Comparison of methods for rhythm analysis of complex animals’ acoustic signals

Abstract

Analyzing the rhythm of animals' 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. Author summaryIn the analysis of animal communication more and more interest is shown in rhythm of animal communication and what information this might convey. In this paper, we establish a workflow to analyze the temporal structure-namely the rhythm-of any particular animals'acoustic signal with methods that are applicable for a wide range of signals and results that are easily comparable and interpretable. This workflow will enhance the understanding of rhythmicality in animals' acoustic signals as well as facilitate comparison between species. Methods we conducted ranged from simple distributional and visual PLOS COMPUTATIONAL BIOLOGY PLOS Computational Biology | https://doi.

Assessing acoustic competition between sibling frog species using rhythm analysis

“…Using clear and comparable workflows, incorporating multiple variability parameters, and calculating the exact beats describing a sequence of calls to best observe and track changes in calling behavior is a relatively new approach (Burchardt & Knörnschild, 2020;Ravignani & Norton, 2017). This study is novel in its application of this approach on anuran species, and it is the first time that this method has been used to visualize and quantify behavioral responses to acoustic competition.…”

“…Method instructions were followed as suggested by Burchardt and Knörnschild (2020). IOIs were used as the basis for all analyses but were adjusted to be End-to-End Intervals within a sequence, as opposed to the beginning of one call to the beginning of the next, owing to the more definitive structure of the species' call at this point.…”

“…IOIs were used as the basis for all analyses but were adjusted to be End-to-End Intervals within a sequence, as opposed to the beginning of one call to the beginning of the next, owing to the more definitive structure of the species' call at this point. A visual inspection of IOI distribution in a histogram was followed by calculations of nPVI values and the coefficient of variation per sequence (for more detailed information on equations on nPVI and coefficient of variation, see Burchardt & Knörnschild, 2020). In this study, we report on two connected parameters related to rhythm analysis: IOIs (of the whole dataset) and exact beat frequencies (in Hertz, beats per second) of individual call sequences; the first parameter being an indicator of the applicability of exact calculations, and the second being the results of those exact calculations.…”

A primer on rhythm quantification for fish sounds: a Mediterranean case study

“…">BackgroundRhythms (i.e. non-random, ordered and recurrent alternation of different elements in a sequence of sounds and silence) can be found everywhere in the world, from human speech to music and animals' communication [1,2]. They can be an inherent…”

“…A sequence can be terminated if it is disturbed by a different individual or after a prolonged silence. Sequences can range from only a few elements to containing several hundred elements, as is the case in bird song or long echolocation sequences in sperm whales [2,29].…”