https://orcid.org/0000-0002-9210-7934
Figures
Abstract
In animals, the rhythmical properties of calls are known to be shaped by physical constraints and the necessity of conveying information. As a consequence, investigating rhythmical properties in relation to different environmental conditions can help to shed light on the relationship between environment and species behavior from an evolutionary perspective. Sciaena umbra (fam. Sciaenidae) male fish emit reproductive calls characterized by a simple isochronous, i.e., metronome-like rhythm (the so-called R-pattern). Here, S. umbra R-pattern rhythm properties were assessed and compared between four different sites located along the Mediterranean basin (Mallorca, Venice, Trieste, Crete); furthermore, for one location, two datasets collected 10 years apart were available. Recording sites differed in habitat types, vessel density and acoustic richness; despite this, S. umbra R-calls were isochronous across all locations. A degree of variability was found only when considering the beat frequency, which was temporally stable, but spatially variable, with the beat frequency being faster in one of the sites (Venice). Statistically, the beat frequency was found to be dependent on the season (i.e. month of recording) and potentially influenced by the presence of soniferous competitors and human-generated underwater noise. Overall, the general consistency in the measured rhythmical properties (isochrony and beat frequency) suggests their nature as a fitness-related trait in the context of the S. umbra reproductive behavior and calls for further evaluation as a communicative cue.
Citation: Picciulin M, Bolgan M, Burchardt LS (2024) Rhythmic properties of Sciaena umbra calls across space and time in the Mediterranean Sea. PLoS ONE 19(2): e0295589. https://doi.org/10.1371/journal.pone.0295589
Editor: Javier Tellechea, Facutlad de Ciencias, URUGUAY
Received: July 13, 2023; Accepted: November 22, 2023; Published: February 21, 2024
Copyright: © 2024 Picciulin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data and codes to reproduce the results are now available at the public Github repository: https://github.com/LSBurchardt/Rhythmic-properties-Sciaena-umbra
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Acoustic communication plays an important role in mediating intraspecific interactions in terrestrial and aquatic animals across a wide taxonomic spectrum [1]. In many marine animals, including marine mammals, fish, and crustaceans acoustic signals can mediate social interactions during reproduction, resource defense, group cohesion, anti-predator strategies, and individual recognition [2, 3]. A strong selection toward acoustic communication as a behavioral character providing fitness-related advantages has been recently proven across vertebrate evolution [4, 5]; in fishes, sound production has evolved independently over 30 times as a potential result of exaptation and potentially occurs in nearly two-thirds of actinopterygian species [5, 6].
In many taxa, biologically relevant information is encoded in species-specific signals and in their temporal organization. Vocal rhythms are known to encode individual or context-related information in terrestrial vertebrates such as primates, birds, and anurans [7–10], and in marine vertebrates such as Phocidae and whales [11–15]. The rhythmical properties of fish vocalizations are less documented, despite qualitative assessments indicating their existence and their potential key role in crucial behaviors [16–18]. A variable rhythm of sound production was found in the Lusitanian toadfish (Halobatrachus didactylus) [19]. Furthermore, an inter-specific variability of rhythmical properties was found between the three most common fish sound types in the Mediterranean basin [20], which have been recorded over a wide spatio-temporal range [17, 18, 21–28], i.e. the rochei’s snake blenny (Ophidion rochei) produces a random temporal succession of sounds, the brown meagre (Sciaena umbra) emits isochronous, i.e., metronome-like calls (the so-called regular pattern or R-pattern), while more complex rhythmical properties characterized the so-called ‘Kwa’ vocalizations attributable to Scorpaena spp. [20]. Among these species, S. umbra, one of the five Mediterranean Sciaenidae, was the first to be monitored in the Mediterranean basin thanks to Passive Acoustic Monitoring [16, 17, 23, 27, 29–31].
Sciaena umbra is a small-sized, sedentary, and gregarious demersal fish inhabiting rocky bottoms and Posidonia oceanica beds across the Mediterranean Sea [32–34]. S. umbra has been largely impacted by overfishing and is nowadays listed in Annex III (Protected Fauna Species) of the Barcelona Conventions and is classified as a vulnerable fish species by the International Union for Conservation of Nature [35, 36]. Like other Sciaenids [37–41], S. umbra emits drumming, multi-pulsed sounds during its reproductive period [16, 42] but only males are vocal given that the sonic apparatus is absent in females [31]. In S. umbra, the acoustic R-pattern occurs more frequently at dusk, ultimately leading to the formation of a chorus, [sensu [16, 43]]; the latter has been proven to be a reliable natural indicator of the species’ spawning behavior [17]. At sea, S. umbra also produces a variable number of sounds lacking any fixed repetition rate (the irregular pattern) [16].
Sciaena umbra sound type can be unequivocally identified thanks to its intra-sound temporal and spectral features (i.e. number of pulses per sound, pulse duration, pulse period, sound dominant frequency) in datasets recorded by different teams and with different equipment, in several Mediterranean regions [23, 27, 31]. Within this relatively restricted range of variation, S. umbra intra-sound features can be affected by environmental, anthropic, and biological factors: in fishes, pulse rate (number of pulses per unit of time) and sound fundamental frequency can be influenced by water temperature [44–46]. Pulse rate variations have been observed in different fish species [38, 47, 48]; in S. umbra, this appears also to be affected by the exposure to multiple boat passages, suggesting that this species can compensate human-generated disturbance by increasing sound production [30]. Further, the competition for acoustic resources within a vocal fish community has been proven to influence the spatial and diel pattern of emissions, as well as intra-sound features [27, 49]. In fact, S. umbra realized acoustic niche varies across locations among the same habitat type; despite this, this species appears less efficient than others (e.g. O. rochei) in allocating different temporal and spectral resources across sites in relation to the presence of other vocal competitors [27].
In animals, rhythmical call properties are known to be shaped by the physical constraints (environment, individual resources) and necessities to convey information [8]; investigating temporal periodicity in animal vocalization can provide insight into the relationship between environment and the species behavior from an evolutionary perspective. As a consequence, changes in the rhythmical properties of fish calls in relation to different environmental, biological, and anthropic conditions should be better studied. A previous study shows that S. umbra R-calls recorded in different locations and different years seem to be uttered with similar beats [20]; by expanding this original dataset to four different study areas and, for one location, to two different study periods, the present paper aims to describe the rhythmical patterns of S. umbra calls across a range of environmental conditions in the Mediterranean basin and discuss the implication of the observed results.
