Determinants of variability in signature whistles of the Mediterranean common bottlenose dolphin

One of the most studied aspects of animal communication is the acoustic repertoire difference between populations of the same species. While numerous studies have investigated the variability of bottlenose dolphin whistles between populations, very few studies have focused on the signature whistles alone and the factors underlying differentiation of signature whistles are still poorly understood. Here we describe the signature whistles produced by six distinct geographical units of the common bottlenose dolphin (Tursiops truncatus) in the Mediterranean Sea and identify the main determinants of their variability. Particularly, the influence of the region (proxy of genetic distance), the geographic site, and the environmental (sea bottom-related) and demographical (population-related) conditions on the acoustic structure of signature whistles was evaluated. The study provides the first evidence that the genetic structure, which distinguishes the eastern and western Mediterranean bottlenose dolphin populations has no strong influence on the acoustic structure of their signature whistles, and that the geographical isolation between populations only partially affected whistle variability. The environmental conditions of the areas where the whistles developed and the demographic characteristics of the belonging populations strongly influenced signature whistles, in accordance with the “acoustic adaptation hypothesis” and the theory of signature whistle determination mediated by learning.

Only PC2 and PC3 were significantly associated with site, while no differences were found on PC1 (Table 2, Fig. 4). PC2 (min and start frequencies) were higher in PC and LA and lower in GC. PC3 was higher in LA, thus the negatively correlated frequency range was lower, while duration and number of inflection points did not change ( Table 2, Fig. 4).
The cluster analysis grouped the sea bottom variables (habitat type and depth range) into three clusters: (i) H1 grouped together the sites (PC and LA) characterized by Posidonia bed, infralittoral sand and circalittoral coarse sediment bottoms not exceeding 150 m of depth; (ii) H2 grouped together the sites (AL, CL and FI) characterized by circalittoral coastal terrigenous muds and coarse sediment and muddy detritic bottoms not exceeding 100 m of depth; (iii) H3 corresponded with GC, characterized by open-sea detritic bottoms on shelfedge, offshore circalittoral coastal terrigenous muds and bathyal mud, with a depth up to 500 m. Only PC2 and PC3 were significantly associated with sea bottom (Table 3). PC2 (min and start frequencies), were higher in H1 and lower in H3 (Fig. 5). PC3 was lower in H2 compared to H1 and H3 (Fig. 5). Particularly, frequency range was higher in H2 than in H1 and H3, while the number of inflection points was higher in H3.
Based on population demography, the cluster analysis grouped the six sites into four clusters: (i) P1 corresponded to LA, a quite large open population, with small group size and mostly transient individuals; (ii) P2 included AL and GC, the smallest sized populations with medium group size and mainly resident individuals; (iii) P3 corresponded to CL, an intermediate sized and isolated population, with large group size and mostly resident individuals; (iv) P4 included FI and PC, large sized open populations, with large group size and mostly transient individuals. All PCs were significantly associated with population demography (Table 4, Fig. 6). PC1 was higher in P1 respect to the others, thus max and end frequencies were both lower. PC2, thus min and start frequencies, were higher in P1. PC3 was higher in P1 and lower in P3. Particularly, frequency range and duration were lower in P1 while the number of inflection points was higher in P2 (Table 4, Fig. 6).

