DOI: 10.5061/dryad.4f4qrfj99
title: Data from: Low frequency sampling rates are effective to record bottlenose dolphins

description: Acoustic monitoring in cetacean studies is an effective but expensive
 approach. This is partly because of the high sampling rate required by
 acoustic devices when recording high-frequency echolocation clicks.
 However, the proportion of recording echolocation clicks at different
 frequencies is unknown for many species, including bottlenose dolphins.
 Here, we investigated the echolocation clicks for two subspecies of
 bottlenose dolphins in the western South Atlantic Ocean. The possibility
 of record echolocation clicks at 24 and 48 kHz was assessed by two
 approaches. First, we considered the clicks in the frequency range up to
 96 kHz. We found a loss of 0.95-13.90% of echolocation clicks in the
 frequency range below 24 kHz, and 0.01-0.42% below 48 kHz, to each
 subspecies. Then, we evaluated these recordings downsampled at 48 and 96
 kHz and confirmed that echolocation clicks are recorded at these lower
 frequencies, with some loss. Therefore, despite reaching high frequencies,
 the clicks can also be recorded at lower frequencies because echolocation
 clicks from bottlenose dolphins are broadband. We concluded that
 ecological studies based on presence-absence data are still effective for
 bottlenose dolphins when acoustic devices with a limited sampling rate are
 used.
description: Data
 sampling Echolocation
 clicks of two bottlenose dolphin subspecies that occur across different
 environments in the western South Atlantic Ocean (wSAO) were analyzed.
 These were <i>Tursiops truncatus truncatus</i>, which are
 found in open waters, and <i>T. t. gephyreus</i>, which are
 found in coastal areas. We collected <i>T. t. gephyreus</i>
 data from the lagoon system adjacent to Laguna (28º20’S, 48º50’ W),
 southern Brazil. This lagoon system has depths of between 0.4 and 13 m,
 with an average depth of 1.8 m. The data were collected from December
 4<sup>th</sup> to 12<sup>th</sup>, 2017, using a
 4.4 m inflatable research boat with a 30 hp outboard engine. The
 recordings were made with the engine off at sites with depths of 2.5 to
 5.0 m. The hydrophone was positioned 1.5 m from the surface and 15 groups
 were sampled using recordings that lasted around 4 to 34 min. Each sampled
 group contained two to seven dolphins that were displaying travel or
 foraging behavior. The
 <i>T. t. truncatus</i> were opportunistically recorded in open
 waters by the Cetaceans Project Monitoring in the Santos Basin area, south
 and southeastern Brazil. The data were collected on February
 14<sup>th</sup> and 24<sup>th</sup>, 2017, from
 three sites near the coast, at depths of 21, 28, and 35 m. Photographic
 records confirmed the subspecies <i>T. t. truncatus</i>. The
 group sizes ranged from 12 to 30 individuals, and two groups showed
 travelling behavior, and one was travelling/foraging. The recordings
 lasted approximately 7 to16 min. Two types of vessels were used: a 23.7 m
 mini supply vessel with two 325 hp engines and a 5 m inflatable boat with
 a 50 hp outboard engine. The hydrophone was positioned 5 m from the
 surface and the engines were off during the recordings. All the
 recordings from both subspecies were made using a Reson TC 4032 hydrophone
 (0,005-120 kHz) connected to a Sony PCM-D100 recorder with a sampling rate
 of 192 kHz/24 bit (maximum frequency of 96 kHz – Nyquist
 frequency).  
 Sample
 processing Two different
 approaches were used to analyze if echolocation clicks recorded in a
 frequency range up to 96 kHz can be recorded in frequency ranges below 24
 and 48 kHz. First, the recordings were assessed visually through
 spectrograms to quantify the proportion of echolocation clicks occurring
 in each frequency range of interest. That is, each click observed in the
 spectrograms was verified if it occurred below and above frequency
 thresholds of 24 and 48 kHz. Second, the recordings were downsampled at 48
 and 96 kHz (Nyquist frequency of 24 and 48 kHz, respectively) and
 processed by an automatic signal detector, to test if echolocation clicks
 are recorded when recordings are made at a lower sampling rate.
  
 <i>Proportion of
 echolocation clicks in each frequency
 range</i> The recordings
 from each dolphin group were fractionated into 1-minute samples to
 standardize the sample units. However, some recording durations were
 longer than multiples of 1 minute. Therefore, to use all records, we
 included samples of less than 1 minute in the analysis, that is those
 sections beyond the 1-minute limit of the last sample. We analyzed the
 samples in spectrograms using Raven Pro 1.6.1 software, with a sampling
 rate of 192 kHz/ 24 bit, Hann window, 512 points in size, and overlap of
 50%. Raven uses Fourier transform to create spectrograms as a frequency
 domain representation of the signal. The
 spectrograms were visually inspected to identify echolocation clicks.
