Quantifying similarity in animal vocal sequences:which metric performs best?
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 o...
Published in: | Methods in Ecology and Evolution |
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Online Access: | https://risweb.st-andrews.ac.uk/portal/en/researchoutput/quantifying-similarity-in-animal-vocal-sequences(1ba9eb03-f810-4194-b759-5219a42d9bc7).html https://doi.org/10.1111/2041-210X.12433 https://research-repository.st-andrews.ac.uk/bitstream/10023/9266/1/MEE_acceptedmanuscript.pdf http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12433/suppinfo |
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ftunstandrewcris:oai:risweb.st-andrews.ac.uk:publications/1ba9eb03-f810-4194-b759-5219a42d9bc7 2023-05-15T16:36:08+02:00 Quantifying similarity in animal vocal sequences:which metric performs best? Kershenbaum, Arik Garland, Ellen Clare 2015-12 application/pdf https://risweb.st-andrews.ac.uk/portal/en/researchoutput/quantifying-similarity-in-animal-vocal-sequences(1ba9eb03-f810-4194-b759-5219a42d9bc7).html https://doi.org/10.1111/2041-210X.12433 https://research-repository.st-andrews.ac.uk/bitstream/10023/9266/1/MEE_acceptedmanuscript.pdf http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12433/suppinfo eng eng info:eu-repo/semantics/openAccess 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 Sequence Animal communication Vocal Edit distance Markov Stochastic processes article 2015 ftunstandrewcris https://doi.org/10.1111/2041-210X.12433 2022-06-02T07:44:56Z 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 ... Article in Journal/Newspaper Humpback Whale Megaptera novaeangliae University of St Andrews: Research Portal Methods in Ecology and Evolution 6 12 1452 1461 |
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Open Polar |
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University of St Andrews: Research Portal |
op_collection_id |
ftunstandrewcris |
language |
English |
topic |
Sequence Animal communication Vocal Edit distance Markov Stochastic processes |
spellingShingle |
Sequence Animal communication Vocal Edit distance Markov Stochastic processes Kershenbaum, Arik Garland, Ellen Clare Quantifying similarity in animal vocal sequences:which metric performs best? |
topic_facet |
Sequence Animal communication Vocal Edit distance Markov Stochastic processes |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
Kershenbaum, Arik Garland, Ellen Clare |
author_facet |
Kershenbaum, Arik Garland, Ellen Clare |
author_sort |
Kershenbaum, Arik |
title |
Quantifying similarity in animal vocal sequences:which metric performs best? |
title_short |
Quantifying similarity in animal vocal sequences:which metric performs best? |
title_full |
Quantifying similarity in animal vocal sequences:which metric performs best? |
title_fullStr |
Quantifying similarity in animal vocal sequences:which metric performs best? |
title_full_unstemmed |
Quantifying similarity in animal vocal sequences:which metric performs best? |
title_sort |
quantifying similarity in animal vocal sequences:which metric performs best? |
publishDate |
2015 |
url |
https://risweb.st-andrews.ac.uk/portal/en/researchoutput/quantifying-similarity-in-animal-vocal-sequences(1ba9eb03-f810-4194-b759-5219a42d9bc7).html https://doi.org/10.1111/2041-210X.12433 https://research-repository.st-andrews.ac.uk/bitstream/10023/9266/1/MEE_acceptedmanuscript.pdf http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12433/suppinfo |
genre |
Humpback Whale Megaptera novaeangliae |
genre_facet |
Humpback Whale Megaptera novaeangliae |
op_source |
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 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1111/2041-210X.12433 |
container_title |
Methods in Ecology and Evolution |
container_volume |
6 |
container_issue |
12 |
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1452 |
op_container_end_page |
1461 |
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1766026443297390592 |