Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Learned use of an innate sound-meaning association in birds

Nature Ecology & Evolution (2025)Cite this article

Subjects

Abstract

Signals in vocal communication systems range from innate to learned. Although innate and learned signals are often assumed to be independent, Darwin speculated that they could be evolutionarily related, with the former being the foundation of the latter even in our own communication system, language. Here we test this hypothesis by studying the vocal communication systems of avian hosts of brood parasites. First, we show that 21 bird species separated by approximately 53 million years of evolution produce structurally similar ‘whining’ vocalizations towards their respective brood parasites. Exploring the social correlates of whining vocalization production, we find that species that produce this vocalization often exist in areas with dense parasite–host networks, suggesting that its production facilitates interactions among host species. Experiments across three continents show that this vocalization is referential towards brood parasites in multiple host species, that hearing them elicits an innate rapid recruiting response, and that host species from different continents respond equally to the whining vocalizations of each other, indicating that convergent use facilitates cooperative defences across species. Our results provide an example of a referential animal vocalization for which sound production in the correct context is learned but for which hearing it elicits an innate response, representing an intermediate between innate and learned signals.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Evolutionary history of vocalizations by hosts of brood parasites and macroecological correlates of whining vocalization production.
Fig. 2: Whining vocalizations are functionally referential in superb fairy-wrens and white-browed scrubwrens and attract the attention of nearby passerines.
Fig. 3: Whining vocalizations elicit an equivalent behavioural response at sympatric and allopatric host nests.

Similar content being viewed by others

Data availability

Datasets used in this study are available in the Supplementary Data.

Code availability

An R script containing all the codes used for statistical analyses is available in the Supplementary Code.

References

  1. Darwin, C. The Descent of Man, and Selection in Relation to Sex Vol. 1 (John Murray, 1871).

  2. Számadó, S. & Szathmáry, E. Selective scenarios for the emergence of natural language. Trends Ecol. Evol. 21, 555–561 (2006).

    Article  PubMed  Google Scholar 

  3. Wheeler, B. C. & Fischer, J. Functionally referential signals: a promising paradigm whose time has passed. Evol. Anthropol. 21, 195–205 (2012).

    Article  PubMed  Google Scholar 

  4. Jurisevic, M. A. & Sanderson, K. J. A comparative analysis of distress call structure in Australian passerine and non-passerine species: influence of size and phylogeny. J. Avian Biol. 29, 61–71 (1998).

    Article  Google Scholar 

  5. Aubin, T. Why do distress calls evoke interspecific responses? An experimental study applied to some species of birds. Behav. Process. 23, 103–111 (1991).

    Article  Google Scholar 

  6. Lingle, S. & Riede, T. Deer mothers are sensitive to infant distress vocalizations of diverse mammalian species. Am. Nat. 184, 510–522 (2014).

    Article  PubMed  Google Scholar 

  7. Pinker, S. Words and Rules: The Ingredients of Language (Basic Books, 1999).

  8. Blasi, D. E., Wichmann, S., Hammarström, H., Stadler, P. F. & Christiansen, M. H. Sound–meaning association biases evidenced across thousands of languages. Proc. Natl Acad. Sci. USA 113, 10818–10823 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Johansson, N., Anikin, A. & Aseyev, N. Color sound symbolism in natural languages. Lang. Cogn. 12, 56–83 (2020).

    Article  Google Scholar 

  10. Joo, I. Phonosemantic biases found in Leipzig–Jakarta lists of 66 languages. Linguist. Typol. 24, 1–12 (2020).

    Article  Google Scholar 

  11. Winter, B., Sóskuthy, M., Perlman, M. & Dingemanse, M. Trilled /r/ is associated with roughness, linking sound and touch across spoken languages. Sci. Rep. 12, 1035 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ćwiek, A. et al. Novel vocalizations are understood across cultures. Sci. Rep. 11, 10108 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Ludwig, V. U., Adachi, I. & Matsuzawa, T. Visuoauditory mappings between high luminance and high pitch are shared by chimpanzees (Pan troglodytes) and humans. Proc. Natl Acad. Sci. USA 108, 20661–20665 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Seyfarth, R. M. & Cheney, D. L. The origin of meaning in animal signals. Anim. Behav. 124, 339–346 (2017).