 These clicks were defined and identified as those belonging to click
 trains with interclick intervals longer than 10 ms. The total number of
 echolocation clicks recorded up to 96 kHz was counted manually. Each
 echolocation click was visually inspected to verify its occurrence below
 and/or above frequency thresholds of 24 kHz and 48 kHz given the full
 frequency range of the recordings. Then clicks were counted in each
 frequency threshold to estimate the proportion of clicks that appear in
 each threshold. The total number of echolocation clicks recorded up to 96
 kHz was paired with the total number of echolocation clicks that occurred
 in 24 kHz and/or 48 kHz thresholds. This is because the same click, when
 visualized below 24 kHz, was counted as “up to 24 kHz” and “up to 48 kHz”,
 while the clicks with frequencies above 24 kHz, but below 48 kHz, were
 only counted as “up to 48 kHz”.  
 <i>Downsampling and
 automatic detections</i> A sampled
 high-frequency signal can change when it is sampled in different, lower
 sampling rates. Because of this, in our second approach, the recordings
 were downsampled at 48 and 96 kHz to analyze if echolocation clicks are
 recorded at 24 and 48 kHz, using an automatic signal detector. The
 downsample and the following automatic  clicks detection were made using R
 3.6.0. First, a fourth-order Butterworth 15 kHz high-pass filter and an
 anti-aliasing Finite Impulse Response (FIR) low-pass filter to the 24 or
 48 kHz were applied in the original recordings, for each corresponding
 downsample frequency. Then, the downsample was made using the “downsample”
 function of the “tuneR” R package. Next, we used the “auto_detec” function
 of the “warbleR" R package to detect the echolocation clicks
 automatically in recordings at 96, 48, and 24 kHz—in other words, the
 original and downsampled frequencies. The parameters to configure the
 “auto_detec” were defined through the “optimize_auto_detec” function from
 the “warbleR" R package. The
 “optimize_auto_detec” function takes a selection table containing the
 times of each signal (echolocation clicks, in this case), and then run the
 automatic detection with multiple parameters to find the ones that
 maximizes sensitivity and specificity of signal detections compared to the
 selection table. Subsamples of 5-seconds from different recordings and
 different dolphins’ groups were used to select the echolocation clicks and
 validate automatic detections. The selection tables contained one
 subsample of each <i>T. t. truncatus</i> group (total of three
 groups), and one subsample of two different <i>T. t.
 gephyreus</i> groups. The signals were manually selected in the
 subsamples of each sampling rate, to define the best-adjusted parameters
 to detect clicks in each frequency (96, 48, and 24 kHz). The least number
 of subsamples from <i>T. t. gephyreus</i> used was due to
 these dolphins are from the same population and environment (lagoon
 system). Even though we
 have selected the optimal parameters to configure the signal detector
 based on <i>a priori</i> manual detections, the sensibility
 and specificity of the detector were not the same to all recordings. Then,
 only the presence/absence of signal detections were considered, since we
 cannot guarantee that all detections are true positive. Furthermore,
 additional filters were applied to reduce the number of false-positive
 detections. First, all detections were filtered based on the interval of
 signals detected, similar to interclick intervals, excluding detections
 with an interval longer than 0.2 s, since echolocation clicks occur in
 click trains, not isolated. Second, the detection localization (in time)
 in the original recording was compared with detection localization in
 downsampled recordings, assuming the detector performance was better in
 recordings at 96 kHz. Since the echolocation clicks in the downsampled
 recordings tend to be longer than in the original record, a buffer was
 created around each detection of the original recordings by expanding the
 start and end time of detections by the length of each signal (i.e. click
 duration) to guarantee the match between the same detections at each
 frequency. Next, recordings were divided into bins of one second and
 matching bins from the division of the 1-minute samples from the first
 approach. The bins from
 the original recordings (96 kHz) were binarized. Only 1-second bins with
 at least one signal detected present were kept and compared with the same
 respective bins in 24 and 48 kHz. Finally, the matches between bins with
 the presence of signals in downsampled recordings (24 and 48 kHz) and in
 original recordings (96 kHz) were quantified. Finally, this yields a
 proportion of seconds with signals detected in downsampled recordings
 given the total number of seconds with detections in the original files.
 These proportions in each 1-minute sample (or fractions) were used to
 calculate the probability to detect echolocation clicks at 24 or 48
 kHz.   Sampling rate
 assessment First, the
 hypothesis that bottlenose dolphins echolocation clicks occur equally in
 frequencies ranges of up to 24, 48 and 96 kHz, when recorded from
 free-living animals, was tested. A generalized linear mixed-effects models
 (GLMM) was constructed using a binomial error structure with a logit link
 function. The proportion of echolocation clicks that occurs at each
 frequency range out of the total number of echolocation clicks recorded up
 to 96 kHz was the response variable and the frequency range and
 environment (lagoon system or open water) were fixed effect terms. To
 account for sample pairing (up to 24 kHz and up to 48 kHz), pairs of
 samples were treated as random effects and were used nesting within
 recording identities to account for autocorrelation in group composition
 and behavioral context during sampling. 
