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African elephants address one another with individually specific name-like calls


Personal names are a universal feature of human language, yet few analogues exist in other species. While dolphins and parrots address conspecifics by imitating the calls of the addressee, human names are not imitations of the sounds typically made by the named individual. Labelling objects or individuals without relying on imitation of the sounds made by the referent radically expands the expressive power of language. Thus, if non-imitative name analogues were found in other species, this could have important implications for our understanding of language evolution. Here we present evidence that wild African elephants address one another with individually specific calls, probably without relying on imitation of the receiver. We used machine learning to demonstrate that the receiver of a call could be predicted from the call’s acoustic structure, regardless of how similar the call was to the receiver’s vocalizations. Moreover, elephants differentially responded to playbacks of calls originally addressed to them relative to calls addressed to a different individual. Our findings offer evidence for individual addressing of conspecifics in elephants. They further suggest that, unlike other non-human animals, elephants probably do not rely on imitation of the receiver’s calls to address one another.

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Fig. 1: Evidence that calls are specific to individual receivers within a caller.
Fig. 2: Evidence that vocal labelling probably did not rely on imitation of the receiver’s calls.
Fig. 3: Mixed evidence that different callers use similar labels for the same receiver.
Fig. 4: Response to playbacks of test stimuli (calls originally addressed to the subject) versus control stimuli (calls from the same caller originally addressed to a different individual).

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Data availability

Data are available at (ref. 60).

Code availability

Code is available at (ref. 61).


  1. Fitch, W. T. The evolution of language: a comparative review. Biol. Philos. 20, 193–230 (2005).

    Article  Google Scholar 

  2. Macedonia, J. M. & Evans, C. S. Variation among mammalian alarm call systems and the problem of meaning in animal signals. Ethology 93, 177–197 (1993).

    Article  Google Scholar 

  3. Clay, Z., Smith, C. L. & Blumstein, D. T. Food-associated vocalizations in mammals and birds: what do these calls really mean? Anim. Behav. 83, 323–330 (2012).

    Article  Google Scholar 

  4. 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 

  5. Smith, E. A. Communication and collective action: language and the evolution of human cooperation. Evol. Hum. Behav. 31, 231–245 (2010).

    Article  Google Scholar 

  6. Dingemanse, M., Blasi, D. E., Lupyan, G., Christiansen, M. H. & Monaghan, P. Arbitrariness, iconicity, and systematicity in language. Trends Cogn. Sci. 19, 603–615 (2015).

    Article  PubMed  Google Scholar 

  7. 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  CAS  PubMed  PubMed Central  Google Scholar 

  8. Balsby, T. J. S., Momberg, J. V. & Dabelsteen, T. Vocal imitation in parrots allows addressing of specific individuals in a dynamic communication network. PLoS ONE 7, e49747 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Janik, V. M. & Sayigh, L. S. Communication in bottlenose dolphins: 50 years of signature whistle research. J. Comp. Physiol. A 199, 479–489 (2013).

    Article  Google Scholar 

  10. Poole, J. H., Tyack, P. L., Stoeger-Horwath, A. S. & Watwood, S. Elephants are capable of vocal learning. Nature 434, 455–456 (2005).

    Article  CAS  PubMed  Google Scholar 

  11. Stoeger, A. S. et al. An Asian elephant imitates human speech. Curr. Biol. 22, 2144–2148 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Soltis, J., Leong, K. & Savage, A. African elephant vocal communication II: rumble variation reflects the individual identity and emotional state of callers. Anim. Behav. 70, 589–599 (2005).

    Article  Google Scholar 

  13. Clemins, P. J., Johnson, M. T., Leong, K. M. & Savage, A. Automatic classification and speaker identification of African elephant (Loxodonta africana) vocalizations. J. Acoust. Soc. Am. 117, 956–963 (2005).

    Article  PubMed  Google Scholar 

  14. McComb, K., Moss, C., Sayialel, S. & Baker, L. Unusually extensive networks of vocal recognition in African elephants. Anim. Behav. 59, 1103–1109 (2000).

    Article  CAS  PubMed  Google Scholar 

  15. Poole, J. H. in The Amboseli Elephants: A Long-Term Perspective on a Long-Lived Mammal (eds Moss, C. J. et al.) 125–159 (Univ. Chicago Press, 2011).