Materials and methods
Acoustic data collection
Acoustic data collection relates to previous projects [16, 17, 24, 27, 30, 50] carried out in four Mediterranean sites (Fig 1), which differed for several environmental and anthropic conditions. Data were recorded in Palma Bay Marine Reserve (Mallorca, Spain) and the Underwater Biotechnological Park of Crete (Greece). These two sites were located at 20 m depth over Posidonia oceanica meadows but Mallorca was characterized, overall, by a more intense anthropic presence at sea than the Underwater Biotechnological Park of Crete (Greece) (i.e. higher boat noise levels, [27]). Two other sites were considered: one in the Northern Adriatic Sea, i.e., the highly anthropized Venice inlets connecting the Venice lagoon with the sea, and the other in the Miramare Marine Protected Area, an urban MPA located in the Trieste Gulf (Italy); both sites were characterized by artificial rocky reefs close to a muddy bottom with a maximum depth of 18 and 30 meters, respectively. Data were collected in 2017 in Mallorca and Crete, in 2019 and 2020 in Venice. Two acoustic recordings were carried out in Trieste; the first in 2009 and the second ten years after (2019–2020). In each recording site, water temperature data were available (Table 1). Furthermore, the richness of the fish acoustic community (i.e. acoustic richness: number of different fish sound types) was 8 in Crete, 4 in Mallorca [27] and 2 in Venice and Trieste [17, 24, 50]. No permits were needed to conduct this research. Recordings were obtained by using the strictly observational and non-invasive technique of passive acoustic monitoring (PAM). PAM involves the deployment of acoustic data loggers in the marine environment; no animals nor animal samples were collected, and no animal procedure was carried out. Different predictions were initially made about the potential influence of these environmental conditions on Sciaena umbra rhythms (see S1 Table in S1 File).
Map imported from the public domain Natural Earth project, using ggplot::map_data(“world”) in R.
SNAPs dataloggers (Loggerhead Instruments, FL, USA), connected to an HTI-96-min hydrophone (sensitivity: 170 dB re 1V μPa-1; frequency range 2 Hz– 30 kHz; recording .wav files at 44.1 kHz, and 16 bits) were used for collecting acoustic data at Palma Bay Marine Reserve (Mallorca, Spain) and at the Underwater Biotechnological Park of Crete (Greece); here, the SNAPS were synchronized before deployments and were set for recording 1 min every 11 min (over the 24 h) at 44.1 kHz and 16 bits for one month [27]. In Venice (Italy), a pre-amplified GP1280 hydrophone (Colmar SRL, La Spezia, IT; sensitivity −170 dB re. 1 V Pa−1; frequency range, 5 Hz–90 kHz) connected to a Tascam Handy Recorder (Tascam, Montebello, CA) generating .wav files at 44.1 kHz and 16-bit depth was used; three single 10-h acoustic summer campaigns were conducted at 40 listening points along two summers [17, 24, 50]. In Trieste, recordings were collected (i) by using an acoustic data logger prototype provided with a pre-amplified TC 4013 hydrophone (Reson, A/S, Salangerup, Denmark; sensitivity −170 dB re 1 V μPa−1; frequency range 1 Hz to 170 kHz) connected to a Gemini iKey Plus Recording Device (10 min .wav files at 44.1 kHz and 16 bits) in 2009 (series of nocturnal 11-h recordings from June-September) and (ii) by using an underwater acoustic data logger SNAP settled for recording 1 min every 5 min (over the 24 h) for one week (unpublished data).
Acoustic and rhythm analysis
The acoustic samples were analyzed by audio and visual assessment using Raven Pro 64 1.4 (Bioacoustic Research Program, Cornell Laboratory of Ornithology, Ithaca, NY, USA; sound files down-sampled to 4 kHz, fast Fourier transform (FFT) size 256 points, 50% overlap, Hanning window).
In total, five datasets were considered (Table 1). Twenty 1-minute sequences of fish sounds were extracted per dataset. Fish sound sequences are defined as the temporally ordered repetition of at least three elements (one pulsatile sound surrounded by silence), which are temporally clearly isolated from preceding and following elements [20]. A sequence was confidently assigned to the same individual if all elements within the sequence were characterized by the same Signal-to-Noise Ratio (assuming the fish is stationary).To reduce the risk of pseudo-replications (i.e. sampling the same individual more than once), each 1-minute sequence was obtained by a non-consecutive recorded audio file in each dataset; therefore, the assumption was that each sequence was emitted by a different individual.
Ten additional 10-minute sequences of sounds have been extracted from the Trieste 2009 dataset. These sequences were analyzed in accord with the case of 1-minute sequences and the same individual calls were tracked along the file based on a similar Signal-to-Noise Ratio of the identified R-calls but, given the longer duration of the recordings, a sequence was considered “terminated” after 10 seconds of silence. In its turn this implies that, in some of these 10-min recordings, multiple sequences could be assigned to the same individual (Fig 2), thus allowing to characterize intra-individual rhythm variability. Unfortunately,10-minute recordings were available only for Trieste 2009; as a result, the here considered sample size is very small and presents strong temporal dependencies within one recording; therefore this analysis should be considered preliminary.
Two sequences from a 10-minute file can safely be assigned to the same individual due to a very similar Signal-to-Noise Ratio.
Per each identified sequence, any present sound (i.e., element, see [20] for details about terminology and procedure) was selected in the oscillogram from its onset to its offset using the selection function in Raven, which generates a selection table. In the Raven selection table, each selection made by the operator (i.e. each element) is labelled by its start and end times and lower and upper frequency. The Raven selection tables were then exported for subsequent analyses.
For rhythm analysis, the workflow developed by [15] was followed, as described by [20], to which we refer the reader for specific details. The interval between consecutive sounds (inter-onset-interval; IOI) of a sequence was calculated and the distribution of IOIs was visually represented using histograms. The coefficient of variation (CV; the ratio between standard deviation and sample mean) and the best-fitting frequency were calculated for each sequence. In particular, the best-fitting frequency describes a perfect underlying isochronous pattern, that best matches the element onsets in a sequence. It is reported as a frequency (Hz), in elements per second. An IOI beat of 5 Hz would indicate an underlying perfect isochronous pattern with 5 elements per second. This means an element every 200 ms best matches the onset of elements and can therefore be used as an adequate description of the temporal structure of the sequence. The custom-made app to run the rhythm analysis can be found at: https://github.com/LSBurchardt/R_app_rhythm/tree/master/RhythmAnalysis).