    Article  Google Scholar 

  15. Marler, P. The logical analysis of animal communication. J. Theor. Biol. 1, 295–317 (1961).

    Article  PubMed  Google Scholar 

  16. Cheney, D. L. & Seyfarth, R. M. Flexible usage and social function in primate vocalizations. Proc. Natl Acad. Sci. USA 115, 1974–1979 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Davies, N. B. Cuckoos, Cowbirds and Other Cheats (T & A. D. Poyser, 2000).

  18. Pollock, H. S., Hoover, J. P., Uy, F. M. K. & Hauber, M. E. Brood parasites are a heterogeneous and functionally distinct class of natural enemies. Trends Parasitol. 37, 588–596 (2021).

    Article  PubMed  Google Scholar 

  19. Langmore, N. E. et al. Coevolution with hosts underpins speciation in brood-parasitic cuckoos. Science 384, 1030–1036 (2024).

    Article  PubMed  Google Scholar 

  20. Feeney, W. E., Welbergen, J. A. & Langmore, N. E. Advances in the study of coevolution between avian brood parasites and their hosts. Annu. Rev. Ecol. Evol. Syst. 45, 227–246 (2014).

    Article  Google Scholar 

  21. Soler, M. Long-term coevolution between avian brood parasites and their hosts. Biol. Rev. 89, 688–704 (2014).

    Article  PubMed  Google Scholar 

  22. Kennerley, J. A. et al. The overlooked complexity of avian brood parasite–host relationships. Ecol. Lett. 25, 1889–1904 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Feeney, W. E., Welbergen, J. A. & Langmore, N. E. The frontline of avian brood parasite–host coevolution. Anim. Behav. 84, 3–12 (2012).

    Article  Google Scholar 

  24. Macedonia, J. M. & Evans, C. S. Essay on contemporary issues in ethology: variation among mammalian alarm call systems and the problem of meaning in animal signals. Ethology 93, 177–197 (1993).

    Article  Google Scholar 

  25. Gill, S. A. & Sealy, S. G. Functional reference in an alarm signal given during nest defence: seet calls of yellow warblers denote brood-parasitic brown-headed cowbirds. Behav. Ecol. Sociobiol. 56, 71–80 (2004).

    Article  Google Scholar 

  26. Feeney, W. E. et al. Brood parasitism and the evolution of cooperative breeding in birds. Science 342, 1506–1508 (2013).

    Article  PubMed  Google Scholar 

  27. Feeney, W. E. & Langmore, N. E. Social learning of a brood parasite by its host. Biol. Lett. 9, 20130443 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Langmore, N. E. et al. Learned recognition of brood parasitic cuckoos in the superb fairy-wren Malurus cyaneus. Behav. Ecol. 23, 798–805 (2012).

    Article  Google Scholar 

  29. Lawson, S. L. et al. Absence of referential alarm calls in long-term allopatry from the referent: a case study with Galapagos yellow warblers. Behav. Ecol. Sociobiol. 77, 99 (2023).

    Article  Google Scholar 

  30. Feeney, W. E. & Langmore, N. E. Superb fairy-wrens (Malurus cyaneus) increase vigilance near their nest with the perceived risk of brood parasitism. Auk 132, 359–364 (2015).

    Article  Google Scholar 

  31. Payne, R. B., Rowley, I. & Payne, L. L. Splendid wren Malurus splendens response to cuckoos: an experimental test of social organization in a communal bird. Behaviour 94, 108–126 (1985).

    Article  Google Scholar 

  32. Noh, H.-J., Jacomb, F., Gloag, R. & Langmore, N. E. Frontline defences against cuckoo parasitism in the large-billed gerygones. Anim. Behav. 174, 51–61 (2021).

    Article  Google Scholar 

  33. Wheatcroft, D. & Price, T. D. Rates of signal evolution are associated with the nature of interspecific communication. Behav. Ecol. 26, 83–90 (2015).

    Article  Google Scholar 

  34. Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).

    Article  PubMed  Google Scholar 

  35. Aubin, T. & Bremond, J. C. Perception of distress call harmonic structure by the starling (Sturnus vulgaris). Behaviour https://doi.org/10.1163/156853992X00570 (1992).

