Data from: Innovative microphone transmitter reveals differences in acoustic structure between broadcast and whisper songs of Myadestes obscurus (ʻŌmaʻo)
Data files
Jan 06, 2025 version files 1.25 GB
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LusciniaOutput.zip
1.37 MB
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OMAO.Broadcast.Songs.DB.luscdb.zip
565.70 MB
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OMAO.Whisper.Songs.DB.luscdb.zip
684.90 MB
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README.md
7.78 KB
Abstract
Low-amplitude ‘whisper songs’ are a taxonomically broad phenomenon in birds that could play an important role in the suite of behaviors birds use to communicate. Due to its cryptic nature, there are inherent difficulties in capturing high-quality whisper song recordings without interrupting natural behaviors. Thus, whisper song acoustic structure is poorly understood and its potential function remains the subject of debate. Here, we present one of the first quantitative assessments of the acoustic structure of whisper song in birds. Using an innovative microphone transmitter we collected high quality recordings of broadcast and whisper songs from the Myadestes obscurus (ʻŌmaʻo), a thrush species endemic to the Island of Hawai‘i. The transmitter was attached to the birds and broadcasted radio-signals of all vocalizations produced by the individual to distances over 100 m away which minimized disruption of the birds’ normal behavior while recording. We demonstrate that ʻŌmaʻo whisper songs are a distinct class of vocalization that differ from broadcast songs in acoustic characteristics beyond amplitude, such as song length, frequency, and length of silent intervals between notes. These findings, in conjunction with habitat-associated variation in the rate at which ʻŌmaʻo emit these vocalization classes, indicate broadcast and whisper songs likely serve separate functions. This work provides evidence supporting the ‘acoustic adaptation hypothesis’ which posits that densely vegetated habitats promote the evolution of songs with specific acoustic features that maintain signal integrity as the sound propagates through the environment.
README: Data from: Innovative microphone transmitter reveals differences in acoustic structure between broadcast and whisper songs of Myadestes obscurus (ʻŌmaʻo)
https://doi.org/10.5061/dryad.kd51c5bgg
Description of the data and file structure
Files and variables
File: OMAO.Whisper.Songs.DB.luscdb.zip
Description: Luscinia formatted database files for the whisper songs collected during this study. The files can be opened and analyzed using Luscinia bioacoustic software by unzipping the file, opening Luscinia, then navigating the "Database location:" search box on the Log In page to the directory containing the OMAO.Whisper.Songs.DB.h2.db and OMAO.Whisper.Songs.DB.trace.db files (described below). It is important to navigate to the parent directory, and not the database files themselves, as they will not open independently.
Subfile: OMAO.Whisper.Songs.DB.h2.db
Description: Whisper song recording database formatted for analysis in Luscinia using the H2 Database Engine. It stores audio, metadata, and analytical results related to the bioacoustic data processed during this study. This database can be opened, inspected, and analyzed in Luscinia, although the acoustic measurements reported in the manuscript have already been exported and provided in LusciniaOutput.zip. Key data stored in this file include:
- Individuals: Metadata about recorded subjects.
- Recordings: Information about the audio files analyzed, such as file names, dates, and associated metadata.
- Vocalizations: Data about detected vocal events, including timestamps, syllables, and notes.
- Measurements: Spectral and temporal measurements of vocalizations, such as frequency, duration, etc.
Subfile: OMAO.Whisper.Songs.DB.trace.db
Description: Luscinia's log file for the whisper song database, a record of the software’s execution and processes during its analysis of audio data, containing detailed information about the acoustic features of vocalizations analyzed. The trace.db file is organized into multiple tables, each storing specific aspects of the acoustic data. Key tables include:
- Individuals: Information about the individuals whose vocalizations were analyzed, including metadata like species, recording date, and location.
- Recordings: Details about the recordings, such as file names, duration, and associated metadata.
- Syllables and Notes: Acoustic features of individual vocal units (e.g., peak frequency, duration, etc.).
- Measurements: Quantitative features extracted from the spectrograms of vocalizations.
File: OMAO.Broadcast.Songs.DB.luscdb.zip
Description: Luscinia formatted database files for the broadcast songs collected during this study. The files can be opened and analyzed using Luscinia bioacoustic software by unzipping the file, opening Luscinia, then navigating the "Database location:" search box on the Log In page to the directory containing the OMAO.Broadcast.Songs.DB.h2.db and OMAO.Broadcast.Songs.DB.trace.db files (described below). It is important to navigate to the parent directory, and not the database files themselves, as they will not open independently.
Subfile: OMAO.Broadcast.Songs.DB.h2.db
Description: Broadcast song recording database formatted for analysis in Luscinia using the H2 Database Engine. It stores audio, metadata, and analytical results related to the bioacoustic data processed during this study. This database can be opened, inspected, and analyzed in Luscinia, although the acoustic measurements reported in the manuscript have already been exported and provided in LusciniaOutput.zip. Key data stored in this file include:
- Individuals: Metadata about recorded subjects.
- Recordings: Information about the audio files analyzed, such as file names, dates, and associated metadata.
- Vocalizations: Data about detected vocal events, including timestamps, syllables, and notes.
- Measurements: Spectral and temporal measurements of vocalizations, such as frequency, duration, etc.
Subfile: OMAO.Broadcast.Songs.DB.trace.db
Description: Luscinia's log file for the broadcast song database, a record of the software’s execution and processes during its analysis of audio data, containing detailed information about the acoustic features of vocalizations analyzed. The trace.db file is organized into multiple tables, each storing specific aspects of the acoustic data. Key tables include:
- Individuals: Information about the individuals whose vocalizations were analyzed, including metadata like species and recording date.
