Acoustic sequences in non‐human animals: a tutorial review and prospectus |
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| Authors: | Arik Kershenbaum Daniel T. Blumstein Marie A. Roch Çağlar Akçay Gregory Backus Mark A. Bee Kirsten Bohn Yan Cao Gerald Carter Cristiane Cäsar Michael Coen Stacy L. DeRuiter Laurance Doyle Shimon Edelman Ramon Ferrer‐i‐Cancho Todd M. Freeberg Ellen C. Garland Morgan Gustison Heidi E. Harley Chloé Huetz Melissa Hughes Julia Hyland Bruno Amiyaal Ilany Dezhe Z. Jin Michael Johnson Chenghui Ju Jeremy Karnowski Bernard Lohr Marta B. Manser Brenda McCowan Eduardo Mercado III Peter M. Narins Alex Piel Megan Rice Roberta Salmi Kazutoshi Sasahara Laela Sayigh Yu Shiu Charles Taylor Edgar E. Vallejo Sara Waller Veronica Zamora‐Gutierrez |
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| Affiliation: | 1. National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN, U.S.A.;2. Department of Zoology, University of Cambridge, Cambridge, U.K.;3. Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, U.S.A.;4. Department of Computer Science, San Diego State University, San Diego, CA, U.S.A.;5. Lab of Ornithology, Cornell University, Ithaca, NY, U.S.A.;6. Department of Biomathematics, North Carolina State University, Raleigh, NC, U.S.A.;7. Department of Ecology, Evolution and Behavior, University of Minnesota, Falcon Heights, MN, U.S.A.;8. School of Integrated Science and Humanity, Florida International University, Miami, FL, U.S.A.;9. Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, U.S.A.;10. Biological Sciences Graduate Program, University of Maryland, College Park, MD, U.S.A.;11. Department of Psychology & Neuroscience, University of St. Andrews, St Andrews, U.K.;12. Department of Biostatistics and Medical Informatics, K6/446 Clinical Sciences Center, University of Wisconsin, Madison, WI, U.S.A.;13. Department of Biology School of Mathematics and Statistics, University of St. Andrews, St Andrews, U.K.;14. Carl Sagan Center for the Study of Life in the Universe, SETI Institute, Mountain View, CA, U.S.A.;15. Department of Psychology, Cornell University, Ithaca, NY, U.S.A.;16. Department of Computer Science, Universitat Politecnica de Catalunya (Catalonia), Barcelona, Spain;17. Department of Psychology, University of Tennessee, Knoxville, TN, U.S.A.;18. National Marine Mammal Laboratory, AFSC/NOAA, Seattle, WA, U.S.A.;19. Department of Psychology, University of Michigan, Ann Arbor, MI, U.S.A.;20. Division of Social Sciences, New College of Florida, Sarasota, FL, U.S.A.;21. CNPS, CNRS UMR 8195, Université Paris‐Sud, UMR 8195, Orsay, France;22. Department of Biology, College of Charleston, Charleston, SC, U.S.A.;23. Department of Psychology, Hunter College and the Graduate Center, The City University of New York, New York, NY, U.S.A.;24. Department of Physics, Pennsylvania State University, University Park, PA, U.S.A.;25. Department of Electrical and Computer Engineering, Marquette University, Milwaukee, WI, U.S.A.;26. Department of Biology, Queen College, The City University of New York, Flushing, NY, U.S.A.;27. Department of Cognitive Science, University of California San Diego, La Jolla, CA, U.S.A.;28. Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, U.S.A.;29. Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland;30. Department of Veterinary Medicine, University of California Davis, Davis, CA, U.S.A.;31. Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, U.S.A.;32. Department of Evolution, Ecology, & Behavior, University at Buffalo, The State University of New York, Buffalo, NY, U.S.A.;33. Department of Integrative Biology & Physiology, University of California Los Angeles, Los Angeles, CA, U.S.A.;34. Division of Biological Anthropology, University of Cambridge, Cambridge, U.K.;35. Department of Psychology, California State University San Marcos, San Marcos, CA, U.S.A.;36. Department of Anthropology, University of Georgia at Athens, Athens, GA, U.S.A.;37. Department of Complex Systems Science, Graduate School of Information Science, Nagoya University, Nagoya, Japan;38. Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, U.S.A.;39. Department of Computer Science, Monterrey Institute of Technology, Monterrey, Nuevo León, Mexico;40. Department of Philosophy, Montana State University, Bozeman, MT, U.S.A.;41. Centre for Biodiversity and Environment Research, University College London, London, U.K. |
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| Abstract: | Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well‐known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise – let alone understand – the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near‐future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, ‘Analysing vocal sequences in animals’. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial‐style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality. |
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| Keywords: | acoustic communication information information theory machine learning Markov model meaning network analysis sequence analysis vocalisation |
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