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Collective behaviour can stabilize ecosystems


Collective behaviour is common in bacteria, plants and animals, and therefore occurs across ecosystems, from biofilms to cities. With collective behaviour, social interactions among individuals propagate to affect the behaviour of groups, whereas group-level responses in turn affect individual behaviour. These cross-scale feedback loops between individuals, populations and their environments can provide fitness benefits, such as the efficient exploitation of uncertain resources, as well as costs, such as increased resource competition. Although the social mechanics of collective behaviour are increasingly well-studied, its role in ecosystems remains poorly understood. Here we introduce collective movement into a model of consumer–resource dynamics to demonstrate that collective behaviour can attenuate consumer–resource cycles and promote species coexistence. We focus on collective movement as a particularly well-understood example of collective behaviour. Adding collective movement to canonical unstable ecological scenarios causes emergent social–ecological feedback, which mitigates conditions that would otherwise result in extinction. Collective behaviour could play a key part in the maintenance of biodiversity.

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Fig. 1: Collective behaviour promotes ecosystem stability and species coexistence.
Fig. 2: A social–ecological feedback loop stabilizes ecosystems with collective consumers.
Fig. 3: With collective consumers, enriching the system increases the critical value of enrichment at which population cycles begin (K*), following approximately K* ≈ K.

Data availability

Output from the agent-based simulations can be found on GitHub ( and Zenodo (

Code availability

Simulation code and scripts for statistical analysis can be found on GitHub ( and Zenodo (


  1. 1.

    Chesson, P. General theory of competitive coexistence in spatially-varying environments. Theor. Popul. Biol. 58, 211–237 (2000).

  2. 2.

    Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).

  3. 3.

    Ellner, S. P., Snyder, R. E., Adler, P. B. & Hooker, G. An expanded modern coexistence theory for empirical applications. Ecol. Lett. 22, 3–18 (2019).

  4. 4.

    Rosenzweig, M. L. Paradox of enrichment: destabilization of exploitation ecosystems in ecological time. Science 171, 385–387 (1971).

  5. 5.

    Costantino, R. F., Cushing, J. M., Dennis, B. & Desharnais, R. A. Experimentally induced transitions in the dynamic behaviour of insect populations. Nature 375, 227–230 (1995).

  6. 6.

    Fussmann, G. F., Ellner, S. P., Shertzer, K. W. & Hairston, N. G. Jr. Crossing the Hopf bifurcation in a live predator-prey system. Science 290, 1358–1360 (2000).

  7. 7.

    Dalziel, B. D. et al. Persistent chaos of measles epidemics in the prevaccination United States caused by a small change in seasonal transmission patterns. PLoS Comput. Biol. 12, e1004655 (2016).

  8. 8.

    Darwin, C. On the Origin of Species by Means of Natural Selection, or The Preservation of Favoured Races in the Struggle for Life (John Murray, 1859).

  9. 9.

    Gause, G. F. Experimental analysis of Vito Volterra’s mathematical theory of the struggle for existence. Science 79, 16–17 (1934).

  10. 10.

    Hutchinson, G. E. The paradox of the plankton. Am. Nat. 95, 137–145 (1961).

    Article  Google Scholar 

  11. 11.

    Chesson, P. Multispecies competition in variable environments. Theor. Popul. Biol. 45, 227–276 (1994).

    Article  Google Scholar 

  12. 12.

    McCann, K., Hastings, A. & Huxel, G. R. Weak trophic interactions and the balance of nature. Nature 395, 794–798 (1998).

  13. 13.

    Rooney, N., McCann, K., Gellner, G. & Moore, J. C. Structural asymmetry and the stability of diverse food webs. Nature 442, 265–269 (2006).

  14. 14.

    Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).

  15. 15.

    May, R. M. Host-parasitoid systems in patchy environments: a phenomenological model. J. Anim. Ecol. 47, 833–844 (1978).

    Article  Google Scholar 

  16. 16.

    Briggs, C. J. & Hoopes, M. F. Stabilizing effects in spatial parasitoid–host and predator–prey models: a review. Theor. Popul. Biol. 65, 299–315 (2004).

  17. 17.

    Vicsek, T. & Zafeiris, A. Collective motion. Phys. Rep. 517, 71–140 (2012).

    Article  Google Scholar 

  18. 18.

    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).

  19. 19.

    Nagy, M., Akos, Z., Biro, D. & Vicsek, T. Hierarchical group dynamics in pigeon flocks. Nature 464, 890–893 (2010).

  20. 20.

    Dalziel, B. D., Corre, M. L., Côté, S. D. & Ellner, S. P. Detecting collective behaviour in animal relocation data, with application to migrating caribou. Methods Ecol. Evol. 7, 30–41 (2015).

    Article  Google Scholar 

  21. 21.

    Torney, C. J. et al. Inferring the rules of social interaction in migrating caribou. Phil. Trans. R. Soc. B 373, 20170385 (2018).

  22. 22.

    Fryxell, J. M., Mosser, A., Sinclair, A. R. E. & Packer, C. Group formation stabilizes predator–prey dynamics. Nature 449, 1041–1043 (2007).

  23. 23.

    Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I. & Shochet, O. Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75, 1226 (1995).

    CAS  Article  Google Scholar 

  24. 24.

    Buhl, J. et al. From disorder to order in marching locusts. Science 312, 1402–1406 (2006).

  25. 25.

    King, A. J., Fehlmann, G., Biro, D., Ward, A. J. & Fürtbauer, I. Re-wilding collective behaviour: an ecological perspective. Trends Ecol. Evol. 33, 347–357 (2018).

  26. 26.

    Sumpter, D. J. T. Collective Animal Behavior (Princeton Univ. Press, 2010).

  27. 27.

    Guttal, V. & Couzin, I. D. Social interactions, information use, and the evolution of collective migration. Proc. Natl Acad. Sci. USA 107, 16172–16177 (2010).

    CAS  Article  Google Scholar 

  28. 28.

    Barbier, M. & Watson, J. R. The spatial dynamics of predators and the benefits and costs of sharing information. PLoS Comput. Biol. 12, e1005147 (2016).

  29. 29.

    Lotka, A. J. Analytical note on certain rhythmic relations in organic systems. Proc. Natl Acad. Sci. USA 6, 410–415 (1920).

    CAS  Article  Google Scholar 

  30. 30.

    Rosenzweig, M. L. & MacArthur, R. H. Graphical representation and stability conditions of predator-prey interactions. Am. Nat. 97, 209–223 (1963).

    Article  Google Scholar 

  31. 31.

    Couzin, I. D., Krause, J., James, R., Ruxton, G. D. & Franks, N. R. Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218, 1–11 (2002).

  32. 32.

    Couzin, I. D., Krause, J., Franks, N. R. & Levin, S. A. Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005).

  33. 33.

    MacArthur, R. H. Population ecology of some warblers of northeastern coniferous forests. Ecology 39, 599–619 (1958).

    Article  Google Scholar 

  34. 34.

    Dalziel, B. D., Thomann, E., Medlock, J. & De Leenheer, P. Global analysis of a predator-prey model with variable predator search rate. J. Math. Biol. 81, 159–183 (2020).

  35. 35.

    Lukas, D. & Clutton-Brock, T. Social complexity and kinship in animal societies. Ecol. Lett. 21, 1129–1134 (2018).

  36. 36.

    Purves, D. W., Lichstein, J. W., Strigul, N. & Pacala, S. W. Predicting and understanding forest dynamics using a simple tractable model. Proc. Natl Acad. Sci. USA 105, 17018–17022 (2008).

    CAS  Article  Google Scholar 

  37. 37.

    Dalziel, B. D. et al. Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities. Science 362, 75–79 (2018).

  38. 38.

    Monk, C. T. et al. How ecology shapes exploitation: a framework to predict the behavioural response of human and animal foragers along exploration-exploitation trade-offs. Ecol. Lett. 21, 779–793 (2018).

  39. 39.

    Hutchins, D. A. & Fu, F. Microorganisms and ocean global change. Nat. Microbiol. 2, 17058 (2017).

  40. 40.

    Zakem, E. J. et al. Ecological control of nitrite in the upper ocean. Nat. Commun. 9, 1206 (2018).

  41. 41.

    Axtell, R. L. Zipf distribution of U.S. firm sizes. Science 293, 1818–1820 (2001).

  42. 42.

    Turchin, P. et al. Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization. Proc. Natl Acad. Sci. USA 115, E144–E151 (2018).

    CAS  Article  Google Scholar 

  43. 43.

    Press, W. H. Numerical Recipes in C (Cambridge Univ. Press, 1986).

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B.D.D. is supported by US National Science Foundation award EEID-1911994 and by grants from the David and Lucile Packard Foundation. J.R.W. is supported by the DARPA Young Faculty Award YFA N66001-17-1-4038 and the NASA A.8 project 80NSSC19K0203SPE. S.P.E. is supported by US National Institute of General Medical Sciences of the National Institutes of Health, award number R01 GM122062 and by US National Science Foundation award DEB-1933497. The authors thank P. Adler, S. Hacker, N. Hairston Jr, J. Lubchenco, J. Morales, B. Menge and their research groups for feedback on earlier versions of this manuscript.

Author information




B.D.D. conceived the work, analysed the data and drafted the manuscript. B.D.D., M.N., J.R.W. and S.P.E. interpreted the data and revised the manuscript.

Corresponding author

Correspondence to Benjamin D. Dalziel.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Vishwesha Guttal and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Negative density dependence in resource encounter rates promotes coexistence in collective consumers, reversing canonical competitive exclusion.

Solid lines show linear fits. Blue lines show fits that omit outlying abundances driven by initial conditions. Dashed lines enclose 5 standard errors on either side of lines of best fit. For independent consumers (top row), encounter rates remain near the expected value of e0 (black horizontal line). For collective consumers, mean encounter rate is lower and decreases with increasing abundance. Simulation parameters are given in Table 1.

Extended Data Fig. 2 Systematic differences in access to resources in collective consumers dependent on group structure and resource abundance.

a The per-capita encounter rate of questing consumers is positively correlated with the number of consumer groups. b-c The per-capita encounter rate of handling consumers is less strongly correlated with the number of groups, so questing consumers are more strongly disadvantaged when the consumer population forms into fewer groups. d The fraction of the consumer population questing varies with resource abundance as ϕ ~ eκR where κ is a scaling parameter.

Extended Data Fig. 3 Encounter rate depends on both the average size and number of collective groups.

a Solid line shows best fit via linear regression, dashed lines enclose ± 5 standard errors. b Residual variation in encounter rate as a function of the number of groups. c The average size of groups is inversely correlated with the number of groups.

Extended Data Fig. 4 A social–ecological feedback loop stabilizes ecosystems with collective consumers.

Results analogous to those shown in Fig. 2 but with the timescale for behavioural decisions δ, consumer mortality rate m and conversion efficiency b all decreased by a factor of 10, relative to their values shown in Table 1.

Extended Data Fig. 5 Resource abundance affects the relationship between consumer population size and the number of consumer groups.

There are more consumer groups for the same number of consumers when more resources are present. Point size is proportional to resource abundance.

Extended Data Fig. 6 Phase portraits of the one-consumer resource system with per-capita encounter rate viewed as a state variable.

a Data from the individual-based simulation with K = 8000 and the rest of the parameters at the values specified in Table 1, with encounter rate calculated using eqn. (5). b The same data, but replacing observed encounter rate with a Monod function e(R) = e0R/(R + g) where R is taken from the simulation data and the parameter g = 250 expresses the strength of the net impact of collective behaviour on encounter rate, as the resource abundance at which encounter rate is half its maximum value. Encounter rates shown are smoothed with a moving average with a bandwidth of 5 time units.

Extended Data Fig. 7 Collective behaviour promotes ecosystem stability in a range of contexts.

Stability results analogous to Fig. 1a,b with independent consumers in black and collective consumers in red for a,b collective resource, c,d pursuit and avoidance behaviours, e,f, alignment only, g,h low noise, and i,j, high noise. See Sensitivity Analysis in Supplementary Information.

Extended Data Fig. 8 Collective behaviour promotes ecosystem species coexistence in a range of contexts.

Coexistence results analogous to Fig. 1c,d with independent consumers in black and collective consumers in red for a,b collective resource, c,d pursuit and avoidance behaviours, e,f, alignment only, g,h low noise, and i,j, high noise. See Sensitivity Analysis in Supplementary Information.

Extended Data Fig. 9 Coexistence results when one of the competing consumers may behave independently while the other exhibits collective behaviour.

a both consumers independent; b superior consumer behaves collectively, inferior consumers independent; c superior consumer independent, inferior consumer behaves collectively; d both collective. The relative capture efficiency of the inferior consumer is 0.9 (Table 1).

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Dalziel, B.D., Novak, M., Watson, J.R. et al. Collective behaviour can stabilize ecosystems. Nat Ecol Evol 5, 1435–1440 (2021).

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