African elephant rumbles differ between populations and sympatric social groups: possible consequences of vocal learning?
Data files
Sep 21, 2023 version files 120.95 MB
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20230911_MasterList_dur90_aenv_pmelspec_certID1_Samb_Ambo_n1918_dates_fixed.rds
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20230919_dfall_seltabs_and_acoustic_features_for_upload.csv
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20230919_seltabs_for_upload_group_signature_paper.csv
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20230919_variable_definitions_group_signature_paper.xlsx
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README.md
Abstract
Vocal production learning, the ability to modify vocalizations in response to sounds made by others, was a critical prerequisite for the evolution of human speech but is rare among mammals. Elephants have exhibited this ability in captivity, yet its function in wild elephants remains unknown. Female African savannah elephants (Loxodonta africana) live in large societies with nested tiers of association in which vocal signatures of group identity could facilitate recognition of distant social affiliates. Vocal production learning allows the formation of such group signatures in many species and can also cause vocal differentiation between populations. However, the existence of vocal signatures of social group or population in elephants was unexplored. We recorded multiple social groups of wild elephants in two Kenyan populations (Samburu and Amboseli) and used random forest models to determine if calls could be assigned to individual callers, family groups, bond groups (collections of family groups), or populations based on acoustic structure. Calls were assigned by a random forest model to individual callers and populations with better-than-chance accuracy, demonstrating population-level divergence in vocalization structure. While random forest models failed to accurately assign calls to family or bond group, calls from the same family or bond group were significantly more similar (higher proximity scores) than calls from different groups, suggesting the existence of group signatures as well. We discuss possible drivers of this differentiation and argue that vocal learning is the most likely explanation for population- and group-level variation in elephants. The existence of group signatures suggests recognition of large numbers of individuals as a possible adaptive function for vocal production learning in elephants.
README
# African elephant rumbles differ between populations and sympatric social groups: possible consequences of vocal learning?
We investigated whether elephant rumbles contain acoustic signatures of
individual caller identity, family group identity, bond group
(collection of bonded family groups), or population. We recorded rumbles
from wild African elephants in Samburu & Buffalo Springs National
Reserves, northern Kenya and Amboseli National Park, southern Kenya,
noting the identity of the caller and the behavioral context. We
measured a suite of acoustic features on each call and used random
forest models to try to assign calls to population, bond group, family
group, and individual caller based on the acoustic features. We found
that calls could be assigned to population and individual caller, but
not family or bond group, with significantly better accuracy than the
majority classifier that always guessed the most common category.
However, when we calculated a proximity score (measure of similarity)
between each pair of calls in the dataset, we found that calls from the
same family or bond group were significantly more similar than calls
from different family or bond groups, indicating that calls likely
contain signatures of social group identity likely exist in the calls as
well as population differences. We discuss possible drivers of this
differentiation and conclude that vocal learning is the most likely
cause, although other possibilities cannot be definitively ruled out.
Description of the data and file structure
“20230919_seltabs_for_upload_group_signature_paper.csv” This
spreadsheet contains all the relevant information associated with each
call in the dataset except for the acoustic feature variables. Each row
represents a single call, and columns indicate the identity of the
caller, the behavioral context, the certainty with which caller ID and
behavioral context were known, the caller’s family group and population,
and other relevant information, including the file name of the sound
file in which the call occurs, the start and end times of the call in
that sound file, the file name of the Raven Pro selection table used to
annotate the sound file, and the number of the selection box in that
table corresponding to the call in question. The sound files and Raven
selection tables themselves are not included in this upload. The full
set of variables in this spreadsheet are defined in the spreadsheet
named “20230919_variable_definitions_group_signature_paper.csv”.
The derived acoustic features used in the analysis are not included in
this spreadsheet. Those measurements can be calculated using the code
provided (“20230911_group_signature_code_for_upload.R”) and the raw
acoustic measurements in the .RDS file named
“20230911_MasterList_dur90_aenv_pmelspec_certID1_Samb_Ambo_n1918_dates_fixed.rds”.
Values of “NA” in this spreadsheet indicate that the information in
question was unknown for a given observation. Alternatively the user can
simply use the spreadsheet
“20230919_dfall_seltabs_and_acoustic_features_for_upload.csv”
which contains the behavioral/identity information and the derived
acoustic features together.
“20230919_variable_definitions_group_signature_paper.csv” This
spreadsheet defines each of the variables in the files
“20230919_seltabs_for_upload_group_signature_paper.csv” and
“20230919_dfall_seltabs_and_acoustic_features_for_upload.csv”.
