Quantifying similarity in animal vocal sequences : which metric performs best?
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
1. Many 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. 2. Here, we use both simulated and empirical datasets from animal vocal sequences (rock hyrax, Procavia capensis; humpback whale, Megaptera novaeangliae; bottlenose dolphin, Tursiops truncatus; and Carolina 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. 3. 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 and n-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 datasets, despite being less commonly used in the field of animal acoustic communication. 4. The Levenshtein 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 Markov chains, hidden Markov models, or Shannon 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.
Kershenbaum , A & Garland , E C 2015 , ' Quantifying similarity in animal vocal sequences : which metric performs best? ' , Methods in Ecology and Evolution , vol. 6 , no. 12 , pp. 1452-1461 . https://doi.org/10.1111/2041-210X.12433
Methods in Ecology and Evolution
© 2015 The Authors. Methods in Ecology and Evolution © 2015 British Ecological Society. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at: https://dx.doi.org/10.1111/2041-210X.12433
DescriptionE.C.G is supported by a Newton International Fellowship. Part of this work was conducted while E.C.G. was supported by a National Research Council (National Academy of Sciences) Postdoctoral Fellowship at the National Marine Mammal Laboratory, AFSC, NMFS, NOAA.
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.