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

• Published in: Methods in Ecology and Evolution
• Main Authors: Kershenbaum, Arik, Garland, Ellen C.
• Other Authors: Nakagawa, Shinichi, American Friends Service Committee, National Oceanic and Atmospheric Administration, National Institute for Mathematical and Biological Synthesis, National Science Foundation
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2015
• Subjects: Humpback Whale
• Online Access: http://dx.doi.org/10.1111/2041-210x.12433; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F2041-210X.12433; https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12433; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/2041-210X.12433; https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12433

Summary 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. Here, we use both simulated and empirical data sets from animal vocal sequences (rock hyrax, P rocavia capensis humpback whale, M egaptera novaeangliae bottlenose dolphin, T ursiops truncatus and C arolina chickadee, P oecile 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 ( M arkov 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 ( L evenshtein) distance performed consistently and significantly better than all other tested metrics (including entropy, M arkov chains, n ‐grams, mutual information) for all empirical data sets, despite being less commonly used in the field of animal acoustic communication. The L 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 M arkov chains, hidden M arkov models or S hannon entropy). The recent discovery that non‐ M arkovian vocal sequences may be more common in animal communication than ...