Discussion
SWs from the six studied populations were homogeneous within but distinct between sites, and the dissimilarity was most evident between the whistles from LA and those from the other sites. This pattern is also evident when the similarity is grouped by region: SWs from the south Mediterranean appeared more dissimilar than those from the west and the east, which instead overlapped. If the SWs' dissimilarity was mainly related to the www.nature.com/scientificreports/ geographic isolation between individuals from the six sites and/or the genetic distance between eastern and western populations of the basin, a greater difference in PCs would have been observed. Instead, the largest difference concerns the SWs coming from LA in the South, with the only exception of PC2, and the related min and start frequencies which were lower in CL and GC in the East compared to PC and AL in the West. The highest difference found in the SWs from LA is consistent with the closest acoustic structure between whistles from LA and those from the Atlantic Ocean, compared to those from the western Mediterranean populations, found in a previous study 35 . Given the geographical position in the middle of the Strait of Sicily, a most frequent or recent contact between individuals from LA with those of the neighbouring populations that inhabit the waters of the Atlantic 35 could explain the difference in SWs between the South and the other regions of the Mediterranean Sea. The case in which SWs dissimilarity was not related to a greater genetic distance between populations was already described by Gridley 28 , who found a similar pattern in the SWs from different African Tursiops spp. populations. Although it is known that the development of SWs does not have a strict genetic determination, but rather a determination mediated by learning 19,24 , the definitive understanding of how the genetic distance and the isolation between populations affect the acoustic variability of SWs is still unknown. However, our study provides the first evidence that the genetic structure which distinguishes the eastern and western Mediterranean bottlenose dolphin populations has no strong influence on the acoustic structure of their SWs. Furthermore,   www.nature.com/scientificreports/ even the geographical isolation between populations of the investigated sites only partially influenced the SWs variability. Stronger evidence was obtained by associating the SWs to the type of sea bottom in which they developed and the demographic characteristics of the belonging populations. The highest dissimilarity was found between SWs from H1 and H3. In the environment characterized by the presence of Posidonia bed (H1, corresponding to LA and PC), the SWs had the highest start and min frequencies and were shorter and less modulated compared to the SWs from H2 and H3. Since signature whistles are cohesion call, which are used to maintain group cohesion and facilitate mother-calf reunion, lower frequency and less modulated whistles could be preferred since they transmit further in the marine environment 57 . However, Quintana-Rizzo and Mann 58 found that min frequency whistle attenuated up to seven times more in seagrass areas than in areas with other bottom type (mud or sandy-mud). Coherently, in H3 (GC), characterized prevalently by muddy and detritic bottom, min and start frequency were the lowest recorded and duration and number of inflection points were the highest. Few studies have been conducted to understand the role of depth in dolphin whistles, however, Buckstaff 16 found lower minimum frequencies in deeper habitat and Gridley 28 found longer whistles related to higher depth. These findings are coherent with the SWs characteristics of GC. Unfortunately, no other conclusion can be derived for the other sites, since they all have similar depth condition, thus this aspect needs further investigation.
The strongest influence on the variability of SWs was related to the population demography. The SWs of P1 (LA) and P4 (FI and PC) were the most dissimilar. These populations have the largest size and are composed of mostly transient individuals. A high number of sounds from conspecifics in large open populations can lead to the development of widely distinctive SWs to enhance recognition 57 . In P1, SWs had quite distinct acoustic characteristics: the lowest max and end frequencies, frequency range, duration, and number of inflection points. These characteristics are likely influenced by the combined effect of several factors (region and bottom type), rather than by the type of population alone, and this makes the interpretation of the results more complex.   www.nature.com/scientificreports/ In P2 (corresponding to AL and GC, the smallest sized populations), SWs had the highest number of inflection points. Further, GC had the highest variability in duration. In small populations, where the probability to meet the same individuals is high, different SWs duration and higher number of inflection points can enhance identity coding 59,60 , even if this pattern was found elsewhere, but in a larger population 28 .
Some methodological limitations need to be considered when interpreting the results of this study. Firstly, the sample size used for the analysis should be taken into consideration, since it may be not fully representative of the variability of SWs, especially in some sites. In fact, even if the 13 SW-IDs recorded in GC can be considered sufficiently representative of the SW repertoire of this population (composed of 38 individuals on average), the SW-IDs collected in PC and FI correspond to less than 5% of individuals in these large-sized populations.
Further, a limited number of the potential factors associated with the acoustic environment and whistle variability were considered in this study. For example, data on ambient noise and vessel traffic were not available www.nature.com/scientificreports/  www.nature.com/scientificreports/ for all sites, thus these factors could not be included. High noise levels caused by vessels can have a strong influence on whistle structure 36,61,62 due to the need of making the signal more efficient in terms of transmission in noisy environments and to contrasting masking phenomena. However, a recent study 36 compared the effect of noise on the whistles (both signature and variant) of two populations considered in the present study (AL and CL), showing different acoustic response to the increase of Sound Pressure Levels (in the 125, 500 and 1000 Hz octave bands) and boat presence between the two sites. This finding suggests that ambient noise levels alone are not sufficient to explain the variation in whistle acoustic structure. In fact, all the factors influencing local sound propagation together with the characteristics of boat traffic (in terms of quantity, type and size of boats) and the interaction of these factors with the behavior of dolphin groups and physiology of individuals should be considered. Here, only general, broad-scale environmental characteristics were used. Sound transmission in shallow water is highly variable and depends on bottom sediments, depth and slope 38 , but also on tidal events, temperature gradients, freshwater inputs, obstacles in the sound path 58 and the interactive effect between the sediment and plants (such as seagrass meadows) and/or animals (like benthic in-fauna) that live on the bottom 63 .