  36. Rowley, I. The life history of the superb blue wren Malurus cyaneus. Emu 64, 251–297 (1965).

    Article  Google Scholar 

  37. Sorenson, M. D. & Payne, R. B. Molecular genetic perspectives on avian brood parasitism. Integr. Comp. Biol. 42, 388–400 (2002).

    Article  PubMed  Google Scholar 

  38. Gloag, R., Fiorini, V. D., Reboreda, J. C. & Kacelnik, A. The wages of violence: mobbing by mockingbirds as a frontline defence against brood-parasitic cowbirds. Anim. Behav. 86, 1023–1029 (2013).

    Article  Google Scholar 

  39. Davies, N. B. & Brooke, M. de L. Cuckoos versus reed warblers: adaptations and counteradaptations. Anim. Behav. 36, 262–284 (1988).

    Article  Google Scholar 

  40. Langmore, N. E., Cockburn, A., Russell, A. F. & Kilner, R. M. Flexible cuckoo chick-rejection rules in the superb fairy-wren. Behav. Ecol. 20, 978–984 (2009).

    Article  Google Scholar 

  41. Hoover, J. P. & Robinson, S. K. Retaliatory mafia behavior by a parasitic cowbird favors host acceptance of parasitic eggs. Proc. Natl Acad. Sci. USA 104, 4479–4483 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ficken, M. S. & Popp, J. A comparative analysis of passerine mobbing calls. Auk 113, 370–380 (1996).

    Article  Google Scholar 

  43. Davies, N. B. & Welbergen, J. A. Social transmission of a host defense against cuckoo parasitism. Science 324, 1318–1320 (2009).

    Article  PubMed  Google Scholar 

  44. Campobello, D., Sealy, S. G. & Welbergen, J. A. in Avian Brood Parasitism (ed. Soler, M.) 421–436 (Springer, 2017).

  45. Lawson, S. L., Enos, J. K., Gill, S. A. & Hauber, M. E. Red-winged blackbirds nesting nearer to yellow warbler and conspecific nests experience less brood parasitism. Ecol. Evol. 13, e9818 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Canestrari, D., Marcos, J. M. & Baglione, V. Cooperative breeding in carrion crows reduces the rate of brood parasitism by great spotted cuckoos. Anim. Behav. 77, 1337–1344 (2009).

    Article  Google Scholar 

  47. Trnka, A. & Prokop, P. Polygynous great reed warblers Acrocephalus arundinaceus suffer more cuckoo Cuculus canorus parasitism than monogamous pairs. J. Avian Biol. 42, 192–195 (2011).

    Article  Google Scholar 

  48. Brown, M. & Lawes, M. J. Colony size and nest density predict the likelihood of parasitism in the colonial Southern red bishop Euplectes orix–Diderick cuckoo Chrysococcyx caprius system. Ibis 149, 321–327 (2007).

    Article  Google Scholar 

  49. Wells, M. T. & Barker, F. K. Big groups attract bad eggs: brood parasitism correlates with but does not cause cooperative breeding. Anim. Behav. 133, 47–56 (2017).

    Article  Google Scholar 

  50. Brooker, M. G. & Brooker, L. C. Cuckoo hosts in Australia. Aust. Zoo. Rev. 2, 1–67 (1989).

    Google Scholar 

  51. Curio, E., Ernst, U. & Vieth, W. Cultural transmission of enemy recognition: one function of mobbing. Science 202, 899–901 (1978).

    Article  PubMed  Google Scholar 

  52. Marler, P. Characteristics of some animal calls. Nature 176, 6–8 (1955).

    Article  Google Scholar 

  53. Morton, E. S. On the occurrence and significance of motivation-structural rules in some bird and mammal sounds. Am. Nat. 111, 855–869 (1977).

    Article  Google Scholar 

  54. King, S. L. & Janik, V. M. Bottlenose dolphins can use learned vocal labels to address each other. Proc. Natl Acad. Sci. USA 110, 13216–13221 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Pardo, M. A. et al. African elephants address one another with individually specific name-like calls. Nat. Ecol. Evol. 8, 1353–1364 (2024).

