = 38.46, P = 5.6 × 10−10) and density (group size:box size: χ2 = 6.76, P = 0.009). d, Behavioural variation (mean ± s.e.m.) was affected by group size (χ2 = 7.44, P = 0.006) but not by box size (χ2 = 0.08, P = 0.77) or density (group size:box size: χ2 = 3.50, P = 0.06). e, Behavioural consistency (mean ± s.e.m.) was not affected by group size (χ2 = 0.03, P = 0.87), box size (χ2 = 0.22, P = 0.64) or density (group size:box size: χ2 = 0.02 P = 0.88). Behavioural consistency was transformed by (behavioural consistency + 0.21)1.5. f, Colony growth (mean ± s.e.m.) was affected by group size (χ2 = 3.91, P = 0.048), but not by box size (χ2 = 0.04, P = 0.85) or density (group size:box size: χ2 = 1.00, P = 0.32). Colony growth was transformed by (growth + 0.4)1.9. Thus, the effect of density is small relative to that of group size, and variation in density alone is therefore very unlikely to have confounded our results. g, Larval time-to-eclosion (mean ± s.e.m.) was affected by group size (χ2 = 35.74, P = 2.26 × 10−9) and box size (χ2 = 10.45, P = 0.001) but not by density (group size:box size: χ2 = 0.67, P = 0.41). Time to eclosion was transformed by (time to eclosion)−0.3. h–j, Removing individuals with more than three ovarioles from analyses did not qualitatively affect our results. h, Mean spatial fidelity of the colony increases with group size. Black, mean r.m.s.d. (±s.e.m.) as a function of group size, after excluding individuals with four or more ovarioles. Blue, 95% confidence interval generated by resampling workers from 16-worker colonies (Extended Data Fig. 5a). i, Behavioural variation increases with group size. Black, standard deviation in r.m.s.d. per colony as a function of group size (mean ± s.e.m.), after excluding individuals with more than three ovarioles. Ninety-five per cent confidence intervals and sample sizes are as in a. j, Day-to-day rank consistency increases with group size. Black, mean r.m.s.d. rank correlation coefficients over consecutive days in the first brood care phase as a function of group size (mean ± s.e.m.), after excluding individuals with more than three ovarioles. Blue, 95% confidence intervals generated by randomizing daily ranks in each colony. In a, b, h–j, data for genotypes A and B are pooled.
This file contains Supplementary Methods, Supplementary Notes and Supplementary References. The Supplementary Methods describe the experimental design and statistical analyses used for control experiments (effect of tagging, effect of density, effect of larvae number, effect of worker morphology), and detailed information on theoretical analyses (average task performance frequency, specialization, behavioural variation, fit of simulation runs, indicators of colony fitness, and effect of stochastic elements in the model). The Supplementary Notes show the results of statistical analyses (GLMs) performed on the main experiment data, and detailed results of theoretical analyses (rank correlation specialization and group size, model predictions for larger group sizes, comparison with other specialization metrics, indicators of fitness benefits, and effects of stochastic elements in the model).
Rights and permissions
About this article
Cite this article
Ulrich, Y., Saragosti, J., Tokita, C.K. et al. Fitness benefits and emergent division of labour at the onset of group living. Nature 560, 635–638 (2018). https://doi.org/10.1038/s41586-018-0422-6
This article is cited by
Bumblebee cognitive abilities are robust to changes in colony size
Behavioral Ecology and Sociobiology (2023)
Two simple movement mechanisms for spatial division of labour in social insects
Nature Communications (2022)
Resource sharing is sufficient for the emergence of division of labour
Nature Communications (2022)
The pupal moulting fluid has evolved social functions in ants
Experimental increase of worker diversity benefits brood production in ants
BMC Ecology and Evolution (2021)
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.