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

• Published in: Methods in Ecology and Evolution
• Main Authors: Kershenbaum, Arik, Garland, Ellen Clare
• Other Authors: The Royal Society, University of St Andrews. School of Biology, University of St Andrews. Sea Mammal Research Unit, University of St Andrews. Centre for Social Learning & Cognitive Evolution, University of St Andrews. Centre for Biological Diversity
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2016
• Subjects: Sequence; Animal communication; Vocal; Edit distance; Markov; Stochastic processes; QH301 Biology; QH301; Humpback Whale; Megaptera novaeangliae
• Online Access: https://hdl.handle.net/10023/9266; https://doi.org/10.1111/2041-210X.12433; http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12433/suppinfo

E.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. 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 ...