    Article  PubMed  Google Scholar 

  56. Heine, B. The Grammar of Interactives (Oxford Univ. Press, 2023).

  57. Ameka, F. Interjections: the universal yet neglected part of speech. J. Pragmat. 18, 101–118 (1992).

    Article  Google Scholar 

  58. Watson, S. K. et al. Optionality in animal communication: a novel framework for examining the evolution of arbitrariness. Biol. Rev. 97, 2057–2075 (2022).

    Article  PubMed  Google Scholar 

  59. Flower, T. P., Gribble, M. & Ridley, A. R. Deception by flexible alarm mimicry in an African bird. Science 344, 513–516 (2014).

    Article  PubMed  Google Scholar 

  60. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).

  61. Ripley, B. et al. MASS: support functions and datasets for Venables and Ripley’s MASS. R package version 7.3-61 (2023).

  62. Brooks, M. et al. glmmTMB: Generalized linear mixed models using template model builder. R package version 1.1.10 (2023).

  63. Lenth, R. V. emmeans: Estimated marginal means, aka Least-Squares Means. R package version 1.8.7 (2023).

  64. Revell, L. J. phytools: Phylogenetic tools for comparative biology (and other things). R package version 0.7.47 (2023).

  65. Adams, D. C. Comparing evolutionary rates for different phenotypic traits on a phylogeny using likelihood. Syst. Biol. 62, 181–192 (2013).

    Article  PubMed  Google Scholar 

  66. Paradis, E. et al. ape: Analyses of phylogenetics and evolution. R package version 5.7 (2023).

  67. Lawson, S. L. et al. Do hosts of avian brood parasites discriminate parasitic vs. predatory threats? A meta-analysis. in Advances in the Study of Behaviour Vol. 53 (eds Naguib, M. et al.) 63–95 (Academic Press, 2021).

  68. Cockburn, A. Prevalence of different modes of parental care in birds. Proc. R. Soc. B Biol. Sci. 273, 1375–1383 (2006).

    Article  Google Scholar 

  69. Sahr, K., White, D. & Kimerling, A. J. Geodesic discrete global grid systems. Cartogr. Geogr. Inf. Sci. 30, 121–134 (2003).

    Article  Google Scholar 

  70. BirdLife International and Handbook of the Birds of the World: Bird Species Distribution Maps of the World (BirdLife International, 2017; accessed 9 June 2020); http://datazone.birdlife.org/species/requestdis

  71. Dormann, C. F., Gruber, B. & Fruend, J. Introducing the bipartite package: analysing ecological networks. R. J. 8, 8–11 (2008).

    Google Scholar 

  72. Lawson, S. L., Enos, J. K., Mendes, N. C., Gill, S. A. & Hauber, M. E. Heterospecific eavesdropping on an anti-parasitic referential alarm call. Commun. Biol. 3, 143 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Yasukawa, K. & Cockburn, A. Antipredator vigilance in cooperatively breeding superb fairy-wrens (Malurus cyaneus). Auk 126, 147–154 (2009).

    Article  Google Scholar 

  74. Hartig, F. & Lohse, L. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.4.6 (2022).

  75. Thorsten, P. PMCMRplus: calculate pairwise multiple comparisons of mean rank sums extended. R package version 1.9.6 (2022).

  76. Raven Pro: interactive sound analysis software (K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology, 2023).

  77. Tobias, J. A. et al. AVONET: morphological, ecological and geographical data for all birds. Ecol. Lett. 25, 581–597 (2022).

