02257nas a2200121   4500008004100000245008500041210006900126520181300195100002402008700002502032700002502057856005302082       2020                        eng d00aComparison of methods for rhythm analysis of complex animals’ acoustic signals0 aComparison of methods for rhythm analysis of complex animals aco3 a<p>Analyzing the rhythm of animals&rsquo; acoustic signals is of interest to a growing number of researchers: evolutionary biologists want to disentangle how these structures evolved and what patterns can be found, and ecologists and conservation biologists aim to discriminate cryptic species on the basis of parameters of acoustic signals such as temporal structures. Temporal structures are also relevant for research on vocal production learning, a part of which is for the animal to learn a temporal structure. These structures, in other words, these rhythms, are the topic of this paper. How can they be investigated in a meaningful, comparable and universal way? Several approaches exist. Here we used five methods to compare their suitability and interpretability for different questions and datasets and test how they support the reproducibility of results and bypass biases. Three very different datasets with regards to recording situation, length and context were analyzed: two social vocalizations of Neotropical bats (multisyllabic, medium long isolation calls of Saccopteryx bilineata, and monosyllabic, very short isolation calls of Carollia perspicillata) and click trains of sperm whales, Physeter macrocephalus. Techniques to be compared included Fourier analysis with a newly developed goodness-of-fit value, a generate-and-test approach where data was overlaid with varying artificial beats, and the analysis of inter-onset-intervals and calculations of a normalized Pairwise Variability Index (nPVI). We discuss the advantages and disadvantages of the methods and we also show suggestions on how to best visualize rhythm analysis results. Furthermore, we developed a decision tree that will enable researchers to select a suitable and comparable method on the basis of their data.</p>
1 aBurchardt, Lara, S.1 aKnörnschild, Mirjam1 aGershman, Samuel, J.  uhttps://dx.plos.org/10.1371/journal.pcbi.100775502216nas a2200253   4500008004100000022001300041245010000054210006900154260001600223300001200239490000800251520142800259100002101687700002201708700002501730700002401755700002301779700002101802700002601823700002201849700002501871700002501896856004101921       2019                        eng d  a0896627300aA Modular Approach to Vocal Learning: Disentangling the Diversity of a Complex Behavioral Trait0 aModular Approach to Vocal Learning Disentangling the Diversity o  cJan-10-2019  a87 - 990 v1043 a<p>Vocal learning is a behavioral trait in which the social and acoustic environment shapes the vocal repertoire of individuals. Over the past century, the study of vocal learning has progressed at the intersection of ecology, physiology, neuroscience, molecular biology, genomics, and evolution. Yet, despite the complexity of this trait, vocal learning is frequently described as a binary trait, with species being classified as either vocal learners or vocal non-learners. As a result, studies have largely focused on a handful of species for which strong evidence for vocal learning exists. Recent studies, however, suggest a continuum in vocal learning capacity across taxa. Here, we further suggest that vocal learning is a multi-component behavioral phenotype comprised of distinct yet interconnected modules. Discretizing the vocal learning phenotype into its constituent modules would facilitate integration of findings across a wider diversity of species, taking advantage of the ways in which each excels in a particular module, or in a specific combination of features. Such comparative studies can improve understanding of the mechanisms and evolutionary origins of vocal learning. We propose an initial set of vocal learning modules supported by behavioral and neurobiological data and highlight the need for diversifying the field in order to disentangle the complexity of the vocal learning phenotype.</p>
1 aWirthlin, Morgan1 aChang, Edward, F.1 aKnörnschild, Mirjam1 aKrubitzer, Leah, A.1 aMello, Claudio, V.1 aMiller, Cory, T.1 aPfenning, Andreas, R.1 aVernes, Sonja, C.1 aTchernichovski, Ofer1 aYartsev, Michael, M.  uhttps://bio.acousti.ca/it/node/57585