Quantifying similarity in animal vocal sequences: which metric performs best?

Abstract

SummaryMany 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.Here, we use both simulated and empirical data sets from animal vocal sequences (rock hyrax,Procavia capensis; humpback whale,Megaptera novaeangliae; bottlenose dolphin,Tursiops truncatus; andCarolina chickadee,Poecile carolinensis) 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.Results from the simulated data indicated that multiple metrics were equally successful in reconstructing the information encoded in the sequences of simulated individuals (Markov chains,n‐gram models, repeat distribution and edit distance) and data generated by different stochastic processes (entropy rate andn‐grams). However, the string edit (Levenshtein) distance performed consistently and significantly better than all other tested metrics (including entropy,Markov chains,n‐grams, mutual information) for all empirical data sets, despite being less commonly used in the field of animal acoustic communication.TheLevenshtein 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 asMarkov chains, hiddenMarkov models orShannon entropy). The recent discovery that non‐Markovian vocal sequences may be more common in animal communication than previously thought provides a rich area for future research that requires non‐Markovian‐based analysis techniques to investigate animal grammars and potentially the origin of human language.