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).Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: Funding provided by: National Geographic SocietyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006363Award Number: Funding provided by: Care for the Wild*Crossref Funder Registry ID: Award Number: Funding provided by: Crystal Springs Foundation*Crossref Funder Registry ID: Award Number:

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 that object or individual 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 without imitation. We used machine learning to demonstrate that the receiver of a call could be predicted from the call's acoustic structure and found little evidence that calls addressed to a given receiver imitate the receiver's own calls. Moreover, elephants differentially responded to playbacks of calls originally addressed to them relative to calls addressed to a different individual, indicating that they can determine from a call's structure if it was addressed to them. Our findings offer the first evidence for a non-human species individually addressing conspecifics without imitating the receiver.