DOI: 10.5061/dryad.hmgqnk9nj
title: African elephants address one another with individually specific, name-like calls

description: Personal names are a universal feature of human language, yet few analogs
 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. Labeling 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 analogs 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, likely 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 the first evidence for individual addressing of
 conspecifics in elephants. They further suggest that, unlike other
 nonhuman animals, elephants likely do not rely on imitation of the
 receiver’s calls to address one another.
description: Excel or similar spreadsheet program to open CSV files. R or
 RStudio to open and run code, and to open RDS file.
 Observational dataset (.csv file): Behavioral context information
 for vocalizations recorded from wild African elephants in Samburu and
 Buffalo Springs National Reserves, Kenya between November 2019 and April
 2022. Each row represents a single call, and information such as identity
 of the caller, identity of the addressee, and behavioral context, is
 provided. Information is also included linking each call to the original
 sound file and Raven Pro selection table from which it came (but the sound
 files and Raven Pro selection tables are not included in this
 archive). Acoustic measurements dataset (.rds file): R
 list object containing vectors of acoustic measurements for each call. The
 list is two-tiered; each outer slot corresponds to a single call, and each
 inner slot corresponds to an acoustic measurement, represented as a vector
 of numbers. R code (.R file): Code for aligning the
 observational dataset with the corresponding measurements from the .RDS
 file and performing all statistical analyses on these data. Also includes
 code for figures and code for calculating the acoustic measurements from
 the sound files (sound files not included in this archive).
 Combined dataset, spectral features (.csv file): Behavioral
 context information and derived spectral acoustic features. This
 spreadsheet can be created from the observational dataset and acoustic
 measurements dataset listed above using the code listed above. However,
 for convenience the reader may wish to skip sections 2-4 of the code and
 read this combined dataset into R in section 5 of the code.
 Combined dataset, cepstral features (.csv file): Behavioral
 context information and derived cepstral acoustic features. This
 spreadsheet can be created from the observational dataset and acoustic
 measurements dataset listed above using the code listed above. However,
 for convenience the reader may wish to skip sections 2-4 of the code and
 read this combined dataset into R in section 5 of the code.
 Playback experiment dataset (.csv file): results of playback
 experiment in which wild elephants were presented with two playbacks each:
 a call originally addressed to them (treatment), and a call from the same
 caller originally addressed to a different individual (control).
description: ## # African elephants address one another with individually specific
 calls We investigated the hypothesis that elephants address individual
 members of their family group with name-like calls. We recorded contact,
 greeting, and caregiving rumbles from wild African elephants in Samburu
 & Buffalo Springs National Reserves, northern Kenya and Amboseli
 National Park, southern Kenya, noting when possible the identity of the
 caller and the identity of the receiver. We measured a suite of acoustic
 features on each call and found that calls were specific to individual
 receivers and receiver identity could be predicted from call structure at
 better than chance levels. We also played back calls to the elephants and
 found that elephants responded more quickly and vocalized more in response
 to calls that were originally addressed to them compared to calls from the
 same caller that were originally addressed to someone else. These results
 provide the first evidence that elephants address one another with
 individually specific labels. ## Description of the data and file
 structure “20231225_observational_data_for_upload_revision1_n469.csv” This
 spreadsheet contains the associated observational behavioral data for each
 call used in the analysis. Each row represents a single call, and columns
 indicate the identity of the caller, the identity of the receiver, the
 behavioral context, the certainty with which each of these values were
 known, and other relevant information, including the file name of the
 sound file in which the call occurs, the start and end times of the call
 in that sound file, the file name of the Raven Pro selection table used to
 annotate the sound file, and the number of the selection box in that table
 corresponding to the call in question. The sound files and Raven selection
 tables themselves are not included in this upload. The full set of
 variables in this spreadsheet are defined in the spreadsheet named
 “20240123_variable_definitions_observational_data_for_upload_revision1.csv”. The derived acoustic features used in the analysis are not included in this spreadsheet. Rather, those measurements can be calculated using the code provided (“20240104_vocal_labeling_code_for_upload_revision1.R”) and the raw acoustic measurements in the .RDS file named “20231225_acoustic_contours_for_upload_revision1_n469.rds”. Values of “NA” in this spreadsheet indicate that the information in question was unknown for a given observation. “20240123_variable_definitions_observational_data_for_upload_revision1.xlsx” This spreadsheet defines each of the variables in “20231225_observational_data_for_upload_revision1_n469.csv” “20231225_acoustic_contours_for_upload_revision1_n469.rds” This RDS file is an R list object which contains the raw acoustic contours measured on each call. The code provided (“20240104_vocal_labeling_code_for_upload_revision1.R”) uses these raw acoustic contours to calculate a suite of derived acoustic features which are then used for subsequent statistical analyses. The list is nested with two layers. Each outer slot