    Article  PubMed  Google Scholar 

  78. Lawson, S. L. et al. Referential alarm calling elicits future vigilance in a host of an avian brood parasite. Biol. Lett. 17, 20210377 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the Alexander von Humboldt Foundation (to W.E.F. and M.E.H.), Birds Queensland (to W.E.F. and M.S.W.), the British Trust for Ornithology (to W.E.F.), the Hermon-Slade Foundation (no. HS15/1 to W.E.F.), Griffith University and the University of Queensland (to W.E.F.), a Whitten PhD Studentship in the Department of Zoology, University of Cambridge (to J.A.K.), a Clare Hall Research Award, University of Cambridge (to J.A.K.), an Edward W. Rose Postdoctoral Fellowship in the Cornell Lab of Ornithology for part of this work (to J.A.K.), the US National Science Foundation (no. 1353681 to M.S.W., no. 1953226 to M.E.H. and no. 1952726 to S.A.G.), the National Key R & D Program of China (2023YFF1304600 to W.L.), the National Natural Science Foundation of China (32270526 and 32470513 to W.L.), the National Geographic Society (NGS-60453R-19 to M.E.H.), the Australian Research Council (FT110100505 and DP150103595 to A.P.), the Australian Wildlife Conservancy (to N.T. and A.P.), a Royal Society Dorothy Hodgkin Fellowship (to C.N.S.), a Biotechnology and Biological Sciences Research Council David Phillips Research Fellowship (BB/J014109/1) (to C.N.S.), the National Agency for the Promotion of Science and Technology (ANPCyT), National Science Centre, Poland (grants 2012/05/E/NZ8/02694, 2016/23/B/NZ8/03082 and 2022/45/B/NZ8/03740 to A.A., R. Gula and J.T.), and the National Scientific and Technical Research Council (CONICET) (to V.D.F.). We also thank E. Miller for sharing pipelines, which helped with constructing phylogenetically corrected linear models, as well as B. Kempenaers, C. Riehl and J. Fischer for comments on the manuscript. Finally, we thank all our field assistants (E. Aarsvold, A. Branney, R. Bracken, C. Brock, J. D. Brooks, M. Chan, L. Clarke, J. Cosentino, Z. Davis, W. Deptula, B. Donnelly, V. Drolet-Gratton, S. Dougill, R. Green, D. Erickson, M. Freeby, D. Ferraro, L. Fried, J. Grayum, K. Gielow, M. Grundler, J. Grudens, C. Hawey, N. Hunt, L. Huntsmith, O. Kashembe, S. LeQuier, L. Lichtenauer, M. Marsh, A. Miller, C. Moya, S. Mwanza, J. Platzer, R. Neil, A. Sargent, M. Scheuering, D. Thrasher, D. Tolman, R. Weisbeck, J. Welklin, A. Werrell, J. Upton and E. Zarri) for their time and expertise, without which this research would not have been possible.

Author information

Author notes

  1. These authors contributed equally: William E. Feeney, James A. Kennerley.

Authors and Affiliations

  1. Doñana Biological Station, Spanish National Research Council (CSIC), Seville, Spain

    William E. Feeney

  2. School of Environment and Science, Griffith University, Nathan, Queensland, Australia

    William E. Feeney & Nicole M. Richardson

  3. Department of Biosciences, Durham University, Durham, UK

    William E. Feeney

  4. Max Planck Institute for Biological Intelligence, Seewiesen, Germany

    William E. Feeney

  5. School of Biological Sciences, University of Queensland, Brisbane, Queensland, Australia

    William E. Feeney

  6. Department of Zoology, University of Cambridge, Cambridge, UK

    James A. Kennerley, Mairenn Attwood, Claire N. Spottiswoode & Andrea Manica

  7. Cornell Lab of Ornithology, Ithaca, NY, USA

    James A. Kennerley, Colleen Poje, Nicole M. Richardson, Thomas A. Ryan, Jordan Boersma, Marissa Zamora & Michael S. Webster

  8. Department of Zoology, Stockholm University, Stockholm, Sweden

    David Wheatcroft

  9. Ministry of Education Key Laboratory for Ecology of Tropical Islands, College of Life Sciences, Hainan Normal University, Haikou, China

    Wei Liang & Bo Zhou

  10. Department of Ecology and Evolutionary Biology, University of California, Irvine, CA, USA

    Joleah B. Lamb

  11. School of Biological Sciences, Monash University, Clayton, Victoria, Australia

    Niki Teunissen & Anne Peters

  12. Department of Animal Sciences, Wageningen University and Research, Wageningen, The Netherlands

    Niki Teunissen

  13. Department of Integrative Biology, Oregon State University, Corvallis, OR, USA

    Shelby L. Lawson

  14. Illinois Natural History Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Janice K. Enos

  15. Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, USA

    Colleen Poje & Michael S. Webster

  16. Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA

    Thomas A. Ryan

  17. Department of Biological and Environmental Sciences, University of Gothenburg, Kristineberg Center, Fiskebäckskil, Sweden

    Zara-Louise Cowan

  18. Australian Institute of Marine Science, Indian Ocean Marine Research Centre, The University of Western Australia, Crawley, Western Australia, Australia