In the end, SW convergence between alliance members can reduce diversity in whistle types 25,64 . In the present study no data were available about the sex of the SW emitters neither on the association patterns between individuals. Thus, a most accurate description of the acoustic environment, the identity of the SW emitters and the social relationship between them should deserve attention in future studies to further understand their effect on the development of SWs.   39 . Among  www.nature.com/scientificreports/ the 122 photo-identified dolphins, at least 50% of them show a high level of site fidelity 40 and they were sighted repeatedly every year and in different seasons. Nevertheless, the population seems neither closed nor isolated 40,41 . The mean size of the recorded groups is 7 individuals, with a range between 1 and 17 (Table 5).  (Table 5).

Lampedusa Island (LA). The acoustic recordings were collected in a 48 km 2 area around Lampedusa
Island located on the northern African continental shelf of the Strait of Sicily. The area includes Posidonia bed, infralittoral sand and circalittoral coarse sediment, with a bottom depth not exceeding 110 m. The estimated bottlenose dolphin population is 249 individuals (CI = 162-449) 44 , but likely these dolphins are part of a larger population 44 . The mean group size is 4, with a range between 1 and 20 45 (Table 5).  www.nature.com/scientificreports/ is 900 m). Here, a population of 38 individuals (95% CI = 32-46) was estimated 46 . Most individuals show high resighting frequency, but some dolphins are known to move in and out from the Gulf 47,48 . The mean group size is 8, with a range between 1 and 28 48 (Table 5).

Cres and Lošinj (CL).
The acoustic recordings were collected in an area of about 2000 km 2 in the northeastern Adriatic Sea. These waters are characterized by numerous uninhabited small islands and islets, infralittoral mud, circalittoral coastal terrigenous muds and circalittoral coarse sediment, with an average bottom depth of 70 m. Here, the population size was estimated to 184 individuals (95% CI = 152-250) 49 . The high sighting frequency of known individuals indicate their long-term fidelity to the region 50 . The mean size of the recorded groups is 22, with a range between 2 and 46 36 (Table 5).
Acoustic data collection. Acoustic recordings were collected with different methods and equipment, in different years and by different research groups (see Table 6). When the recordings were obtained by means of PAM (Passive Acoustic Monitoring) devices deployed on the sea bottom, as in LA, species identification was not visually confirmed. Nevertheless, the depths (< 40 m) and distances from the coast (< 1.5 km) were chosen to ensure that only bottlenose dolphins were recorded even in the absence of visual identification 35 . Moreover, no other dolphin species are present in the area. The recordings were collected with different sampling rates (from 44 to 192 kHz). However, since the recordings collected with the lowest sampling rate (44 kHz) did not contain whistles with frequencies higher than the Nyquist frequency (22 kHz), the different sampling rate did not affect the results. This work is based on the observation of dolphins and does not foresee any direct experiment on them. All procedures performed in the study were in accordance with the ethical standards of the involved institutions.
Acoustic data analysis. Signature whistles were defined as "a learned, individually distinctive whistle type in a dolphin's repertoire that broadcasts the identity of the whistle owner" 14 . Thus, signature whistles of a same individual are characterized by the same frequency modulation pattern (called contour-SW). The SWs can be produced in loops (repetitions of the same elements), usually separated by intervals less than 250 ms 27 , and can also have an introductory and/or final loop 14 distinct from the central pattern. We considered any single or multiple-loop whistle, connected or disconnected, as the unit of analysis 27 . To classify a whistle as a SW, the SIGID method 14,51 was applied, following a step-by-step procedure. First, each whistle was graded depending on Table 6. Research boats and equipment used, effort and sampling periods for the six sites. Port Cros (PC), Alghero (AL), Ostia-Fiumicino (FI), Lampedusa (LA), Gulf of Corinth (GC), Cres and Losinj (CL).  30 , the whistles present in any recording session were distinguished as repeated element whistle type (REWT-those whistles with the same contour that are present at least twice within the range of 0.25-10 s during a recording section), and other whistle (OW-variant whistles that did not respect the previous classification rule). A catalogue containing all the REWTs was constructed, assigning to each a unique identification code and a minimum of two good images of the relative contours. Then, the recordings were inspected a second time and whistles were compared with those in the catalogue. A whistle was classified as a SW if a minimum of four stereotyped contours were present in a recorded session and 75% of them occurred within 1-10 s of at least one other 51 . When a SW was identified, a distinctive individual code was assigned (SW-ID) and any whistle with the same contour was assigned to the same SW-ID (Fig. 8).