“20230911_MasterList_dur90_aenv_pmelspec_certID1_Samb_Ambo_n1918_dates_fixed.rds”
This RDS file is an R list object which contains the raw acoustic
contours measured on each call. The code provided
(“20230911_group_signature_code_for_upload.R”) uses these raw
acoustic contours to calculate a suite of derived acoustic features
which are then used for subsequent statistical analyses. The list is
nested with two layers. Each outer slot represents a single call, and is
named with a unique identifier for that call, which is comprised of the
date (YYYYMMDD) and time (HHMM) of the original recording as well as the
Raven selection box number of the call in question. The inner slots
represent individual acoustic contours, each of which is a vector of
values. The acoustic contours are as follows: dur90 = time duration
needed to capture 90% of the energy in the call (not used in analysis)
aenv = Hilbert amplitude envelope aenvtime = time points (sec) for aenv
(not used in analysis) pmsband(1-26): energy in each of the 26 bands of
a mel spectrogram, calculated from 0-500 Hz. Each energy value within a
given band corresponds to one of the time points in aenvtime pmsD(1-26):
differences between successive values of the corresponding pmsband
pmsDD(1-26): differences between successive values of the corresponding
pmsD
“20230919_dfall_seltabs_and_acoustic_features_for_upload.csv”
This file is a spreadsheet containing the observational data (caller ID,
behavioral context, etc.) together with the derived acoustic features.
This spreadsheet is generated in section 4 of the code using the data
files “20230919_seltabs_for_upload_group_signature_paper.csv” and
“20230911_MasterList_dur90_aenv_pmelspec_certID1_Samb_Ambo_n1918_dates_fixed.rds”.
However, instead of recreating it de-novo the reader may skip sections
2-4 of the code and instead load this file in section 5 of the code. The
variables in this spreadsheet are all described in the file
“20230919_variable_definitions_group_signature_paper.csv”. Values
of “NA” in this spreadsheet indicate that the information in question
was unknown for a given observation.
Code/Software
“20230911_group_signature_code_for_upload.R” This R script contains
the code for all the analyses in this study. Each section of the code is
labeled to describe what it does. The packages required for the code to
run are all listed in the first section. The sections in this script are
as follows:
- Load the required packages for the whole script.
- Code used to segment the calls from the original raw sound files and save them as clips for further analysis. The original sound files themselves are not included in this upload so this section cannot be run, but it is included to illustrate how we segmented the recordings
- Code used to measure acoustic contours on the segmented sound clips. The sound clips are not included in this upload so this section can also not be run, but it is included to illustrate how we performed the acoustic contour measurements
- Code to derive acoustic features from the acoustic contours and append them to the observational data. This section requires two files that are included in this upload: “20230919_seltabs_for_upload_group_signature_paper.csv” and “20230911_MasterList_dur90_aenv_pmelspec_certID1_Samb_Ambo_n1918_dates_fixed.rds”.
- Rather than running the previous code chunks, the user may start here and simply read in the file “20230919_dfall_seltabs_and_acoustic_features_for_upload.csv” which contains all the necessary data for the remaining analyses. This code chunk also adds a column for bond group ID (created from the existing column for family group ID), converts relevant variables to factors, and creates a subset of the data with just the calls from the Samburu population.
- Random forest model to predict family group from acoustic features
- Random forest model to predict bond group from acoustic features
- Random forest model to predict population from acoustic features
- Random forest model to predict caller ID from acoustic features
- Calculate random forest proximity scores from family group model for each pair of calls in the dataset and run gamma regression to determine if call pairs from same family group are more similar than call pairs from different family groups
- Calculate random forest proximity scores from bond group model for each pair of calls in the dataset and run gamma regression to determine if call pairs from same bond group are more similar than call pairs from different bond groups
- Calculate random forest proximity scores from population model for each pair of calls in the dataset and run gamma regression to determine if call pairs from same population are more similar than call pairs from different populations
- Make figures for the paper
- Calculate sample sizes for different analyses in the paper
Methods
EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS
Subjects were wild, free-ranging African elephants living in Amboseli National Park (n=43) and Samburu & Buffalo Springs National Reserves (n=158), Kenya. There were 149 females between 0–60 years of age (mean ± SD = 22.9 ± 13.3 years) and 52 males between 0–9 years of age (mean ± SD = 3.6 ± 2.7 years). Recordings from males older than 10 years were excluded, as we had few recordings from adult males other than acoustically distinctive musth rumbles, and we were primarily interested in the vocal behavior of female-calf groups. This study was strictly observational, and the elephants were not handled as part of the study. The study was approved by Colorado State University’s Institutional Animal Care and Use Committee under protocol #19-9229A.
METHOD DETAILS
Data collection
We recorded rumbles from wild female-calf elephant groups in two Kenyan populations with long-term individual-based monitoring projects: Amboseli National Park (“Amboseli”) and Samburu and Buffalo Springs National Reserves (“Samburu”). These two populations are 390 km apart with no current gene flow between them due to intervening urban development 29. The field recording methods for this dataset are published as a preprint 55. We recorded the identity of the caller and the behavioral context of each call, when known. Behavioral context was originally scored using slightly different ethograms in Amboseli 14 and Samburu 55. To facilitate comparison between these two datasets, we concatenated behavioral context into 12 categories shared across both populations. In Amboseli, we recorded our confidence in the caller ID and behavioral context on a subjective ordinal scale (“certain”, “fairly confident”, “educated guess”, or “no idea”). In Samburu, we recorded the confidence as the inverse of the number of possible callers or behavioral contexts (1 if known for certain, 0.5 if one of two possibilities, etc.).