    Rohan M. Brooker

  19. Museum and Institute of Zoology, Polish Academy of Sciences, Warsaw, Poland

    Alfredo Attisano, Roman Gula & Jörn Theuerkauf

  20. School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Ros Gloag

  21. Departamento de Ecología, Genética y Evolución and Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA-UBA-CONICET), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina

    Vanina D. Fiorini

  22. Department of Biological Sciences, Western Michigan University, Kalamazoo, MI, USA

    Sharon A. Gill

  23. Institute of Vertebrate Biology, Czech Academy of Sciences, Brno, Czech Republic

    Marcel Honza

  24. FitzPatrick Institute of African Ornithology, Department of Biological Sciences, University of Cape Town, Cape Town, South Africa

    Claire N. Spottiswoode

  25. Advanced Science Research Center and Programs in Biology and in Psychology, Graduate Center of the City University of New York, New York, NY, USA

    Mark E. Hauber

  26. Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA

    Damián E. Blasi

  27. Catalan Institute for Advanced Study, Barcelona, Spain

    Damián E. Blasi

  28. Center for Brain and Cognition, Pompeu Fabra University, Barcelona, Spain

    Damián E. Blasi

Authors
  1. William E. Feeney
    View author publications

    Search author on:PubMed Google Scholar

  2. James A. Kennerley
    View author publications

    Search author on:PubMed Google Scholar

  3. David Wheatcroft
    View author publications

    Search author on:PubMed Google Scholar

  4. Wei Liang
    View author publications

    Search author on:PubMed Google Scholar

  5. Joleah B. Lamb
    View author publications

    Search author on:PubMed Google Scholar

  6. Niki Teunissen
    View author publications

    Search author on:PubMed Google Scholar

  7. Shelby L. Lawson
    View author publications

    Search author on:PubMed Google Scholar

  8. Janice K. Enos
    View author publications

    Search author on:PubMed Google Scholar

  9. Bo Zhou
    View author publications

    Search author on:PubMed Google Scholar

  10. Colleen Poje
    View author publications

    Search author on:PubMed Google Scholar

  11. Nicole M. Richardson
    View author publications

    Search author on:PubMed Google Scholar

  12. Thomas A. Ryan
    View author publications

    Search author on:PubMed Google Scholar

  13. Zara-Louise Cowan
    View author publications

    Search author on:PubMed Google Scholar

  14. Rohan M. Brooker
    View author publications

    Search author on:PubMed Google Scholar

  15. Mairenn Attwood
    View author publications

    Search author on:PubMed Google Scholar

  16. Jordan Boersma
    View author publications

    Search author on:PubMed Google Scholar

  17. Marissa Zamora
    View author publications

    Search author on:PubMed Google Scholar

  18. Alfredo Attisano
    View author publications

    Search author on:PubMed Google Scholar

  19. Roman Gula
    View author publications

    Search author on:PubMed Google Scholar

  20. Jörn Theuerkauf
    View author publications

    Search author on:PubMed Google Scholar

  21. Ros Gloag
    View author publications

    Search author on:PubMed Google Scholar

  22. Vanina D. Fiorini
    View author publications

    Search author on:PubMed Google Scholar

  23. Sharon A. Gill
    View author publications

    Search author on:PubMed Google Scholar

  24. Anne Peters
    View author publications

    Search author on:PubMed Google Scholar

  25. Marcel Honza
    View author publications

    Search author on:PubMed Google Scholar

  26. Claire N. Spottiswoode
    View author publications

    Search author on:PubMed Google Scholar

  27. Mark E. Hauber
    View author publications

    Search author on:PubMed Google Scholar

  28. Andrea Manica
    View author publications

    Search author on:PubMed Google Scholar

  29. Michael S. Webster
    View author publications

    Search author on:PubMed Google Scholar

  30. Damián E. Blasi
    View author publications

    Search author on:PubMed Google Scholar

Contributions

W.E.F. conceived the study with input from J.A.K., D.W., D.E.B., A.M., W.L. and M.S.W. W.E.F., J.A.K., B.Z., S.L.L., J.K.E., N.M.R., N.T., M.A., S.A.G., V.D.F., J.B., M.Z., A.A., R. Gula, J.T. and M.E.H. collected the data. W.E.F., J.A.K., A.M. and D.W. implemented the analyses. W.E.F., J.A.K., D.W. and D.E.B. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to William E. Feeney.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

Additional information

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

Extended data

Extended Data Fig. 1 Vocalization spectrograms.