For each SW, minimum (min) frequency, maximum (max) frequency, start frequency, end frequency, frequency range, number of inflection points and duration (as defined in La Manna et al. 36  Region, geographic site, sea bottom and population demography. Four types of potential factors influencing SW structure were considered: region, geographic site, sea bottom type and dolphin population demography. From a genetic point of view, a differentiation exists between the bottlenose dolphin populations of the western and eastern Mediterranean regions 37 . Based on this knowledge, the whistles collected in the six sites were assigned to three regions as follows: (i) PC, AL and FI to the western Mediterranean Sea (WEST); (ii) CL and GC to the eastern Mediterranean Sea (EAST); (iii) LA to the southern Mediterranean Sea (SOUTH). 'Region' is therefore a factor with three levels (WEST, EAST, SOUTH) and accounts for the effect of the genetic distance on the structure of SWs. Whistles from LA were classified as SOUTH for two reasons: (i) the Strait of Sicily where Lampedusa is located is the transition zone between the island of Sicily and the African coast which separates the east and west Mediterranean Sea; (ii) there are no genetic data of the bottlenose dolphins off LA, but a previous study found that this population is acoustically closer to the Atlantic populations, compared to the western Mediterranean populations 35 .
The six populations studied live hundreds of kilometres apart and are assumed to be isolated from each other. 'Site' is a factor with six levels (PC, AL, FI, LA, GC, CL) that consider the effect of geographical isolation on the structure of SWs.
Different sea bottom type (substrate, habitat, and depth) can affect the acoustic environment and the sound propagation 38 , thus they can also influence SWs. Data on depth range and preferential habitat types of the studied populations were extracted by EMODnet platform (European Marine Observation and Data Network; https:// emodn et. ec. europa. eu/ en). The prevalent habitat types were classified based on the EUNIS 2021 classification system (Table 5). To evaluate the similarity between the six sites based on the sea bottom type, a hierarchical cluster analysis (using the ward linkage method, Euclidian distance) was performed. Thus, 'sea bottom' is a factor with three levels (H1, H2 and H3-see "Results" section) that accounts for the influence of depth range and habitat on SW structure.
At the end, we extrapolated six demographic variables from existing literature: mean and range of the population size, mean and range of the group size, residency pattern (prevalence of resident or transient individuals) www.nature.com/scientificreports/ and connection with adjacent populations (Table 5). A hierarchical cluster analysis (using the complete linkage method, Euclidian distance) was performed to evaluate the demographic similarity between the six populations. Thus, population demography is a factor with four levels (P1, P2, P3 and P4-see "Results" section) that accounts for the influence of population characteristics on SW structure.
Statistical analysis. First, the similarity in SW repertoire among regions, sites, sea bottom and population demography types were estimated. Thus, a non-metric multidimensional scaling ordination (nMDS) was produced from the sample similarity matrix. The mean values of the acoustic characteristics of each SW were used and data were fourth root transformed before calculating the Bray-Curtis similarity. Then, a one-way nonparametric similarity analysis (Anosim) was applied on the same matrix to test the null hypothesis that there was no difference in SWs between the levels of each factor (region, site, sea bottom and population demography). To perform this analysis, the functions metaMDS and anosim of the R package Vegan 53 were used.
When mean values of SWs are used as units of analysis, in order to respect the independence between samples, the magnitude of variability decreases. Thus, to investigate the association between the SW structure and each factor, all the SWs collected were used, and Generalized Linear Mixed Models (GLMMs) with Gaussian distribution were applied. The GLMMs are an extension of Generalized Linear Models that allow for the inclusion of random effects, by modelling the covariance structure that is generated by the grouping of data 54 . They are used when the data are not independent, such as when a variable is measured more than once from the same individuals 55 as the SWs.
Since the seven whistle characteristics were highly collinear, before running the GLMMs, a principal component analysis (PCA) was used to reduce them into three independent variables. The assumptions (linear relation between variables, sampling adequacy, and absence of outliers) were verified and some outliers were removed from the sample. The first three components (PC1, PC2, PC3) explained 84% of the total variance (see the "Results" section) and were retained because eigenvalues for the remaining four components were all < 1 (Kaiser's criterion). To perform PCA, the function prcomp of the R package Rstats 53 was used. Thus, with the aim to investigate the association between PC1, PC2, and PC3 (the response variables) and each factor separately, four groups of models were built using region, site, sea bottom and population demography as fixed terms, while SW-ID was considered as a random factor in the models. The best model was validated by means of graphical inspection of residuals (i.e., residuals vs. fitted values plots to verify homogeneity; Q-Q plots of the residuals for normality; and plots of residuals vs. each explanatory variable to check for independence). To perform the GLMMs, the function lme of the R package nlme 56 was used.

Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.