Acoustic analysis
We processed and measured the recordings following a previously published protocol 55. In brief, we calculated the time series of energy values in 26 overlapping spectral bands between 0-500 Hz warped to the Mel scale 56, as well as the time series of delta and delta-delta coefficients for each of these bands (representing the rate and acceleration of change over time in each spectral band, respectively). We also calculated the Hilbert amplitude envelope for each call, resulting in a total of 79 equal-length time series, or “acoustic contours”, for each call. We then performed a robust principal components analysis on the acoustic contours of all calls stacked end to end, decomposing the data into a sparse matrix, which described outliers in the data, and a low-rank matrix, which described the remaining observations 57. We saved the means of each of the first 74 low-rank principal components for each call (describing 99.9% of the variation), 16 metrics describing the modulation of the low-rank principal components, and 4 metrics describing the sparse matrix as features for further analysis.
QUANTIFICATION AND STATISTICAL ANALYSIS
All statistical analyses were performed in R v. 4.2.2 58 and 0.05 was used as the significance threshold for all tests. For all random forest models, we only included calls with a high degree of confidence in the identity of the caller (“certain” or “fairly confident” in Amboseli, “1” in Samburu).
To test the hypothesis that Amboseli and Samburu elephants exhibit population-level acoustic differences, we ran a random forest (500 trees, 6 variables/node, 60% of observations/tree, minimum node size = 1, no maximum tree depth) to predict population as a function of the acoustic features. This analysis included 1258 calls from 158 individuals in Samburu and 597 calls from 43 individuals in Amboseli. We randomly selected 20% of the callers with at least five calls each from each population and allocated all calls from these callers to the test set, with the remaining calls allocated to the training set. We calculated the proportion of observations in the test set that were classified correctly (classification accuracy) and ran a one-tailed exact binomial test comparing the classification accuracy to the proportion that would have been classified accurately if the model always guessed the most common group in the training set (majority classifier). We repeated this process 1000 times and calculated the median P-value across all runs. The mean ± SD proportion of calls allocated to the test set was 0.18 ± 0.043.
To test the hypothesis that elephants exhibit vocal signatures of group identity at the bond group or family group level, we ran two additional random forest models (same hyperparameters) to predict bond group and family group, respectively, as a function of the acoustic features using only data from Samburu. We defined social groups based on previous work and continuous monitoring 12,59. We excluded Amboseli from the bond and family group models because 85% of our Amboseli recordings came from one family group. To ensure that the models could only use group-specific features and not individual-specific features to predict bond or family group, we randomly selected 20% of the callers with at least five calls each from each group and allocated all calls from these callers to the test set with the remaining callers allocated to the training set 60. We only included groups that had at least two individuals with at least five calls each, so that each group would always have at least five calls in both the training and test sets. This resulted in 1170 calls from six bond groups for the bond group model and 1112 calls from seven family groups for the family group model. We ran 1000 iterations, calculating the classification accuracy and p-value for each run as before. The mean ± SD proportion of calls allocated to the test set was 0.18 ± 0.054 for the bond group model and 0.16 ± 0.055 for the family group model.
To test the hypothesis that elephant rumbles are individually specific, we ran a fourth random forest (same hyperparameters) to predict individual ID as a function of the acoustic features. As calls produced by the same caller on the same date might exhibit similar features due to temporary circumstances such as caller’s internal state, behavioral context, and ambient conditions, we randomly selected one date for each caller and held out all calls from these caller-dates as the test set 60. We used callers from both populations for this analysis but only included callers that produced at least three calls on at least two different dates each (505 calls from 22 callers in Samburu, 324 calls from 11 callers in Amboseli). We calculated the classification accuracy and p-value for each of 1000 iterations as before. The mean ± SD proportion of calls allocated to the test set was 0.24 ± 0.026.
To determine if there were acoustic differences among populations, bond groups, or family groups that could not be explained by behavioral context or caller age, we calculated random forest proximity scores between each possible pair of calls. The random forest proximity score for a given pair of calls was the proportion of trees for which both calls were classified in the same terminal node, adjusted for the size of the node, and represented a metric of call similarity in terms of the acoustic features most relevant to predicting the response variable 61. We calculated proximity scores from three different random forests (population, bond group, and family group), using the same hyperparameters and subsets of the data as before except that we increased the number of trees to 8000 and did not hold out any observations as a test set. For each set of proximity scores, we ran a generalized linear model with a gamma error distribution modeling proximity score as a function of whether the two calls in a given pair came from the same “class” (population, bond group, or family group, depending on the model) and the absolute value of the age difference between the two callers. As proximity scores could be 0, we added 0.0001 to all proximity scores so all the values would be positive. To control for behavioral context and individual ID, we only included pairs of calls with the same behavioral context and different callers. We also only included calls for which we had high confidence in the behavioral context (“certain” or “fairly confident” in Amboseli, “1” in Samburu). This resulted in a sample size of 217,389 call pairs for the population model, 101,344 call pairs for the bond group model, and 93,935 call pairs for the family group model.
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