DOI: 10.5061/dryad.kh18932hb
title: Behavioural context of call production in humpback whale calves: Identification of potential begging calls in a Mysticete species

description: Baleen whale calves vocalize, but the behavioural context and role of
 their social calls in mother-calf interactions are yet to be documented
 further. We investigated the context of call production in humpback whale
 (Megaptera novaeangliae) calves using camera-equipped animal-borne
 multi-sensor tags. Behavioural states, including suckling sessions, were
 identified using accelerometer, depth, and video data. Call types were
 categorized through clustering techniques. We found that call types and
 rates predict the occurrence of a given state. Milling, resting, and
 traveling were associated with a median call rate of 0 calls min-1, while
 surface play, tagging responses, and suckling were associated with higher
 call rates, averaging up to a median of 0.5 calls min-1 for suckling.
 Suckling sessions were mainly associated with two sets of low-frequency
 calls corresponding to previously described burping, barking, and snorting
 sounds. Surface play sessions featured mid-frequency calls with whoop-like
 sounds and other call types. These results address the significance of
 vocal signalling in mother-calf communication and the calf’s development,
 including the first identification of potential begging calls. Overall,
 this study offers new insights into baleen whale behaviour, underscores
 the importance of social calls in mother-calf interactions, and enhances
 our understanding of communication systems in aquatic mammalian
 mother-young pairs.
description: # Behavioural context of call production in humpback whale calves:
 Identification of potential begging calls in a Mysticete species Baleen
 whale calves vocalize, but the behavioral context and role of their social
 calls in mother-calf interactions require further documentation. Using
 camera-equipped multi-sensor tags, we identified behavioral states in
 humpback whale calves and analyzed the relationship between call types and
 behavioral states, including suckling sessions. ### 1. **Dataset
 Description** The dataset (data.csv) contains acoustic characteristics of
 social calls and behavioral state data for studying the context of
 vocalizations in humpback whale calves. Each row in the dataset
 corresponds to a single recorded vocalization event, with the
 corresponding behavioral state segment (1 segment = 1 continuous
 behavioral state). There may be multiple calls from the same behavioral
 state segment. If a behavioral state segment does not contain any calls,
 its data are still included as one row labeled as "O.Silence." 
 Columns in the Dataset: * **depID**: Unique identifier for each
 deployment/calf. * **AV_call_type**: Provisionally assigned name based on
 aural and visual examination following the description in Saloma et al
 (2022). "O.Silence" is used for segments without any calls
 (silent). * **st**: Start time of the call relative to the original
 Audio/Video file, measured in seconds. * **en**: End time of the call
 relative to the original Audio/Video file, measured in seconds. * **Dur**:
 Duration of the call, expressed in seconds. * **F0**: Fundamental
 frequency of the call, measured in Hz. * **Fmax**: Maximum frequency of
 the call, measured in Hz. * **Q25**: First energy quartile frequency,
 measured in Hz. * **Q50**: Median energy quartile frequency, measured in
 Hz. * **Q75**: Third energy quartile frequency, measured in Hz. * **Bdw**:
 Frequency bandwidth within which the total energy falls within 12 dB of
 Fmax, measured in Hz. * **stateNum**: Numerical identifier for the
 corresponding behavioral state segment. * **state_st**: Start time of the
 behavioral state segment relative to the deployment data start time,
 measured in seconds. * **state_en**: End time of the behavioral state
 segment relative to the deployment data start time, measured in seconds. *
 **state_vidNam**: Name of the original Audio/Video file. *
 **state_Start_s_**: Start time of the state relative to the original
 Audio/Video file, measured in seconds. * **state_Stop_s_**: End time of
 the state relative to the original Audio/Video file, measured in seconds.
 * **raw_depID**: Original unique identifier for the deployment/calf prior
 to any processing. This identifier is based on the tagging date, along
 with other information such as the tag unit used (identified by color) and
 the category of the tagged individual. * **behavior**: Describes the
 behavioral state. Mi: Milling, Pl: Surface play session, Po: Response to
 tagging, Re: Resting, Su: Suckling session, T: Traveling. For the rows
 labeled as "O.Silence" (see details above), the following
 columns (relating to call characteristics)—st, en, Dur, F0, Fmax, Q25,
 Q50, Q75, and Bdw—are marked as **missing data "NA" **(Not
 Assigned), as no acoustic parameters are associated with segments lacking
 calls. ### 2. **Code Overview** Language: R Version: v.1 #### 2.1
 **Initial Data Preparation** The code reads in the dataset and filters out
 silence (`O.Silence`). #### 2.2 **Cluster Analysis** * **Principal
 Component Analysis (PCA)**: PCA is performed on the acoustic parameters
 (after handling missing data with multiple imputations) to reduce
 dimensionality and explore the structure of the call data. *
 **Hierarchical Clustering**: Clustering is applied using the results of
 PCA, determining 13 distinct clusters. These clusters represent different
 "Types" of vocalizations. * **Cluster Assignment**: The original
 dataset is updated with cluster labels (`clust`) for each vocalization,
 and a contingency table is generated to compare the clusters with the
 provisionally assigned categories. #### 2.3 **Data Preparation for
 Behavioral Analysis** * **Combining Silence and Calls**: Silence and
 vocalization data are combined back to create a full dataset
 (`acou_classified`), where each call is assigned a cluster number (silence
 = 0). * **Vocal Activity Calculation**: A binary indicator (`vox`) is
 added to each row to distinguish between calls (1) and silence (0). The
 duration of each behavioral state segment is calculated, and the number of
 calls per state segment is aggregated. * **Call Type Occurrence**: The
 presence or absence of each call type in the segment is recorded for
 further analysis. #### 2.4 **Visualizations** * **PCA Visualization**: The
 PCA results are plotted to visualize the contributions of variables and
 the clustering of the calls. * **Call Frequency Plot**: A bar plot shows
 the frequency of different call types for each individual (`depID`),
 excluding silence segments. * **Call Rate by Behavior**: A box plot
 illustrates the distribution of call rates across different behavioral
 states. #### 2.5 **Random Forest Classification** * **Random Forest
 Model**: A random forest model is built to predict behavioral states based
 on the call rates and presence/absence of each call type. * **Confusion
 Matrix**: The model’s accuracy is evaluated using a confusion matrix,
 which shows the correct classifications and the expected classifications
 by chance. * **Variable Importance Plot**: The importance of different
 call types in predicting behavior is visualized in a heatmap. ### 3.
 **Notes** 1. **Load Dataset**: Make sure `data.csv` is in the working
 directory before running the code. 2. **Run Cluster Analysis**: The
 clustering part of the code identifies distinct call types, so ensure you
 have run it. 3. **Behavioral Context Analysis**: The behavioral state
 analysis requires the combined dataset with silence and calls, and several
 aggregation operations are performed at this stage to obtain a table with
 each row corresponding to a unique behavioral state segment. ### 4.
 **Required Libraries** Ensure that the following R libraries are
 installed: * `dplyr`: For data manipulation. * `NbClust`: For determining
 the optimal number of clusters. * `ggplot2`: For plotting. * `missMDA`:
 For multiple imputation of missing data. * `FactoMineR` and `factoextra`:
 For PCA and clustering. * `rfPermute`: For building random forest models.
 ### 5. **File Outputs** The code generates multiple plots and tables,
 which can be saved or visualized directly: * PCA plots of acoustic
 parameters. * Cluster visualization plots. * Call frequency and rate
 plots. * etc. ### 6. **Contact** For questions or clarifications about the
 dataset or code, please contact the author.