represents a single call, and is named with a unique identifier for that call, which is comprised of the date (YYYYMMDD) and time (HHMM) of the original recording as well as the Raven selection box number of the call in question. The inner slots represent individual acoustic contours, each of which is a vector of values. The acoustic contours are as follows: aenv = Hilbert amplitude envelope aenvtime = time points (sec) for aenv (not used in analysis) pmsband(1-26): energy in each of the 26 bands of a mel spectrogram, calculated from 0-500 Hz. Each energy value within a given band corresponds to one of the time points in aenvtime pmsD(1-26): differences between successive values of the corresponding pmsband pmsDD(1-26): differences between successive values of the corresponding pmsD cc(1-12): the first 12 cepstral coefficient contours, calculated from a mel spectrogram with 40 bands from 0-500 Hz. Each cepstral coefficient value in a given contour corresponds to a particular time point in aenvtime ccD(1-12): differences between successive values of the corresponding cc contour ccDD(1-12): differences between successive values of the corresponding ccD contour The features referred to in the paper and the R code as “spectral features” are derived from the following contours: aenv, pmsband(1-26), pmsD(1-26), and pmsDD(1-26) The features referred to in the paper and the R code as “cepstral features” are derived from the following contours: aenv, cc(1-12), ccD(1-12), and ccDD(1-12) “20231225_dfall_obs_data_and_spectral_features_revision1_n469.csv” This file is a spreadsheet containing the observational data (caller ID, receiver ID, etc.) together with the derived SPECTRAL acoustic features. This spreadsheet is generated in section 4 of the code using the data files “20231225_observational_data_for_upload_revision1_n469.csv” and “20231225_acoustic_contours_for_upload_revision1_n469.rds”. However, instead of recreating it de-novo the reader may skip sections 2-4 of the code and instead load this file in section 5 of the code. The variables in this spreadsheet are all described in the file “20240123_variable_definitions_observational_data_for_upload_revision1.xlsx”. Values of “NA” in this spreadsheet indicate that the information in question was unknown for a given observation. “20240106_dfall_obs_data_and_cepstral_features_revision1_n469.csv” This file is a spreadsheet containing the observational data (caller ID, receiver ID, etc.) together with the derived CEPSTRAL acoustic features. This spreadsheet is generated in section 4 of the code using the data files “20231225_observational_data_for_upload_revision1_n469.csv” and “20231225_acoustic_contours_for_upload_revision1_n469.rds”. However, instead of recreating it de-novo the reader may skip sections 2-4 of the code and instead load this file in section 5 of the code. The variables in this spreadsheet are all described in the file “20240123_variable_definitions_observational_data_for_upload_revision1.xlsx”. Values of “NA” in this spreadsheet indicate that the information in question was unknown for a given observation. “20250704_playback_data_for_upload_corrected.csv” This spreadsheet contains the results of the playback experiment. In this experiment, wild elephants were exposed to playbacks of a call originally addressed to them (test) and a call from the same caller originally addressed to someone else (control). We filmed the elephants’ responses and recorded from the video and audio recording the latency to approach the speaker within 10 min of the playback, the latency to vocalize within 10 min of the playback, the latency to vigilance within 10 min of the playback, the number of vocalizations produced within 10 min of the playback, and the amount of time spent being vigilant in 1 min following the playback minus the amount of time spent being vigilant in 1 min before the playback. The variables in this spreadsheet are all listed and defined in the spreadsheet named “20230409_variable_definitions_playback_data_for_upload.xlsx”. Values of “NA” in this spreadsheet indicate that the information in question could not be assessed for a given observation. “20230409_variable_definitions_playback_data_for_upload.xlsx” This spreadsheet defines each of the variables in “20230409_playback_data_for_upload.csv”. “20240104_vocal_labeling_code_for_upload_revision1.R” This R script contains the code for all analyses in this study. Each section of the code is labeled to describe what it does. This script replaces an earlier version of the script called “20230409_vocal_labeling_code_for_upload.R”. ## Code/Software “20240104_vocal_labeling_code_for_upload_revision1.R” This R script contains the code for all the analyses in this study. Each section of the code is labeled to describe what it does. The packages required for the code to run are all listed in the first section. The sections in this script are as follows: 1. Load the required packages for the whole script. 2. Code used to segment the calls from the original raw sound files and save them as clips for further analysis. The original sound files themselves are not included in this upload so this section cannot be run, but it is included to illustrate how we segmented the recordings 3. Code used to measure acoustic contours on the segmented sound clips. The sound clips are not included in this upload so this section can also not be run, but it is included to illustrate how we performed the acoustic contour measurements 4. Code to derive acoustic features from the acoustic contours and append them to the observational data. This section requires two files that are included in this upload: “20231225_observational_data_for_upload_revision1_n469.csv” and “20231225_acoustic_contours_for_upload_revision1_n469.rds”. We ran all of our analyses twice: once with the spectral features and once with the cepstral features (see above). Starting in this section of the script, where it is relevant we have included lines of code to allow the user to use either the spectral features or the cepstral features, with comments to indicate which is which. The code pertaining to cepstral features is currently commented out while the code pertaining to spectral features is not. To run the analysis with the cepstral features, the user should remove the # in front of the relevant lines of code for using the cepstral features, and comment out the corresponding lines for the spectral features. 