Although sea turtles have long been considered deaf and silent, recent studies have confirmed their ability to hear and produce sounds. A study revealed that juvenile green sea turtles Chelonia mydas, can produce ten distinct types of sounds, with some of them potentially involved in social communication. To investigate the potential biological function of these sounds, we used video recordings associated with acoustic recordings of 23 free-ranging juvenile green turtles and we examined the co-occurrences of sounds with behaviours or external events. Our study revealed that most of the sounds were produced when the sea turtles were resting or swimming. However, four sound types were produced in more specific contexts. Long sequences of rumbles were recorded after sunset and mainly during resting. All these rumbles appear to have been produced by several individuals recorded simultaneously, suggesting that rumbles may be used for social interactions. The frequency modulated sound was highly associated with scratching behaviour and thus may express a positive emotion. Finally, the long squeak was produced by individuals in the presence of human beings, and the grunt was occasionally heard when green turtles were vigilant or approaching a conspecific. These two sounds may be the first evidence of an alarm or warning signal for intra-specific communication in green turtles. Our results mark a significant milestone in advancing the understanding of sound production in the behavioural ecology of sea turtles. Further investigations are now required to test the hypotheses suggested by our findings, which could significantly contribute to their conservation.
Research Article
First investigation on the behavioural contexts of underwater sound production in juvenile green turtles, Chelonia mydas
https://doi.org/10.21203/rs.3.rs-4674363/v1
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Behaviour
Chelonia mydas
Chelonians
Juveniles
Underwater sound
The underwater sound production in Chelonioidea is yet not well documented. We investigated the contexts of sound production in juvenile green sea turtles. Our results show for the first time that juvenile green turtles produced at least 4 identified sounds in specific contexts. This is a crucial step, as it will provide a solid basis for understanding the behaviour of green sea turtles and thus improving current conservation methods. To date, the lack of knowledge on sea turtle behavioural ecology and acoustic communication hinders the implementation of mitigation measures to effectively reduce mortality and disturbance from human activities. Our findings offer the possibility of using species-specific sounds in a deterrent system to prevent them from becoming entangled in fishing nets, with the aim of reducing by-catch.
Sharing information among animals plays a crucial role in social interactions such as finding a mate, interacting within social partners (particularly during foraging or alerting to the presence of a predator), and providing care to offspring (Bradbury & Vehrencamp, 1998). There are many ways of conveying information, with the most widely recognised and studied being the transmission via acoustic signals. Among vertebrates, sound production has been well studied in birds, mammals and anurans but much less is known about reptiles which produce sounds relatively rarely (Jerem & Mathews, 2021; Russell & Bauer, 2021; Vergne et al., 2009). Studies on chelonian sound production are limited, and almost absent in sea turtles.
In freshwater turtles, studies have presented evidence of sound production (in air and under water) and acoustic communication (Ferrara et al., 2017; Ferrara, Vogt, & Sousa-Lima, 2013; Ferrara, Vogt, Giles, et al., 2013) suggesting that the produced sounds could be involved in social behaviour (e.g. Arrau turtle Podocnemis expansa, Ferrara, Vogt, Harfush, et al., 2014; Ferrara, Vogt, Sousa-Lima, et al., 2014; Oblong turtle Chelodina oblonga, Giles et al., 2009). These findings marked the initial phase of revising prevailing concepts regarding chelonian sound production and social behaviours.
Until recently, sea turtles were considered a ‘silent group’ (Campbell & Evans, 1972). Additionally, due to the absence of a visible tympanum in sea turtles (possessing only the middle and inner ear without an external ear, Bartol et al., 2003), they have been considered as deaf. However, behavioural and electrophysiological studies have shown their hearing abilities in air and under water (DeRuiter & Larbi Doukara, 2012; Lavender et al., 2014). Hearing seems to be well adapted to their underwater environment, as the subtympanic fatty tissue of sea turtles has a density similar to that of water, which reduces sound attenuation and optimises sound transmission to the inner ear (Ketten, 2008). Juvenile and hatchling sea turtles have varying underwater hearing abilities across different species (e.g. juvenile kemp’s ridley sea turtles Lepidochelys kempii, Bartol & Ketten, 2006; juvenile loggerhead sea turtles Caretta caretta, Lavender et al., 2014; hatchling leatherback sea turtles Dermochelys coriacea, Piniak et al., 2012). For example, the underwater hearing sensitivity of juvenile green sea turtles Chelonia mydas, ranges between 50 and 1600 Hz with maximum sensitivity between 200 and 400 Hz (Piniak et al., 2016). Based on biased beliefs, the sound production of sea turtles has been neglected and, as a result, never thoroughly investigated.