Environmental variables and statistical analysis
Different environmental, biotic and anthropogenic variables were taken into consideration due to their proven potential influence on S. umbra vocal behavior (see Introduction), i.e., water temperature, recording month, time of day, acoustic richness, and vessel density for different vessel types. To estimate vessel traffic in the study areas we used the Automatic Identification System (AIS) available data; generally speaking, these data include position and identification of ships of 300 gross tonnage and upwards, cargo ships of 500 gross tonnage and upwards and all passenger ships; smaller recreational boats typically do not use AIS. A square of 25 km2 around each monitoring location (center) was considered; the average AIS traffic density was extracted from the EMODnet Human Activities web portal (www.emodnet-humanactivities.eu). EMODnet maps are based on AIS data yearly purchased from Collected Localization Satellites (CLS) and ORBCOMM; here, density is expressed as hours per square kilometer per month in 1x1 km cells and it is provided for different ship types. Data were available by month of the year and were downloaded for the months corresponding to the study’s acoustic recordings. Six different vessel categories were considered separately: trade vessels, fishing vessels, recreational vessels, passenger vessels, “other” vessel types and all vessels combined.
Linear relationships were not verified for all variables, for example, because of many similar values and zero inflation, and therefore a linear approach was not suitable. General Additive Mixed Models were therefore used to quantify the influence of environmental, biotic and anthropogenic factors on the IOI beat. GAMs allowed both categorical and continuous variables to be included in the model. Continuous variables were smoothed. For details on the modelling approach see S2 Table in S1 File.
One outlier (3x standard deviations away from the mean for the IOI beat [Hz] parameter) was found and removed from the data before running the model. Model selection was based on the Akaike Information Criterion (AIC; [51]). In a step-wise process, parameters with the worst significance were removed from the model, until the best AIC was reached (S2 Table in S1 File). The statistical significance of the remaining factors was determined by a P-value of ≤0.05.The analysis was performed in R 4.2.3 (R Core Team, 2023).
Results
An isochronous rhythm was found in all locations. This is confirmed by a steep unimodal distribution of Inter-Onset-Intervals (Fig 3A), as well as by the low coefficient of variation within sequences (Fig 3B) for all locations and different years (mean CV: 0.18, min = 0.08, max = 0.53, SD = 0.09).
(a) Histogram of the duration of Inter-Onset-Intervals (IOIs) for the S. umbra sequences analyzed per location. (b) The coefficient of variation for the S. umbra sequences was analyzed per location. (c) IOI Beats [Hz] for the S. umbra sequences analyzed per location.
The best-fitting IOI beats were similar for most study areas, ranging from 0.016 and 0.52 Hz with an average of 0.36 Hz and a standard deviation of 0.08 Hz (Fig 3C). The data collected at the Venice inlets, however, represent an exception since the IOI beat was significantly faster (Fig 3C, 0.46 Hz, min = 0.32 Hz, max = 0.68 Hz, SD = 0.1 Hz) than all the other datasets, (ANOVA: F = 6.647, p = 0.0005***; Student’s t-Test with Bonferroni correction for multiple testing and Effect Size Cohens D: Trieste vs. Venice p = 0.0014, D = 1.18; Crete vs. Venice p = 0.0014, D = 1.23; Mallorca vs. Venice p = 0.004, D = 0.99).
Approximately 67% of the observed variation in the IOI beat could be statistically explained by eight variables according to GAMs(sample size N = 77; Adj. R² = 0.67, Deviance explained = 77.2%, GCV = 0.004, Scale est. = 0.003, Table 2 and S2 Table in S1 File, S1 Fig in S1 File). These eight variables are day of the recording, the interaction between day and month of the recording (and therefore the breeding season progression), the time of day [h], total vessel density, vessel density of trade ships, vessel density of fishing ships, acoustic diversity and temperature.
Model variables after model selection, for smoothed values, the F statistic and p-value are reported. For categorical values, the Estimate and p-value are reported. Significance levels: * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001.
Of the eight variables that contribute to explaining 67% of the variance in the sample, only four variables had a significant effect on the IOI beat [Hz]. These variables were the interaction between the month and day of the recording, trade and fishing vessel density and acoustic richness. The interaction between the month and day of the recording had a statistically significant influence at the .001 level on the IOI beat as determined by the GAMs model (S2 Table in S1 File). It seems, therefore, that IOI beat increases as the reproductive season proceeds(higher in August vs. July and June; Fig 4). Further, the S. umbra IOI beat seems to be influenced (i.e., speed up) by the number of sound types present in each location (i.e. acoustic richness, p = 0.00***). Finally, the model shows statistical significance for the vessel density of trading ships (p < 0.00***) and fishing ships (p < 0.00 ***). Conversely, the total vessel density does not have a significant effect, suggesting that specific vessel types might influence rhythm production more than others. Both, higher trading and fishing vessel density speed up the IOI beat (S2 Fig in S1 File and Fig 3). Vessel density distribution per location is shown in Fig 5. The temperature did not have a clear effect on the IOI beat (Fig 6).
Vessel types that were part of the final GAM model are indicated in red colors and stripes; vessel types cut from the model during model selection are indicated in yellow colors and dots, the total vessel density, which is also included in the final model is indicated as the red boundary. Locations are sorted from West to East, indicated are the month and the year of the data collection (e.g., Jul-17 indicates the vessel density for July 2017).
In contrast with the inter-site variability, within-area variability over long periods was not found: in fact, similar IOI beats were found in Trieste in 2009 and 2021 (Fig 7, Welch two sample t-test: t = -0.45, df = 34.99, p = 0.65, effect size Cohens D: 0.15).
Intra-individual variability in IOI beat was further considered on the basis of ten 10-minute files collected in Trieste in 2009; out of them, only three files had multiple sequences assigned to single individuals (see Materials and Methods for details): in one file, four vocal sequences were assigned to one individual, in two other files two vocal sequences were assigned per individual. Beat frequency range size, i.e. the difference between the fastest and slowest beat (Hz) calculated per each of the three individuals, was equal to 0.17, 0.006 and 0.08 Hz respectively. For comparisons, the beats range size in the whole 1-minute dataset (n = 80) was 0.33, at least two-fold bigger than the one measured within individual calls. As a consequence, IOI beat variability seems to be not driven by intra-individual but mostly by inter-individual differences. This conclusion however should be considered with care given the small sample size used for the analysis.
Discussion
Vocal activity is an important communication modality in fish: it reflects vital processes [41, 52–59] and conveys species-specific, individual characteristics and motivation states [60–63] under the pressure of the environmental constraints, such as interference by ambient noise patterns and sound transmission properties [64–66]. Here, S. umbra reproductive R-calls were recorded in four sites across the Mediterranean basin (from West to East: Mallorca, Venice, Trieste, Crete) and their rhythm was analyzed. Given the highly variable environmental conditions, ranging from artificial reefs on muddy bottoms to Posidonia oceanica meadows, as well as in local soundscape (due to different fish community composition and different human-generated noise inputs), differences were expected between locations. On the contrary, a highly stereotyped isochronous rhythm of around 0.36 Hz (0.36 elements per second) was found in all sites except for Venice, where the R-calls presented a faster beat. Additionally, an isochronous rhythm, with a consistent beat, was found in the S. umbra population recorded at the Marine Protected Area of Miramare (Trieste, Italy) about 10 years apart. In particular, the preliminary analysis on these 10-min R-sequences indicates that the beat variability seems to be not driven by within-individual but most likely by inter-individual differences, suggesting a consistency of this parameter at the individual level. If this is the case, a relatively stable population of individuals are expected to show a similar pattern of beat calls in time, as was the case of the Miramare Protected Area given the nature of protection of this area and the longevity of the species, whose individuals can live up to about 15–20 years [67].
All together the collected evidence indicates that the stereotypical R-calls produced by S. umbra males during the mating season tend to maintain a species-specific, consistent rhythm across space and time. In the following sections, different potential reasons for the unexpected consistency will be discussed as well as possible explanations connected to the observed variability.
Consistency of S. umbra R-calls isochrony property between and within locations
The rhythmic behavior may have evolved in many animal species in response to pressures for mate attraction as a powerful overall signal to attract females from further away [68]. In line with this theory, the observed consistency of isochronous rhythm in S. umbra calls recorded in different habitats leads to hypothesize that this temporal property of the calls is a fitness-related acoustic trait, i.e. an important trait for the individual and therefore the species’ reproductive success. Our hypothesis is supported by many considerations.
First, scientific evidence suggests that fish generate, perceive, and recognize acoustic regularity in time. Some fish sounds consist of pulsed sounds with a distinct temporal patterning [61]; intra-sound timing (number of pulses, pulse repetition rate) has been proven to encode relevant information [62, 69–72]. On the other hand, nothing/less is known about the role of inter-sound rhythmical patterns, which are the focus of this present study. Generally speaking, fish’s auditory system is well suited for temporal processing [73]; teleost temporal resolution abilities are defined in the order of a few milliseconds or even less, mostly in accordance with mammals and birds. This implies that some species at least can discriminate each pulse within a sound [74, 75]. Further, sound temporal patterns, as well as amplitude fluctuations and frequency content, are reflected at the level of the brainstem, so that fish’s auditory system can accurately encode the temporal patterns of conspecific sounds [76–78].
Temporal coding of relevant information is mostly expected in fish whose sounds consist of a series of pulses with little modulation in frequency; this is the case Sciaenids, with few exceptions such as the ones reported by [48, 79, 80]. In this family, pulse duration, pulse repetition rate and number of pulses per sound vary among species, leading to conclude that discrimination between species or individuals is based on call temporal characteristics [40, 41, 80–84]. The relevance of temporal features in Sciaenids is also due to the physiological characteristics of its sound-producing mechanism, which, in S. umbra, is present in males only: the sound-generating mechanism involves a pair of super-fast, extrinsic sonic muscles acting on the swim bladder [31]. In Sciaenid’s sounds, a single pulse corresponds to a unit of muscle contraction, with the cycle of each pulse giving the fundamental sound frequency (i.e. base frequency of which harmonic components exist) [85]. When considering peak frequency (i.e., the frequency with the highest energy), this shows a relatively low variability in relation to fish size in comparison to other fish taxa [86]. Overall, intra-sound feature consistency has been found in both space and time [31].
Finally, temporal features are known to be relevant during Sciaenid reproduction; the formation of seasonal reproductive aggregations at sea and call rate appear to be closely related [40]. Furthermore, the time in which the number of calls peaks (i.e., time of day) was found to be positively related to the timing and number of eggs collected at sea [47, 87, 88]. Captivity studies proved that call temporal features reflect spawning activity in many species: in Scienops ocellatus an increase in the number of calls, with longer calls and more pulses, has been recorded when spawning occurs [18, 89] and Cynoscion nebulosus is more likely to spawn when male fish call rate is higher [90]. In captivity, during spawning nights Argyrosomus regius, Sciaenops ocellatus and Umbrina cirrosa emit more calls per unit of time(versus prior to spawning); furthermore, calls are significantly longer and characterized by a higher number of faster-repeated pulses [18]. These same temporal features (i.e. longer sounds, made of a higher number of faster-repeated pulses) were found during the S. umbra chorus recorded at sea [17]. Given all this evidence, inter-sexual selection is expected to action S. umbra rhythm in the R-call pattern, supporting our original hypothesis.
Variability of beat frequency in the isochronous R-pattern between and within locations
Spatial variability was found in the beat frequency: S. umbra R-calls collected along the Venice inlets were characterized by a significantly higher beat frequency. It is interesting to notice that other parameters, such as the coefficient of variation between intervals are comparable to the other locations. The reason for a higher beat frequency is therefore not to be found as a bias of higher variability within sound sequences.
The Venice inlets allow navigation from the Venice lagoon to the Adriatic Sea and vice versa. Intrinsically, they are characterized by high traffic of cargo, passengers and fishing vessels, as well as conspicuous distribution of recreational and small-sized boats [91]. In turn, this substantially increases the inlets’ background noise, resulting in levels typical of harbor underwater areas [24, 92].We therefore hypothesized that this effect could be connected with the highly anthropized local soundscape.
A General Additive Mixed Model was considered to statistically analyze the influence of the potentially relevant environmental and anthropogenic factors on S. umbra beat frequencies. About 67 percent of the observed variability in the R-sound beat could be explained by considering four variables: the interaction between the day and month of the recording, the presence of other fish sound types, i.e., acoustic competition in the study area, and vessel density (here used as a proxy of fish exposure to human-generated noise) of trade ships as well as fishing boats. These results indicate a change in IOI in Venice recordings mostly due to the high vessel density of fishing and trade and different recording times compared to the other locations.
Sciaena umbra vocal activity is known to be characterized by seasonal and daily variations [16, 23, 31] so the influence of the recording timing on the R-pattern was expected, in contrast with intra-sound features [44] the water temperature seems to not play any role in the beat property of this calling. On the other hand, it is known that the noise generated by maritime traffic negatively affects marine fauna at various levels, including animals’ behavior and acoustics (reviewed by [93–95]. More specifically, multiple boat passages have been found to elicit an increase in the S. umbra pulse rate (pulses per minute) recorded at sea [30]. The faster rhythm of S. umbra sound production observed in recordings characterized by a dominant presence of vessel noise (i.e. in the Venice inlets) is consistent with the previous research since speeding up the R-pattern rhythm will necessarily result in a higher number of pulses produced in a given time.
Finally, the presence of inter-specific acoustic competition is suggested to play a potential role in influencing S. umbra R-rhythm beats. [27] proved a reduced inter-site variability when considering intra-sound temporal features, which could result from constraints associated with the species’ sound-producing mechanism [31]. The hypothesis is that species with a relatively reduced degree of sonic system plasticity, and consequently, with a relatively reduced degree of variability in intra-sound features resources allocation, resort to rhythmical variability to compensate for increased vocal competition. A faster R-rhythm would potentially compensate for inter-specific masking generating a signal redundancy, with a mechanism that is potentially similar to human-generated noise masking: in fact, many forms of vocal adjustment are gendered in animals in case of impaired communication including the production of redundant signals [96]. According to communication theory [97], vocal compensation is predicted when the balance between the benefits of successful communication and the costs of such a vocal change is positive, as is the case here, given the key role of R-pattern rhythm in S. umbra reproduction discussed in the previous paragraph.
Despite all these considerations, it has to be kept in mind that the present analysis was based on opportunistic acoustic data collected in the context of other projects (see Materials and Methods for details) and therefore, the presented results reflect the available datasets; on the other hand, not all variability observed in the beat data could be statistically explained by using the considered variables. This also means that more data are needed and/ or that other factors can potentially affect S. umbra R-rhythm as, for example, local population structure (i.e. the local fish density, male/female ratio, age of callers, and so on) and/or the single fish behavior during calling activity. Unfortunately, these features could not be quantified in the present study. In turn, this calls for further studies. In particular, keeping the influence of recording time within the season in mind, it would be very interesting to record data within one location throughout the season, to get a detailed view of rhythmic properties across the breeding cycle in S. umbra.
Conclusions
In conclusion, S. umbra R-calls showed an isochronous nature that was independent of environmental features indicating it is a fitness-related trait for the species. This is in accordance with the case of the Lusitanian toadfish (Halobatrachus didactylus); here, only males with high-quality conditions could sustain isochronous calling [19, 98] and, therefore, isochrony was suggested to act as an honest signal of male quality. In S. umbra, a degree of plasticity was found when considering the beat frequency, which varied spatially, likely influenced by the temporal-related dynamics along the species’ reproductive season, as well as by competition with other biological and human-generated acoustic sources. Overall, this paper shows that the assessment and comparison of vocal rhythm represents a critical aspect of fish communication, and calls for more investigations on this topic.
Supporting information
S1 File. Supplementary information containing details on theoretical considerations and modeling.
https://doi.org/10.1371/journal.pone.0295589.s001
(DOCX)
Acknowledgments
We would like to thank; i) Prof. Eric Parmentier for providing resources and equipment which allowed the collection of three datasets presented in this study; ii) Prof Stefano Malavasi, Dr. Riccardo Fiorin, Dr. Federico Riccato and Dr. Chiara Facca, Dr. Saul Ciriaco and Dr. Maurizio Spoto, Dr. Antonio Codarin for allowing the data acquisition with resources and field-work support in Venice and Trieste (Italy), respectively; iii) Dr. Ignacio A. Catalan and Dr. Josep Alós for field support in Mallorca (Spain); iv) Dr. Thanos Dailianis and Dimitris Androulakis for field support in Crete (Greece); v) Dr. Antonio Petrizzo for his help in extracting vessel traffic data; and vi) Dr. Kathryn Turnbull for insightful discussions on statistical treatment.
References
- 1.
Erbe C, Thomas J Exploring Animal Behavior Through Sound: Volume 1. Springer Nature Switzerland AG 2022. https://doi.org/10.1007/978-3-030-97540-1.
- 2.
Richardson WJ, Greene CR Jr, Malme CI, Thomson DH. Marine Mammals and Noise. 2013 New York: Academic Press.
- 3. Ladich F. Sound Communication in Fishes. 2015 Springer Vienna.
- 4. Chen Z, Wiens JJ. The origins of acoustic communication in vertebrates. Nature Communications.2020;11:369. pmid:31953401
- 5. Rice AN, Farina SC, Makowski AJ, Kaatz IM, Lobel PS, Bemis WE, et al. Evolutionary patterns in sound production across fishes. Ichthyology & Herpetology.2022; 110: 1–12. https://doi.org/10.1643/i2020172.
- 6. Parmentier E, Diogo R, Fine M.L. Multiple exaptations leading to fish sound production. Fish and fisheries.2017; 18(5): 958–966. https://doi.org/10.1111/faf.12217.
- 7. Norton P, Scharff C. Bird song metronomics’: isochronous organization of zebra finch song rhythm. Frontiers in Neuroscience.2016; 10: 309. pmid:27458334
- 8. Kotz S, Ravignani A, Fitch WT. The evolution of rhythm processing. Trends in Cognitive Science.2018; 22:896–910. pmid:30266149
- 9. Ravignani A. Rhythm and synchrony in animal movement and communication. Current Zoology. 2019; 65(1): 77–81. pmid:30697242
- 10. Burchardt LS, Norton P, Behr O, Scharff C, Knörnschild M. General isochronous rhythm in echolocation calls and social vocalizations of the bat Saccopteryx bilineata. Royal Society Open Science. 2019; 6:181076. https://doi.org/10.1098/rsos.181076.
- 11. Mathevon N, Casey C, Reichmuth C, Charrier I. Northern elephant seals memorize the rhythm and timbre of their rivals’ voices. Current Biology. 2017; 27:2352–6.e2. pmid:28736171
- 12. Ravignani A. Comment on temporal and spatial variation in harbor seal (Phocavitulina L.) roar calls from southern scandinavia [J Acoust Soc Am141, 1824–1834 (2017)]. The Journal of the Acoustical Society of America.2018a; 143:5. https://doi.org/10.1121/1.5021770.
- 13. Ravignani A. Spontaneous rhythms in a harbor seal pup calls. BMC Research Notes.2018b;11: 1–4. https://doi.org/10.1186/s13104-017-3107-6.
- 14. Schneider JN, Mercado IIIE. Characterizing the rhythm and tempo of sound production by singing whales. Bioacoustics. 2018; 1:1–18. https://doi.org/10.1080/09524622.2018.1428827.
- 15. Burchardt LS, Knörnschild M. Comparison of methods for rhythm analysis of complex animals’ acoustic signals. PLoS Computational Biology. 2020; 16:e1007755. pmid:32267836
- 16. Picciulin M, Calcagno G, Sebastianutto L, Bonacito C, Codarin A, Costantini M, et al. Diagnostics of nocturnal calls of Sciaena umbra (L., fam. Sciaenidae) in a nearshore Mediterranean marine reserve. Bioacoustics. 2013; 22:109–120. https://doi.org/10.1080/09524622.2012.727277.
- 17. Picciulin M, Fiorin R, Facca C, Malavasi S. Sound features and vocal rhythms as a proxy for locating the spawning ground of Sciaena umbra in the wild. Aquatic Conservation: Marine and Freshwater Ecosystems.2020, 30: 1299–1312. https://doi.org/10.1002/aqc.3340.
- 18. Bolgan M, Crucianelli A, Mylonas CC, Henry S, Falguiere JC, Parmentier E. Calling activity and calls’ temporal features inform about fish reproductive condition and spawning in three cultured Sciaenidae species. Aquaculture.2020b; 735243.
- 19. Vieira M, Amorim MCP, Fonseca PJ. Vocal rhythms in nesting lusitanian toadfish, Halobatrachus didactylus. Ecological Informatics. 2021; 63:101281. https://doi.org/10.1016/j.ecoinf.2021.101281.
- 20. Burchardt LS, Picciulin M, Parmentier E, Bolgan M. A primer on rhythm quantification for fish sounds: a Mediterranean case study. Royal Society Open Science.2021; 8(9):210494 https://doi.org/10.1098/rsos.210494.
- 21. Kéver L, Boyle KS, Parmentier E. Effects of seawater temperature on sound characteristics in Ophidion rochei (Ophidiidae). Journal of Fish Biology. 2015; 87: 502–509. https://doi.org/10.1111/jfb.12730.
- 22. Di Iorio L, Raick X, Parmentier E, Boissery P, Valentini-Poirier CA, Gervaise C. Posidonia meadows calling: A ubiquitous fish sound with monitoring potential. Remote Sensing in Ecology and Conservation. 2018; 4: 248–263. https://doi.org/10.1002/rse2.72.
- 23. Di Iorio L, Bonhomme P, Michez N, Ferrari B, Gigou A, Panzalis P et al. Spatio-temporal surveys of the brown meagre Sciaena umbra using passive acoustics for management and conservation. bioRxiv. 2020; https://doi.org/10.1101/2020.06.03.131326.
- 24. Picciulin M, Facca C, Fiorin R, Riccato F, Zucchetta M, Malavasi S. It is not just a matter of noise: Sciaena umbra vocalizes more in the busiest areas of the Venice tidal inlets Journal of Marine Science and Engineering.2021b; 9: 237. https://doi.org/10.3390/jmse9020237.
- 25. Bolgan M, Soulard J, Di Iorio L, Gervaise C, Lejeune P, Gobert S et al. Sea chordophones make the mysterious /Kwa/ sound: Identification of the emitter of the dominant fish sound in Mediterranean seagrass meadows. Journal of Experimental Biology. 2019; 222(11):jeb196931. pmid:31097606
- 26. Bolgan M, Gervaise C, Di Iorio L, Lossent J, Lejeune P, Raick X et al. Fish biophony in a Mediterranean submarine canyon. The Journal of the Acoustical Society of America.2020a; 147(4):2466–2477. pmid:32359295
- 27. Bolgan M, Di Iorio L, Dailianis T, Catalan IA, Lejeune P, Picciulin M, et al. Vocal fish community structure in Neptune seagrass meadows across the Mediterranean basin. Aquatic Conservation: Marine and Freshwater Ecosystems. 2022; 32:1–19, https://doi.org/10.1002/aqc.3764.
- 28. La Manna G, Picciulin M, Crobu A, Perretti F, Ronchetti F, Manghi M, et al. Marine soundscape and fish biophony of a Mediterranean marine protected area. PeerJ. 2021; 9:e12551. pmid:35003918
- 29. Bonacito C, Costantini M, Picciulin M, Ferrero EA, Hawkins AD. Passive hydrophone census of Sciaena umbra (Sciaenidae) in the Gulf of Trieste (Northern Adriatic Sea, Italy). Bioacoustics. 2002;12:292–294.
- 30. Picciulin M, Sebastianutto L, Codarin A, Calcagno G, Ferrero EA. Brown meagre vocalization rate increases during repetitive boat noise exposures: a possible case of vocal compensation The Journal of the Acoustical Society of America. 2012; 132: 3118–3124 https://doi.org/10.1121/1.4756928.
- 31. Parmentier E, Di Iorio L, Picciulin M, Malavasi S, Lagardère JP, Bertucci F. Consistency of spatiotemporal sound features supports the use of passive acoustics for long-term monitoring. Animal Conservation. 2018; 21: 211–220. https://doi.org/10.1111/acv.12362.
- 32.
Chauvet, Cl. Le corb ou brown meagre (Sciaena umbra—Linnaeus, 1758) quelques elements de sa biologie. In: Boudouresque CF, Avon M, Gravez V(eds.), Les Espèces Marines à Protéger en Méditerranée, GIS Posidonie Publ., France. 1991 pp. 229–235.
- 33.
Harmelin JG. Statut du corb (Sciaena umbra) en Méditerranée. In: Boudouresque CF, Avon M, Gravez V(eds.), Les Espèces Marines à Protéger en Méditerranée 1991, GIS Posidonie Publ., France, pp. 219–227.
- 34. Harmelin JG, Marinopoulos J. Recensement de la population de corbs (Sciaena umbra Linnaeus, 1758:Pisces) du Parc national de Port-Cros (Méditerranée, France) par inventaires visuels. Scientific Report of Port-Cros National Park. 1993; 15: 265–276.
- 35.
Bizsel C, Yokes B, Pollard D, Kara MH, Bariche M, Quignard JP. Sciaena umbra. The IUCN Red List of Threatened Species. Version 2014.3. 2011. Retrieved from www.iucnredlist.org.
- 36.
IUCN. The IUCN Red List of Threatened Species. Version 2019–2.
- 37.
Tavolga, WN. Sonic characteristics and mechanisms in marine fishes. In: Tavolga WN. (Ed) Marine Bio-acoustics New York: Pergamon Press, 1964; pp. 195–211.
- 38. Mok HK, Gilmore RG Analysis of sound production in estuarine aggregations of Pogonias cromis, Bairdiella chrysoura, and Cynoscion nebulosus (Sciaenidae). Bulletin of the Institute of Zoology, Academia Sinica. 1983;22:157–186.
- 39. Fine ML, Schrinel J The effect of loading on disturbance sounds of the Atlantic croaker Micropogonius undulatus: Air versus water. The Journal of the Acoustical Society of America. 2004;116:1271–1275.
- 40. Ramcharitar J, Gannon DP, Popper AN. Bioacoustics of the family Sciaenidae (croakers and drumfishes). Transactions of the American Fisheries Society. 2006; 135: 1409–1431.
- 41. Tellechea SJ, Martinez C, Fine ML, Norbis W. Sound production in whitemouth croaker (Micropogonias furnieri Sciaenidae) and relationship between fish size and disturbance call parameters. Environmental Biology of Fish. 2010, 89:163–172.
- 42. Codarin A, Wysocki LE, Ladich F, Picciulin M. Effects of ambient and boat noise in hearing and communication in three fishes living in a marine protected area (Miramare, Italy). Marine Pollution Bulletin. 2009; 58: 1880–1887. https://doi.org/10.1016/j.marpolbul.2009.07.011.
- 43. Cato DH. Marine biological choruses observed in tropical waters near Australia. The Journal of the Acoustical Society of America. 1978;64: 736−743.
- 44. Ladich F. Acoustic communication in fishes: Temperature plays a role. Fish and Fisheries.2018;19:598–612. https://doi.org/10.1111/faf.12277.
- 45. Connaughton MA, Taylor MH, Fine M. Effects of fish size and temperature on weakfish disturbance calls: implications for the mechanism of sound generation. Journal of Experimental Biology. 2000; 203:1503–1512. pmid:10751166
- 46.
Holt SA Intra- and interday variability in sound production by red drum (Sciaenidae) at a spawning site. Bioacoustics. 2002; 12:227–229.
- 47. Connaughton MA, Taylor MH. Seasonal and daily cycles in sound production associated with spawning in weakfish, Cynoscion regalis. Environmental Biology of Fishes. 1995; 42:233–240.
- 48. Locascio JV, Mann DA. Diel periodicity of fish sound production in Charlotte Harbor, Florida.Transactions of the American Fisheries Society. 2008; 137, 606–615.
- 49. Ruppé L, Clément G, Herrel A, Ballesta L, Décamps T, Kéver L et al. Environmental constraints drive the partitioning of the soundscape in fish. Proceedings of the National Academy of Sciences of the United States of America. 2015; 112(19):6092–6097. https://doi.org/10.1073/pnas.1424667112.
- 50. Picciulin M, Bolgan M, Facca C, Fiorin R, Riccato F, Malavasi S. Sound discrimination of two sympatric, threatened fish species allows for their in situ mapping. Aquatic Conservation: Marine and Freshwater Ecosystems. 2021, 31(8): 2103–2118.https://doi.org/10.1002/aqc.3581.
- 51.
Burnham KP, Anderson DR Model selection and multimodel inference: a practical information-theoretic approach. 2002. 2nd edn. Springer, Berlin.
- 52.
Fine ML, Winn HE, Olla BL Communication in fishes. In: Sebeok TA (Ed) How animals communicate. 1977. Indiana University Press, Bloomington, pp 472–518.
- 53. Lobel PS, Mann DA. Spawning sounds of the damselfish, Dascyllus albisella (Pomacentridae), and relationship to male size. Bioacoustics 1995; 6:187–198.
- 54. Lobel PS. Diversity of fish spawning sounds and the application of passive acoustic monitoring. Bioacoustics.2002; 12, 286–289.
- 55. Rountree RA, Gilmore RG, Goudey CA, Hawkins AD, LuczkovichJ, Mann DA. Listening to fish: applications of passive acoustics to fisheries science. Fisheries.2006; 31:433–446.
- 56. Ladich F, Myrberg AA Jr. Agonistic behavior and acoustic communication. In: Ladich F, Collin SP, Moller P, Kapoor BG (Eds) Communication in fishes. Science, Enfield 2006, pp 121–148.
- 57. Myrberg AA, Lugli M. Reproductive behavior and acoustical interactions. In Ladich F (Ed) Communication in fishes. Science Publishers, Enfield2006, pp 149–176.
- 58. Luczkovich JJ, Pullinger RC, Johnson SE, Sprague MW. Identifying sciaenid critical spawning habitats by the use of passive acoustics. Transactions of the American Fisheries Society.2008; 137: 576–605. https://doi.org/doi:10.1577/T05-290.1.
- 59. Amorim MCP, Vasconcelos RO, Fonseca PJ. Fish sounds and mate choice. In: Ladich F, editor. Sound communication in fishes Springer Vienna. 2015. pp. 1–33.
- 60. Amorim MCP, Fonseca PJ, Almada VC. Sound production during courtship and spawning of Oreochromis mossambicus: male–female and male–male interactions. The Journal of Fish Biology.2003; 62:658–672. https://doi.org/10.1046/j.1095-8649.2003.00054.x.
- 61. Amorim MCP. Diversity of sound production in fish. In: Ladich F, Collin SP, Moller P, Kapoor BG, editors. Communication in fishes. Science, Enfield. 2006. pp. 71–105.
- 62. Bass AH, McKibben JR. Neural mechanisms and behaviors for acoustic communication in teleost fish. Progress in Neurobiology.2003; 69(1):1–26. pmid:12637170
- 63. Simões JM, Duarte IG, Fonseca PJ, Turner GF, Amorim MCP. Courtship and agonistic sounds by the cichlid fish Pseudotropheus zebra. The Journal of the Acoustical Society of America. 2008;124:1332–1338. https://doi.org/10.1121/1.2945712.
- 64. Lugli M. Sounds of shallow water fishes pitch within the quiet window of the habitat ambient noise. The Journal of Comparative Physiology A. 2010; 196:439–451. pmid:20443010
- 65. Lugli M, Yan HY, Fine ML. Acoustic communication in two freshwater gobies: the relationship between ambient noise, hearing thresholds and sound spectrum. The Journal of Comparative Physiology A. 2003;189:309–320. pmid:12665991
- 66. Amorim MCP, Vasconcelos RO, Bolgan M, Pedroso SS, Fonseca PJ. Acoustic communication in marine shallow waters: Testing the acoustic adaptive hypothesis in sand gobies. Journal of Experimental Biology. 2018;221(22):jeb183681. pmid:30171096
- 67. La Mesa ML, Colella S, Giannetti G, Arneri E. Age and growth of brown meagre Sciaena umbra (Sciaenidae) in the Adriatic Sea. Aquatic Living Resources.2008; 21:153–161. https://doi.org/10.1051/alr:2008029.
- 68. Ravignani A. Chronometry for the chorusing herd: Hamilton’s legacy on context-dependent acoustic signalling–a comment on Herbers (2013) Biology Letters. 2014. https://doi.org/10:20131018.
- 69. Myrberg AA, Spires JY. Sound discrimination by the bicolor damselfish, Eupomacentrus partitus. Journal of Experimental Biology. 1972; 57: 727–735. https://doi.org/10.1242/jeb.57.3.727.
- 70. Spanier E. Aspects of species recognition by sound in four species of damselfish, genus Eupomacentrus (Pisces: Pomacentridae). Zeitschrift fur Tierpsychologie.1979; 54:301–316. https://doi.org/10.1111/j.1439-0310.1979.tb00691.x.
- 71.
Myrberg AA, Spanier E, Ha SJ. Temporal patterning in acoustic communication. In: Reese ES, Lighter FJ (Eds.), Contrasts in Behaviour. John Wiley and Sons, New York, 1979, pp. 137–179.
- 72. Hawkins AD, Rasmussen KJ. The calls of Gadoid fish. Journal of the Marine Biological Association UK.1978;58:891–911.https://doi.org/10.1017/S0025315400056848.
- 73. Vasconcelos RO, Fonseca PJ, Amorim MCP, Ladich F. Representation of complex vocalizations in the Lusitanian toadfish auditory system: evidence of fine temporal, frequency and amplitude discrimination. Proceedings of the Royal Society B: Biological Sciences. 2011; 278:826–834. pmid:20861044
- 74. Popper AN. Auditory threshold in the goldfish (Carassius auratus) as a function of signal duration. The Journal of the Acoustical Society of America. 1972, 52:596–602.https://doi.org/10.1121/1.1913150.
- 75. Wysocki LE, Ladich F. Can fishes resolve temporal characteristics of sounds? New insights using auditory brainstem responses. Hearing Research.2002; 169: 36–46. pmid:12121738
- 76. Wysocki LE, Ladich F. The representation of conspecific sounds in the auditory brainstem of teleost fishes. Journal of Experimental Biology. 2003;206: 2229–2240. pmid:12771172
- 77. Wysocki LE. Detection of communication sounds. In: Ladich F, Collin SP, Moller P, Kapoor BG (Eds) Communication in Fishes. Enfield, NH: Science Publishers, 2006; pp. 175–203.
- 78.
Bass AH, Ladich F. Vocal-acoustic communication: From neurons to brain. In:WebbJF , FayRR , Popper AN (Eds.). Fish bioacoustics. New York, NY: Springer Science+Business Media, LLC, 2008,pp. 253–278.
- 79. Tellechea JS, Norbis W. Olsson D, Fine ML. Calls of the black drum (Pogonias cromis: Sciaenidae):geographical differences in sound production between northern and southern hemisphere populations. Journal of experimental zoology. Part A, Ecological genetics and physiology. 2011, 315: 48−55.
- 80. Tellechea JS, Izquierdo S, Perez W, Norbis W. Sound variation by hypertrophy and atrophy sonic muscle in the male southern black drum (Pogonias courbina). The Journal of the Acoustical Society of America. 2022, 152(1): 429–436. https://doi.org/10.1121/10.0012690.
- 81. Saucier MH, Baltz DMSpawning site selection by spotted seatrout, Cynoscion nebulosus, and black drum, Pogonias cromis, in Louisiana. Environmental Biology of Fishes. 1993; 36:257–272.
- 82. Ladich F, Fine ML (2006). Sound-generating mechanisms in fishes: a unique diversity in vertebrates. In: Ladich F, Collin SP, Moller P, Kapoor BG (Eds) Communication in fishes. Science, Enfield2006, pp 3–43.
- 83. Mok HK, Yu HY, Ueng JP, Wei RC. Characterization of sounds of the blackspotted croaker Protonibea diacanthus (Sciaenidae) and localization of its spawning sites in the estuarine coastal waters of Taiwan. Zoological Studies 2009; 48:325–333.
- 84. Tellechea JS, Norbis W. Sexual dimorphism in sound production and call characteristics in the striped weakfish Cynoscion guatucupa. Zoological Studies.2012, 51:946–955.
- 85. Lagardère JP, Mariani A. Spawning sounds in meagre Argyrosomus regius recorded in the Gironde estuary, France. The Journal of Fish Biology.2006; 69:1697–1708. https://doi.org/10.1111/j.1095-8649.2006.01237.x.
- 86.
Parmentier E, Fine ML. Fish sound production: insight. In Vertebrate sound production and acoustic communication: Suthers R, Tecumseh F, Popper AN, Fay RR (Eds). New York: Springer. 2016, pp. 19–49.
- 87. Luczkovich JJ, Sprague MW, Johnson SE, Pullinger RC. Delimiting spawning areas of weakfish, Cynoscion regalis (Family Sciaenidae) in Pamlico Sound, North Carolina using passive hydroacoustic surveys. 1999. Bioacoustics 10:143–160. https://doi.org/10.1080/09524622.1999.9753427.
- 88. Tellechea JS, Bouvier D, Norbis W. Spawning sounds in whitemouth croaker (Sciaenidae): seasonal and daily cycles. Bioacoustics. 2011a, 20: 159–168. https://doi.org/10.1080/09524622.2011.9753641.
- 89. Montie EW, Kehrer C, Yost J, Brenkert K, O’Donnell T, Denson MR. Long-term monitoring of captive red drum Sciaenops ocellatus reveals that calling incidence and structure correlate with egg deposition. The Journal of Fish Biology. 2016; 88: 1776−1795.
- 90. Montie EW, Hoover M, Kehrer C, Yost J, Brenkert K, O’Donnell T et al. Acoustic monitoring indicates a positive relationship between calling frequency and spawning in captive spotted seatrout (Cynoscion nebulosus) PeerJ. 2017;4:e1656v1 https://doi.org/10.1111/jfb.12938.
- 91. Pranovi F, Anelli Monti M, Caccin A, Colla S, Zucchetta M. Recreational fishing on the west coast of the northern Adriatic Sea (Western Mediterranean) and its possible ecological implications. Regional Studies in Marine Science. 2016; 3: 273–278. https://doi.org/10.1016/j.rsma.2015.11.013.
- 92. Picciulin M, Zucchetta M, Facca C, Malavasi S. Boat-induced pressure does not influence breeding site selection of a vulnerable fish species in a highly anthropized coastal area. Marine Pollution Bulletin. 2022; 180: 113750. pmid:35597000
- 93. Di Franco E, Pierson P, Di Iorio L, Calò A, Cottalorda JM, Derijard B et al.Effects of marine noise pollution on Mediterranean fishes and invertebrates: a review. Marine Pollution Bulletin. 2020; 159: Article 111450. pmid:32892911
- 94. Duarte CM, ChapuisL , Collin SP, Costa DP, Devassy RP, Eguiluz VM. The soundscape of the Anthropocene. Ocean Science. 2021; 371. https://doi.org/10.1126/science.aba4658.
- 95. Kunc HP, McLaughlin KE, Schmidt R Aquatic noise pollution: implications for individuals, populations, and ecosystems. Proceedings of the Royal Society B: Biological Sciences.2016;283:20160839. https://doi.org/10.1098/rspb.2016.0839.
- 96. Tyack PL. Implications for marine mammals of large scale changes in the marine acoustic environment. Journal of Mammalogy.2008;89:549–558.https://doi.org/10.1644/07-MAMM-S-307R.1.
- 97.
Shannon CE, Weaver W. The Mathematical Theory of Communication, University of Illinois Press, Urbana, IL, 1949,pp. 125.
- 98. Amorim MCP, Simões JM, Mendonça N, Bandarra NM, Almada VC, Fonseca PJ. Lusitanian toadfish song reflects male quality. Journal of Experimental Biology. 2010; 213(17):2997–3004. pmid:20709928