5. Set the dataset that you wish to use for subsequent analyses (spectral features or cepstral features) 6. Code to calculate which calls belong to the same bout and the ranked position of each call within its bout. Bout defined as successive calls from the same caller in the same original raw recording with no more than 30 sec between successive calls. 7. Analysis to compare the mean similarity of different categories of call pairs. Similarity is measured using random forest proximity scores calculated with the algorithm devised by Rhodes, Cutler, and Moon 2022. We run two comparisons: pairs of calls with the same caller and same receiver vs. pairs of calls with the same caller and different receivers, and pairs of calls with different callers and the same receiver vs. pairs of calls with different callers and different receivers. 8. Code to add another variable to the dataset which indicates whether a given call was “imitative” of the receiver’s calls or “not imitative” of the receiver’s calls. This is determined by first calculating the mean proximity score between the focal call and each of the calls made by the receiver (meanElic1), and then calculating the mean proximity score between each of the calls made by the focal caller to someone other than the focal receiver and each of the calls made by the focal receiver (meanOther). The idea is to determine whether the focal call is more similar to the receiver’s calls than is typical for the focal caller. If meanElic1 &gt; meanOther, the focal call is deemed to be more similar to the receiver’s calls than typical for that caller, and is therefore considered “imitative”. Otherwise, it is considered “non-imitative”. 9. Analysis to determine if calls are more similar to the receiver’s calls than to calls made by other individuals. We compare pairs of calls in which the caller of one call was the receiver of the other call to pairs of calls in which this was not the case, while controlling for the identity of the two callers. 10. Random forest models using the ranger package to predict receiver ID as a function of the derived acoustic features. The models are run with 7-fold cross validation, and each 7-fold model is run 2000x and the classification accuracy is averaged across the 2000 runs. The first model stratifies the cross-validation folds by caller and receiver ID to ensure that the same number of calls from each caller-receiver dyad are included in each fold to the extent possible. This tests the hypothesis that receiver ID can be predicted from call structure within a given caller. The second model partitions the cross-validation folds such that all calls from the same caller-receiver dyad are always included in the same fold. This tests the hypothesis that receiver ID can be predicted from call structure across callers. 11. Run the same random forest models from section 10, but this time randomly permute the acoustic feature variables so they are uninformative. Run each model 10,000x instead of 2000x, and save the classification accuracy from each run in a data table. This data table will be used in the following section as a null distribution to test the hypothesis that the corresponding model from step 10 can predict receiver ID better than chance. 12. Calculate a p-value for the classification accuracy of each random forest model by comparing it to the null distribution generated in section 11 13. Run logistic regression model to assess the factors influencing the probability of a call’s receiver ID being correctly/incorrectly predicted in &gt;95% of runs of the first random forest model in section 10. Calls that were not correct in &gt;95% of runs or incorrect in &gt;95% of runs are excluded. 14. Calculate variable importance scores from two random forest models: one to predict receiver ID from acoustic features and one to predict caller ID from acoustic features. We use a conditional inference random forest in the package party rather than using ranger to obtain unbiased estimates of variable importance. This code generates a table of the variable importance scores for the derived acoustic features (Supplementary Table S2) and also calculates the relative importance of the original acoustic contours and puts them in a separate table (Supplementary Table S3. We only calculate variable importance scores for the spectral features because they are easier to interpret than the cepstral features. 15. Code to generate histograms and boxplots illustrating the results of the analyses in sections 7, 9, and 10 (Figs 1-3 in the main manuscript. 16. Code to generate figure illustrating how spectral acoustic contours were measured (Extended Data Fig. 1) 17. Code to generate multi-panel 3-D scatterplots showing similarity among calls addressed to same vs. different receivers within a given caller (Extended Data Fig. 2), and code to generate a single-panel scatterplot showing similarity among calls addressed to the same receiver across callers (Extended Data Fig. 3) 18. Analysis of playback results. Models to test the hypotheses that latency to approach, latency to vocalize, latency to vigilance, number of vocalizations, and time spent vigilant after playback minus before playback differ between test and control trials 19. Code to generate plots illustrating the results of the analyses in section 19 (Fig. 4 in main manuscript) 20. Code to calculate sample sizes for all the analyses in this script 21. Code to generate table of number of calls for each caller-receiver pair (Supplementary Table S1) ## Version changes 26-sept-2025: Replaced .csv file with playback data (20230409_playback_data_for_upload.csv) with updated version (20250704_playback_data_for_upload_corrected.csv) that corrects a few data entry mistakes in the latency to vocalize and number of vocalizations. Correcting these mistakes did not qualitatively change the results (significance is the same).