jats:titleSummary</jats:title>jats:pjats:listjats:list-itemjats:pMany animals communicate using sequences of discrete acoustic elements which can be complex, vary in their degree of stereotypy, and are potentially open‐ended. Variation in sequences can provide important ecological, behavioural or evolutionary information about the structure and connectivity of populations, mechanisms for vocal cultural evolution and the underlying drivers responsible for these processes. Various mathematical techniques have been used to form a realistic approximation of sequence similarity for such tasks.</jats:p></jats:list-item>jats:list-itemjats:pHere, we use both simulated and empirical data sets from animal vocal sequences (rock hyrax,jats:italic<jats:styled-content style="fixed-case">P</jats:styled-content>rocavia capensis</jats:italic>; humpback whale,jats:italic<jats:styled-content style="fixed-case">M</jats:styled-content>egaptera novaeangliae</jats:italic>; bottlenose dolphin,jats:italic<jats:styled-content style="fixed-case">T</jats:styled-content>ursiops truncatus</jats:italic>; and<jats:styled-content style="fixed-case">C</jats:styled-content>arolina chickadee,jats:italic<jats:styled-content style="fixed-case">P</jats:styled-content>oecile carolinensis</jats:italic>) to test which of eight sequence analysis metrics are more likely to reconstruct the information encoded in the sequences, and to test the fidelity of estimation of model parameters, when the sequences are assumed to conform to particular statistical models.</jats:p></jats:list-item>jats:list-itemjats:pResults from the simulated data indicated that multiple metrics were equally successful in reconstructing the information encoded in the sequences of simulated individuals (<jats:styled-content style="fixed-case">M</jats:styled-content>arkov chains,jats:italicn</jats:italic>‐gram models, repeat distribution and edit distance) and data generated by different stochastic processes (entropy rate andjats:italicn</jats:italic>‐grams). However, the string edit (<jats:styled-content style="fixed-case">L</jats:styled-content>evenshtein) distance performed consistently and significantly better than all other tested metrics (including entropy,<jats:styled-content style="fixed-case">M</jats:styled-content>arkov chains,jats:italicn</jats:italic>‐grams, mutual information) for all empirical data sets, despite being less commonly used in the field of animal acoustic communication.</jats:p></jats:list-item>jats:list-itemjats:pThe<jats:styled-content style="fixed-case">L</jats:styled-content>evenshtein distance metric provides a robust analytical approach that should be considered in the comparison of animal acoustic sequences in preference to other commonly employed techniques (such as<jats:styled-content style="fixed-case">M</jats:styled-content>arkov chains, hidden<jats:styled-content style="fixed-case">M</jats:styled-content>arkov models or<jats:styled-content style="fixed-case">S</jats:styled-content>hannon entropy). The recent discovery that non‐<jats:styled-content style="fixed-case">M</jats:styled-content>arkovian vocal sequences may be more common in animal communication than previously thought provides a rich area for future research that requires non‐<jats:styled-content style="fixed-case">M</jats:styled-content>arkovian‐based analysis techniques to investigate animal grammars and potentially the origin of human language.</jats:p></jats:list-item></jats:list></jats:p>