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

Nature Ecology & Evolution volume 8, pages 1353–1364 (2024)Cite this article

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An Author Correction to this article was published on 17 July 2025

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Abstract

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 https://doi.org/10.5061/dryad.hmgqnk9nj (ref. 60).

Code availability

Code is available at https://doi.org/10.5281/zenodo.10576772 (ref. 61).

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Acknowledgements

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.

Author information

Authors and Affiliations

  1. Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA

    Michael A. Pardo & George Wittemyer

  2. Department of Electronic and Computer Engineering, Colorado State University, Fort Collins, CO, USA

    Kurt Fristrup

  3. Save The Elephants, Nairobi, Kenya

    David S. Lolchuragi, Iain Douglas-Hamilton & George Wittemyer

  4. ElephantVoices, Sandefjord, Norway

    Joyce H. Poole & Petter Granli

  5. Amboseli Elephant Research Project, Nairobi, Kenya

    Cynthia Moss

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  1. Michael A. Pardo
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Contributions

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.

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

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Supplementary Information

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 8, 1353–1364 (2024). https://doi.org/10.1038/s41559-024-02420-w

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