             A candidate
 model was built to test the hypothesis and it was compared to a null model
 containing only the intercept. Models were compared using the Akaike
 information criterion corrected (AICc) for small samples and the Akaike
 weights. The model with the lowest AICc value and the highest Akaike
 weight was considered to be the most parsimonious and the one that better
 supported data variation. We simulated 10,000 datasets from the fitted
 model to validate the model assumption. Then, the Kolmogorov-Smirnov test
 was used to determine whether the deviations between the observed and
 expected residual distributions were significant. The random effects were
 validated by visually comparing a QQ-plot of the random effects quantiles
 against the standard normal quantiles. The assumptions were valid if most
 values fell along the line. Model fit was assessed using the theoretical
 marginal and conditional  R². Marginal R²   represents the proportion of
 the total variance explained by the predictors (fixed effects) and
 conditional  R² represents the proportion of the variance explained by
 both the fixed and random effects.            
 Finally, the hypothesis that the echolocation clicks are recorded at
 frequencies up to 24 and 48 kHz, was tested. A second set of GLMM was
 constructed using a binomial error structure to test if echolocation
 clicks can be detected equally across the different sampling rates (the
 downsampled recordings). Considering only the bins with presence of at
 least one signal in the original frequency, the response variable was the
 proportion of bins with detections that occurs in the 1-minute samples at
 each downsampled frequency out of the total number of bins with detections
 in the equivalent sample in original frequency. The frequency and the
 environment (lagoon system or open water) were used as predictors, similar
 to the first model. The 1-minute samples were treated as random effects,
 nested within recording identities to account for autocorrelation in group
 composition and behavioral context during sampling. The next steps in this
 analysis, which involved candidate model build, models comparison, and the
 model fit assessment, were conducted as in the first model. All analyses
 were conducted using R 3.6.0.
description: <b>01_data_prop_freq.csv</b> The file contains 220 one minute samples (rows) and seven columns: <i>dolphin_group</i> contains a string for each group recorded, with a prefix for coastal (COAST) and offshore (OFF) recordings, and the ID of the group separated by an underline; <i>environment</i> contains information of the environment where the recording was made (coastal or offshore); <i>recording</i> contains the number of the recording file; <i>duration</i> contains the sample duration in seconds (up to 60 s); <i>click_L24</i> contains the number of clicks only detected in frequencies below 24 kHz; <i>clicks_L48</i> contains the number of clicks detected in frequencies up to 48 kHz; <i>clicks_total</i> contains all the clicks detected up to 96 kHz.   <b>Data_GPS_location.csv </b> The file contains 18 locations of recordings, with four columns. <i>environment</i> contains information of whether the recordings were made in the coastal lagoon system (Lagoon_s) or offshore (Open_w); <i>group</i> contains the group ID for each recording in the environment; <i>Lat</i> and <i>Long</i> contains the latitude and longitude in degrees, respectively.   <b>setup.R</b> R code to prepare the environment before running <b>analysis.R</b>, <b>figure_seewave.R</b>, <b>map.R</b>. Briefly, setup.R installs and load R packages, load custom functions and define colours to plot figures.   <b>analysis.R </b> Contains the R code to run the analysis and reproduce the results.   <b>figure_seewave.R</b> Contains the R code to plot spectrograms from audio samples <b>above_24khz.wav</b> and <b>above_48khz.wav</b>. <i>plotSpectro</i> function is loaded from <b>setup.R</b>.   <b>map.R </b> Contains the R code to generate the maps.   <strong>detect_clicks_warbler.R</strong> Contains the R code to apply filters and downsample the recordings.   <strong>filter_clicks_warbleR.R</strong> Contains the R code to filter detections based on the interval of signals detected, to analyze the overlapping clicks in each downsampled frequency, and to analyze the results.   <strong>get_clicks.R</strong> Contains the R code to run the automatic detection of clicks, with the parameters used in each frequency and for each environment.   <strong>modelling_click_detections.R</strong> Contains the R code to consider the detections by time bins and bins with detections per minute, and to analyze the results.   <strong>validate_clickCountR.R</strong> Contains the R code to run the 'optimize_auto_detect' funcion.   <strong>above_24khz.wav</strong> Sound archive of a sample subset, to plot the spectrogram figure.   <strong>above_48khz.wav</strong>  Sound archive of a sample subset, to plot the spectrogram figure.