  16. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  17. Rhodes, J. S., Cutler, A. & Moon, K. R. Geometry- and accuracy-preserving random forest proximities. IEEE Trans. Pattern Anal. Mach. Intell. 45, 10947–10959 (2023).

    Article  PubMed  Google Scholar 

  18. Foley, N. M. et al. A genomic timescale for placental mammal evolution. Science 380, eabl8189 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Dahlin, C. R., Young, A. M., Cordier, B., Mundry, R. & Wright, T. F. A test of multiple hypotheses for the function of call sharing in female budgerigars, Melopsittacus undulatus. Behav. Ecol. Sociobiol. 68, 145–161 (2014).

    Article  PubMed  Google Scholar 

  20. Wanker, R., Sugama, Y. & Prinage, S. Vocal labelling of family members in spectacled parrotlets, Forpus conspicillatus. Anim. Behav. 70, 111–118 (2005).

    Article  Google Scholar 

  21. Prat, Y., Taub, M. & Yovel, Y. Everyday bat vocalizations contain information about emitter, addressee, context, and behavior. Sci. Rep. 6, 39419 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wittemyer, G., Douglas-Hamilton, I. & Getz, W. M. The socioecology of elephants: analysis of the processes creating multitiered social structures. Anim. Behav. 69, 1357–1371 (2005).

    Article  Google Scholar 

  23. Archie, E. A., Moss, C. J. & Alberts, S. C. The ties that bind: genetic relatedness predicts the fission and fusion of social groups in wild African elephants. Proc. R. Soc. B 273, 513–522 (2006).

    Article  CAS  PubMed  Google Scholar 

  24. Howard, D. J., Gengler, C. & Jain, A. What’s in a name? A complimentary means of persuasion. J. Consum. Res. 22, 200–211 (1995).

    Article  Google Scholar 

  25. King, S. L., Sayigh, L. S., Wells, R. S., Fellner, W. & Janik, V. M. Vocal copying of individually distinctive signature whistles in bottlenose dolphins. Proc. R. Soc. B 280, 20130053 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Baotic, A. & Stoeger, A. S. Sexual dimorphism in African elephant social rumbles. PLoS ONE 12, e0177411 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Stoeger, A. S., Zeppelzauer, M. & Baotic, A. Age-group estimation in free-ranging African elephants based on acoustic cues of low-frequency rumbles. Bioacoustics 23, 231–246 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zaman, S. R., Sadekeen, D., Alfaz, M. A. & Shahriyar, R. One source to detect them all: gender, age, and emotion detection from voice. In Proc. IEEE 45th Annual Computers, Software, and Applications Conference 338–343 (IEEE, 2021).

  29. Berg, K. S., Delgado, S., Cortopassi, K. A., Beissinger, S. R. & Bradbury, J. W. Vertical transmission of learned signatures in a wild parrot. Proc. R. Soc. B 279, 585–591 (2012).

    Article  PubMed  Google Scholar 

  30. Stevens, S. S., Volkmann, J. & Newman, E. B. A scale for the measurement of the psychological magnitude pitch. J. Acoust. Soc. Am. 8, 185–190 (1937).

    Article  Google Scholar 

  31. Vernes, S. C. et al. The multi-dimensional nature of vocal learning. Philos. Trans. R. Soc. B 376, 20200236 (2021).

    Article  Google Scholar 

  32. Bradbury, J. W. & Balsby, T. J. S. The functions of vocal learning in parrots. Behav. Ecol. Sociobiol. 70, 293–312 (2016).

    Article  Google Scholar 

  33. Connor, R. C. Dolphin social intelligence: complex alliance relationships in bottlenose dolphins and a consideration of selective environments for extreme brain size evolution in mammals. Philos. Trans. R. Soc. Lond. B 362, 587–602 (2007).

    Article  Google Scholar 

  34. Bachorec, E. et al. Spatial networks differ when food supply changes: foraging strategy of Egyptian fruit bats. PLoS ONE 15, e0229110 (2020).

  35. Kerth, G., Perony, N. & Schweitzer, F. Bats are able to maintain long-term social relationships despite the high fission–fusion dynamics of their groups. Proc. R. Soc. B 278, 2761–2767 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Moss, C. J. & Poole, J. H. in Primate Social Relationships: An Integrated Approach (ed. Hinde, R. A.) 315–325 (Blackwell Science, 1983).

  37. Altmann, J. Observational study of behavior: sampling methods. Behaviour 49, 227–267 (1974).

    Article  CAS  PubMed  Google Scholar 

  38. de Silva, S. Acoustic communication in the Asian elephant, Elephas maximus maximus. Behaviour 147, 825–852 (2010).

    Article  Google Scholar 

  39. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing (2022).

  40. Sueur, J., Aubin, T. & Simonis, C. seewave, a free modular tool for sound analysis. Bioacoustics 18, 213–226 (2008).

    Article  Google Scholar 

  41. Ligges, U., Krey, S., Mersmann, O. & Schnackenberg, S. tuneR: analysis of music and speech. R Project (2018).

  42. Anikin, A. Soundgen: an open-source tool for synthesizing nonverbal vocalizations. Behav. Res. Methods 51, 778–792 (2019).

    Article  PubMed  Google Scholar 

  43. Heffner, R. S. & Heffner, H. E. Hearing in the elephant (Elephas maximus): absolute sensitivity, frequency discrimination, and sound localization. J. Comp. Physiol. Psychol. 96, 926–944 (1982).

    Article  CAS  PubMed  Google Scholar 

  44. Ren, Y. et al. A framework for bioacoustic vocalization analysis using hidden Markov models. Algorithms 2, 1410–1428 (2009).

    Article  Google Scholar 

  45. Davis, S. B. & Mermelstein, P. Comparison of parametric representations for monosyllabic word recognition. IEEE Trans. Acoust. 28, 357–366 (1980).

    Article  Google Scholar 

  46. Sykulsi, M. rpca: RobustPCA: decompose a matrix into low-rank and sparse components. R package version 0.2.3. R Project (2015).

  47. Thomson, D. J. Spectrum estimation and harmonic analysis. Proc. IEEE 70, 1055–1096 (1982).

    Article  Google Scholar 

  48. Correll, J., Mellinger, C. & Pedersen, E. J. Flexible approaches for estimating partial eta squared in mixed-effects models with crossed random factors. Behav. Res. Methods 54, 1626–1642 (2022).

    Article  PubMed  Google Scholar 

  49. Wright, M. N. & Ziegler, A. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77, 1–17 (2017).

  50. Wittemyer, G. & Getz, W. M. Hierarchical dominance structure and social organization in African elephants, Loxodonta africana. Anim. Behav. 73, 671–681 (2007).

    Article  Google Scholar 

  51. Archie, E. A., Morrison, T. A., Foley, C. A. H., Moss, C. J. & Alberts, S. C. Dominance rank relationships among wild female African elephants, Loxodonta africana. Anim. Behav. 71, 117–127 (2006).

    Article  Google Scholar 

  52. Archie, E. A., Moss, C. J. & Alberts, S. C. in The Amboseli Elephants: A Long-Term Perspective on a Long-Lived Mammal (eds Moss, C. J. et al.) 238–245 (Univ. Chicago Press, 2011).

  53. Blanca, M. J., Alarcón, R., Arnau, J., Bono, R. & Bendayan, R. Non-normal data: is ANOVA still a valid option? Psicothema 29, 552–557 (2017).

    PubMed  Google Scholar 

  54. Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T. & Zeileis, A. Conditional variable importance for random forests. BMC Bioinform. 9, 307 (2008).

  55. Poole, J. H., Payne, K., Langbauer, W. R. J. & Moss, C. J. The social contexts of some very low-frequency calls of African elephants. Behav. Ecol. Sociobiol. 22, 385–392 (1988).

    Article  Google Scholar 

  56. Poole, J. H. & Granli, P. in The Amboseli Elephants: A Long-Term Perspective on a Long-Lived Mammal (eds Moss, C. J. et al.) 109–124 (Univ. Chicago Press, 2011).

  57. Therneau, T. M. coxme: mixed effects cox models. R package version 2.2-18.1. R Project (2019).

  58. Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  59. Kleiber, C. & Zeileis, A. Applied Econometrics with R (Springer, 2008).

  60. Pardo, M. African elephants address one another with individually specific calls. Dryad (2024).

  61. Pardo, M. African elephants address one another with individually specific calls. Zenodo (2024).

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We thank the Office of the President of Kenya, the Samburu, Isiolo and Kajiado County governments, the Wildlife Research & Training Institute of Kenya, and Kenya Wildlife Service for permission to conduct fieldwork in Kenya. We thank Save The Elephants and the Amboseli Trust for Elephants for logistical support in the field, J. M. Leshudukule, D. M. Letitiya and N. Njiraini for assistance with the fieldwork, G. Pardo for blinding the playback stimuli and S. Pardo for input on the statistical analyses. We thank J. Berger, W. Koenig and A. Horn for comments on the manuscript. This project was funded by a Postdoctoral Research Fellowship in Biology to M.A.P. from the National Science Foundation (award no. 1907122) and grants to J.H.P. and P.G. from the National Geographic Society, Care for the Wild, and the Crystal Springs Foundation. Fieldwork was supported by Save the Elephants.

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Authors and Affiliations



M.A.P. conceived the study. M.A.P. and D.S.L. collected the data in Samburu, and J.H.P. and P.G. collected the data in Amboseli. M.A.P. and K.F. performed the statistical analysis, and M.A.P. created the figures. M.A.P. drafted the manuscript, and K.F., J.H.P. and G.W. edited it. C.M., I.D.-H. and G.W. provided resources and access to long-term datasets, and G.W. supervised the study.

Corresponding author

Correspondence to Michael A. Pardo.

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

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Extended data

Extended Data Fig. 1 Schematic illustrating how spectral acoustic features were measured.

First, a spectrogram was calculated by applying a Fast Fourier Transform to the signal (Hamming window, 700 samples, 90% overlap). Then a mel filter bank with 26 overlapping triangular filters between 0-500 Hz was applied to each window of the spectrogram to produce a mel spectrogram. The mel spectrogram was then normalized by dividing the energy value in each cell by the total energy in that time window and these proportional energies were logit-transformed so they would not be limited to between 0 and 1. As features for the robust principal components analysis, we used the vector of energy in each of the 26 mel frequency bands as well as the vectors of delta and delta-delta values for each frequency band (representing the change and acceleration in energy over time, respectively). In the spectrogram and mel spectrogram in this figure, warmer colors indicate higher amplitudes (greater energy).

Extended Data Fig. 2 Scatterplots illustrating the separation in 3D space between calls from the same caller to different receivers.

Axes are the first three principal coordinates extracted from the proximity scores of a random forest trained to predict receiver ID. Each plot represents a single caller, each point is a single call, and receiver IDs are coded by both color and shape. This figure only includes calls where caller ID was known for certain, where the call was predicted correctly in at least 25% of random forest iterations, and where the caller made at least two such calls each to at least two different receivers.

Extended Data Fig. 3 Scatterplot illustrating the clustering in 3D space of calls from different callers to the same receiver.

Axes are the first three principal coordinates extracted from the proximity scores of a random forest trained to predict receiver ID. Each shape represents a different receiver and each color represents a different caller. This figure only includes calls where caller ID was known for certain, where the call was predicted correctly in at least 25% of random forest iterations, and where the receiver received at least one such call each from at least two different callers.

Extended Data Table 1 Acoustic features used in the random forest models
Extended Data Table 2 Results of random forest models predicting receiver ID as a function of the acoustic features
Extended Data Table 3 Definitions of social relationship categories between caller and receiver
Extended Data Table 4 Results for linear mixed model assessing whether calls are specific to individual receivers or the type of relationship between caller and receiver
Extended Data Table 5 Results for mixed effects logistic regression modeling the probability of a call being correctly classified
Extended Data Table 6 Results for linear mixed model assessing whether calls addressed to a receiver imitate the receiver’s calls
Extended Data Table 7 Results for linear mixed model assessing whether different callers use similar labels for same receiver

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Supplementary Discussion and Tables 1–4.

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Pardo, M.A., Fristrup, K., Lolchuragi, D.S. et al. African elephants address one another with individually specific name-like calls. Nat Ecol Evol (2024).

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