Representative spectrograms of vocalizations of 26 species, grouped by family, included in the study produced in response to either predators or brood parasites. Top row, from left: brown thornbill (Acanthiza chrysorrhoa), fan-tailed gerygone (Gerygone flavolateralis), white-browed scrubwren (Sericornis frontalis), great reed warbler (Acrocephalus arundinaceus), oriental reed warbler (Acrocephalus orientalis), common tailorbird (Orthotomus sutorius), rufescent prinia (Prinia rufescens), tawny-flankd prinia (Prinia subflava), purple-crowned fairy-wren (Malurus coronatus), superb fairy-wren (Malurus cyaneus), variegated fairy-wren (Malurus lamberti), yellow-faced honeyeater (Caligavis chrysops), brown honeyeater (Lichmera indistincta), noisy friarbird (Philemon corniculatus). Bottom row, from left: chalk-browed mockingbird (Mimus saturninus), yellow warbler (Setophaga petechia), Hume’s leaf warbler (Phylloscopus humei), Ijima’s leaf warbler (Phylloscopus ijimae), western crowned warbler (Phylloscopus occipitalis), buff-barred warbler (Phylloscopus pulcher), mountain chiffchaff (Phylloscopus sindianus), greenish warbler (Phylloscopus trochiloides), willow warbler (Phylloscopus trochilus), Japanese leaf warbler (Phylloscopus xanthodryas), grey-hooded warbler (Phylloscopus xanthoschistos), grey fantail (Rhipidura albiscapa). Color above spectrograms denote vocalization type as in Fig. 1 in the main text. Green: alarm; blue: ‘whining’; yellow: ‘seet’; ‘red’: no unique vocalization.

Extended Data Fig. 2 Estimating the correlation between whining vocalization behaviour and linkage density.

Distribution of posterior means (N = 4,000 posterior samples per tree) obtained following MCMC sampling in a Bayesian threshold model to estimate the evolutionary correlation between the presence of the whining vocalization behaviour in a species and the median linkage density across the species’ range. MCMC sampling was conducted over 5,000,000 iterations with a thinning rate of 1000. The first 20% of iterations (1,000,000 iterations) was discarded as burn-in. The analysis was conducted using 1,000 randomly sampled trees based on the Hackett backbone, and 1,000 randomly sampled trees based on the Ericson backbone. Of these trees, 970 and 985 out of 1000 trees, respectively, had posterior distributions excluding zero.

Extended Data Fig. 3 Phylogeny of species that attended cuckoo model presentation trials or were detected in the vicinity of nests during point counts but did not attend the trial.

For each species it is denoted whether it is known to be a host species of a brood parasite, and whether the species belongs to the order Passeriformes (passerines) which includes the species most frequently parasitized by brood parasitic cuckoos and species known to produce whining vocalizations, or not (non-passerines).

Extended Data Fig. 4 Yellow warbler responses to playbacks.

Responses of yellow warblers (Setophaga petechia) towards 30 s playbacks of brood parasite-context whining vocalizations by superb fairy-wrens (Malurus cyaneus), conspecific predator-context chip, conspecific brood parasite-context seet vocalizations and wood thrush (Hylocichla mustelina) song, a non-threatening control (N = 14 for all treatments). Generalized linear mixed models were used (see Methods for more detail on statistical procedures). Boxes denote median and interquartile ranges, whiskers denote 1.5x interquartile range, and dots indicate data that lies outside of 1.5x interquartile range.

Supplementary information

Supplementary Information

Supplementary Methods and Results, Supplementary Tables 1–4 and Supplementary References.

Reporting Summary

Peer Review File

Supplementary Data

Zip file containing all data.

Supplementary Code

Feeneyetal_Code.Rmd.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feeney, W.E., Kennerley, J.A., Wheatcroft, D. et al. Learned use of an innate sound-meaning association in birds. Nat Ecol Evol (2025). https://doi.org/10.1038/s41559-025-02855-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41559-025-02855-9

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing