A System for Describing Bird Song Units

Thompson, Nicholas S.; Ledoux, Kerry; Moody, Kevin

doi:10.1080/09524622.1994.9753257

Cited by 48 publications

(27 citation statements)

References 14 publications

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“…For differentiation between rail species, we were limited to five frequency‐derived parameters due to high levels of pairwise correlation among initial seven parameters. The addition of new parameters describing variation in the temporal domain and aggregated phrases and notes could possibly increase the statistical power of our analyses (Thompson, LeDoux, & Moody, ). Although parametric classification methods require parameter selection prior to analysis, nonparametric classification methods allow for parameter selection during analysis.…”

Section: Discussionmentioning

confidence: 99%

Quantitative acoustic differentiation of cryptic species illustrated with King and Clapper rails

Stiffler

Schroeder

Anderson

et al. 2018

Ecology and Evolution

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Reliable species identification is vital for survey and monitoring programs. Recently, the development of digital technology for recording and analyzing vocalizations has assisted in acoustic surveying for cryptic, rare, or elusive species. However, the quantitative tools that exist for species differentiation are still being refined. Using vocalizations recorded in the course of ecological studies of a King Rail (Rallus elegans) and a Clapper Rail (Rallus crepitans) population, we assessed the accuracy and effectiveness of three parametric (logistic regression, discriminant function analysis, quadratic discriminant function analysis) and six nonparametric (support vector machine, CART, Random Forest, k‐nearest neighbor, weighted k‐nearest neighbor, and neural networks) statistical classification methods for differentiating these species by their kek mating call. We identified 480 kek notes of each species and quantitatively characterized them with five standardized acoustic parameters. Overall, nonparametric classification methods outperformed parametric classification methods for species differentiation (nonparametric tools were between 57% and 81% accurate, parametric tools were between 57% and 60% accurate). Of the nine classification methods, Random Forest was the most accurate and precise, resulting in 81.1% correct classification of kek notes to species. This suggests that the mating calls of these sister species are likely difficult for human observers to tell apart. However, it also implies that appropriate statistical tools may allow reasonable species‐level classification accuracy of recorded calls and provide an alternative to species classification where other capture‐ or genotype‐based survey techniques are not possible.

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Section: Discussionmentioning

confidence: 99%

Quantitative acoustic differentiation of cryptic species illustrated with King and Clapper rails

Stiffler

Schroeder

Anderson

et al. 2018

Ecology and Evolution

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“…For instance, bird songs have been subdivided in a variety of subcategories (e.g., Shiovitz 1975) of which as many as 28 have been compiled by Thompson et al (1994), including syllable, note, bout, phrase, trill and element. These authors also proposed a complex formula system for bird songs, which however has not been adopted by many ornithologists.…”

Section: Units and Terms Recommended For The Description Of Anuran Callsmentioning

confidence: 99%

The use of bioacoustics in anuran taxonomy: theory, terminology, methods and recommendations for best practice

Köhler

Jansen

Rodríguez³

et al. 2017

Zootaxa

459

410

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Vocalizations of anuran amphibians have received much attention in studies of behavioral ecology and physiology, but also provide informative characters for identifying and delimiting species. We here review the terminology and variation of frog calls from a perspective of integrative taxonomy, and provide hands-on protocols for recording, analyzing, comparing, interpreting and describing these sounds. Our focus is on advertisement calls, which serve as premating isolation mechanisms and, therefore, convey important taxonomic information. We provide recommendations for terminology of frog vocalizations, with call, note and pulse being the fundamental subunits to be used in descriptions and comparisons. However, due to the complexity and diversity of these signals, an unequivocal application of the terms call and note can be challenging. We therefore provide two coherent concepts that either follow a note-centered approach (defining uninterrupted units of sound as notes, and their entirety as call) or a call-centered approach (defining uninterrupted units as call whenever they are separated by long silent intervals) in terminology. Based on surveys of literature, we show that numerous call traits can be highly variable within and between individuals of one species. Despite idiosyncrasies of species and higher taxa, the duration of calls or notes, pulse rate within notes, and number of pulses per note appear to be more static within individuals and somewhat less affected by temperature. Therefore, these variables might often be preferable as taxonomic characters over call rate or note rate, which are heavily influenced by various factors. Dominant frequency is also comparatively static and only weakly affected by temperature, but depends strongly on body size. As with other taxonomic characters, strong call divergence is typically indicative of species-level differences, whereas call similarities of two populations are no evidence for them being conspecific. Taxonomic conclusions can especially be drawn when the general advertisement call structure of two candidate species is radically different and qualitative call differences are thus observed. On the other hand, quantitative differences in call traits might substantially vary within and among conspecific populations, and require careful evaluation and analysis. We provide guidelines for the taxonomic interpretation of advertisement call differences in sympatric and allopatric situations, and emphasize the need for an integrative use of multiple datasets (bio-acoustics, morphology, genetics), particularly for allopatric scenarios. We show that small-sized frogs often emit calls with frequency components in the ultrasound spectrum, although it is unlikely that these high frequencies are of biological relevance for the majority of them, and we illustrate that detection of upper harmonics depends also on recording distance because higher frequencies are attenuated more strongly. Bioacoustics remains a prime approach in integrative taxonomy of anurans if uncertainty due to possible intraspecific variation and technical artifacts is adequately considered and acknowledged.

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“…After parametric analysis, song elements are either told apart or lumped together. In a bottom-up approach, the pool of classi®ed song elements is then taken to categorize the songs and thus assess the repertoire of song-types (Todt 1968;Lemon and Chat®eld 1971;Shiovitz 1975, Bondesen 1979Hultsch and Todt 1981;Kroodsma 1982;Thompson et al 1994).…”

Section: General Rules Of Song Organizationmentioning

confidence: 99%

How songbirds deal with large amounts of serial information: retrieval rules suggest a hierarchical song memory

Todt

Hultsch

1998

Biological Cybernetics

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Many songbirds develop remarkably large vocal repertoires, and this has prompted questions about how birds are able to successfully learn and use the often enormous amounts of information encoded in their various signal patterns. We have studied these questions in nightingales (Luscinia megarhynchos), a species that performs more than 200 dierent types of songs (strophen), or more than 1000 phonetically dierent elements composing the songs. In particular, we investigated whether and how both song repertoires and song performance rules of nightingales were coded by auditory stimuli presented in serial learning experiments. Evaluation of singing episodes produced by our trained birds revealed that nightingales cope well with an exposure to even long strings of master song-types. They can readily acquire information encoded within and between the dierent master songs, and they memorize, for example, which master song-types they have experienced in the same learning context. Imitations of such song-types form distinct sequential associations that are termed``context groups''. Additionally, nightingales develop other song-type associations that are smaller in size and termed``package groups''. Package formation results from constraints of the acquisition mechanisms which obviously lead to a segmentation of auditorily perceived master song sequences. Further experimentation validated that the song memory of nightingales is organized in a hierarchical manner and holding information about``context groups'' composed of packages,``package groups'' composed of songs, and songs composed of song elements. The evidence suggests that implementation of such a hierarchical organization facilitates a quick retrieval of particular songs, and thereby provides an essential prerequisite for a functionally appropriate use of large vocal repertoire is in songbirds.

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A System for Describing Bird Song Units

Cited by 48 publications

References 14 publications

Quantitative acoustic differentiation of cryptic species illustrated with King and Clapper rails

Quantitative acoustic differentiation of cryptic species illustrated with King and Clapper rails

The use of bioacoustics in anuran taxonomy: theory, terminology, methods and recommendations for best practice

How songbirds deal with large amounts of serial information: retrieval rules suggest a hierarchical song memory

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