- Recordings: Details about the recordings, such as file names, duration, and associated metadata.
- Syllables and Notes: Acoustic features of individual vocal units (e.g., peak frequency, duration, etc.).
- Measurements: Quantitative features extracted from the spectrograms of vocalizations.
File: LusciniaOutput.zip
Description: A collection of Microsoft Excel files with two kinds of data for each of the 8 ʻŌmaʻo used in the study. File names indicate individual birds (OMAO_05), whether it is broadcast (BS) or whisper song (WS) data, and which collection of data it contains, either output from Luscinia's acoustic analysis (Parameters), or syllable grouping libraries (SyllableLib). These data files can be read into R using the OmaoCleanCode.R script to reproduce the results reported in the manuscript.
- Syllable library files have the following columns
- Individual - which individual bird produced the songs, with a short numerical identifier, and the full identifier (as found in Table 1), separated by an underscore.
- Names - an identifier for a each syllable, values contain the individual bird's short identifier number, the date of the recording, the source file name, the song number the syllable comes from, and the syllable number within the song.
- LucGroup - Luscinia's assigned syllable grouping.
- Group - manually adjusted syllable grouping (used for analysis).
- Notes - annotator's notes, these indicate when a faint note was not traced in Luscinia.
- Parameter files have the following measurements derived in Luscinia (details on measurements can be found on the Luscinia website: (https://rflachlan.github.io/Luscinia/)
- Song name
- Syllable Number
- Element Number
- Record time
- Record date
- Start time
- Length
- Gap before
- Peak frequency Mean
- Peak frequency Maximum
- Peak frequency Minimum
- Median frequency Mean
- Median frequency Maximum
- Median frequency Minimum
- Fundamental frequency Mean
- Fundamental frequency Maximum
- Fundamental frequency Minimum
- Peak frequency change Mean
- Peak frequency change Maximum
- Peak frequency change Minimum
- Wiener entropy Mean
- Wiener entropy Maximum
- Wiener entropy Minimum
- Frequency bandwidth Mean
- Frequency bandwidth Maximum
- Frequency bandwidth Minimum
Code/software
Required software:
- R version 4.2 or above (https://www.r-project.org/)
- Luscinia version 2.16.10.29.0 or above (https://rflachlan.github.io/Luscinia/)
Code:
- pdfa_functions.r - functions for running a permuted discriminant function analysis on nonindependent data in R (Mundry R., and C. Sommer (2007). Discriminant function analysis with nonindependent data: Consequences and an alternative. Animal Behaviour 74:965–976.)
- OmaoCleanCode.R - an R script for compiling the Luscinia data for each bird, running the acoustic analyses described in the manuscript, and data visualization.
Methods
We equipped ʻŌmaʻo with a device that consisted of a very high frequency (VHF) radio pulse transmitter and a miniature condenser microphone that broadcasted all sounds emitted by the target bird (JDJC Corporation, Fisher, IL, USA). Radio signals from the microphone transmitter were received by a hand-held 3-element yagi antenna (JDJC Corporation, Fisher, IL, USA) connected to a wideband receiver (AR8200; AOR Ltd., Tokyo, Japan) in wideband amplitude modulation (WAM) mode. The signals were recorded by a Marantz PMD661 professional field recorder (Marantz America, LLC., Mahwah, NJ, USA), using a 2-second pre-record setting, as 24-bit WAV files at a 44.1 kHz sampling rate. The distance at which the microphone transmitter could be detected varied by terrain and weather, and birds recorded in our study were typically at a distance of < 80 m. Each bird was tracked 3–5 days a week, excluding rainy days, for 2–4 hours between 0800–1600, which encompasses the peak hours for ʻŌmaʻo vocalizations.
Recordings were processed using the bioacoustic software Luscinia (v2.16.10.29.0). Songs were delineated by > 5 s of silence between vocalization bouts and classified as either broadcast or whisper songs based on field observations, with verification through spectrogram visualization. Songs were then segmented into syllables, delineated by > 0.2 s of silence between notes in a song, and syllables were further segmented into notes, delineated by any break in continuous sound production. In Luscinia, frame length was set to 5 ms, time step was set to 1.1 ms, maximum frequency was set to 8 kHz, and the Hamming windowing function was selected before each note contour (vectors of parameter measurements with one entry for each time step in the spectrogram) was manually traced with a brush size of 5 pixels.
To determine the number of unique syllables associated with broadcast and whisper songs for each bird, we used Luscinia’s dynamic time warping (DTW) algorithm. The DTW algorithm compares the structure of each syllable with every other syllable based on the Euclidean distance between acoustic features, producing a matrix of syllable dissimilarities. To achieve optimal syllable groupings, DTW acoustic parameters were weighted differently for broadcast and whisper songs. We clustered syllables using unweighted pair group method with arithmetic mean (UPGMA) hierarchical clusterings of the dissimilarity scores from the DTW analysis, assigning syllables to groups based on the resulting dendrogram. We calculated the global silhouette index, a measure of clustering tendency, at each depth within the dendrogram and identified natural clusters among the syllables by searching for the highest peak in the index. The syllable groupings were then visually and aurally validated and reassigned as necessary by an avian acoustics specialist. Although different syllables were incorrectly assigned to the same group < 1% of the time, similar syllables were frequently separated into different groups and needed manual grouping. Syllables were classified into separate groups if they had two or more differences in the number of notes or note shape.