Airborne sound production was observed and characterised in nesting female sea turtles, such as the leatherback (Carr, 1952; Cook & Forrest, 2005; Lutcavage & Lutz, 2017; Mrosovsky, 1972). Additionally, sound production was demonstrated in embryos and hatchlings of several sea turtles species, such as hawksbill Eretmochelys imbricata (Monteiro et al., 2019), kemp’s ridley (Ferrara et al., 2019), green turtle (Ferrara, Mortimer, & Vogt, 2014; Ferrara, Vogt, Giles, et al., 2013), olive ridley Lepidochelys olivacea (Ferrara, Vogt, Giles, et al., 2013; McKenna et al., 2019), and leatherback sea turtle (Ferrara, Vogt, Giles, et al., 2013; Ferrara, Vogt, Harfush, et al., 2014). However, the biological function and sound production mechanisms are not well understood, and further studies are required to draw conclusions regarding the occurrence of acoustic communication in these species (McKenna et al., 2019). The production of sounds in crocodilian hatchlings has also been studied such as Spectacled caiman Caiman crocodilus and American alligator Alligator mississipiensis (Britton, 2001; Garrick & Garrick, 1978; Higgs et al., 2002; Vergne et al., 2009). In the Nile crocodile Crocodylus niloticus, it was shown that sounds produced by the embryos can synchronise hatching, aiding their emergence from the nest (Vergne & Mathevon, 2008). Nevertheless, the sounds produced by sea turtle embryos do not appear to synchronise hatchlings in olive ridley, green and leatherback sea turtles (McKenna et al., 2019; Nishizawa et al., 2021).
The first underwater sounds described in juvenile green turtles (Charrier et al., 2022) exhibited similarities with the general acoustic structure of Oblong turtle (Giles et al., 2009) and Kemp’s ridley sea turtle hatchlings (Ferrara et al., 2019) underwater sounds. However, both frequency and temporal features varied across these studies, attributable to size differences between hatchlings and juveniles. In each of the three species studied (i.e. Oblong turtle, Kemp’s ridley and green sea turtle), sound production ranged from simple pulses to more complex sounds endowed with a harmonic structure and a frequency modulation pattern. In Charrier et al. (2022), control recordings (without green turtles nearby) showed that while some recorded sounds from the surrounding environment were similar in their general structure, the frequency and/or temporal characteristics were different. These variations suggest that some of the recorded sounds were likely produced by other marine species, such as crustaceans or fishes, present in the green turtle's habitat. Although the biological function of sound production is not fully understood, Charrier et al. (2022) demonstrated that squeaks were individual-specific and could potentially be used for individual recognition. Furthermore, all recorded individuals produced sounds at frequencies falling within the hearing range of green turtles, suggesting potential implications for social communication. Other sounds in their repertoire, such as the rumble, the toc and the Frequency Modulated Sound (FMS) felt within the best auditory sensitivity of juvenile green turtles (the frequency of the highest energy of these sounds ranging from 200 to 400 Hz), but they did not show any individual identity. These sounds may thus be involved in intraspecific communication, but are probably not involved in individual recognition processes.
Even if the acoustic characteristics of the sounds produced by juvenile green turtles have been described, the behavioural contexts of such sound production remain unclear (Charrier et al., 2022). The aim of the present study was to investigate the behavioural contexts or external events during which free-ranging juvenile green turtles, equipped with biologgers (multi-sensor tags associated with video camera-hydrophone), produced sounds, thereby providing insights into their associated biological functions. The analysis of their behaviour, using a deep learning algorithm (Jeantet et al., 2021) and external events using video camera data, allowed us to automatically identify main behavioural states of green turtles in synchrony with their acoustic production.
Study Site and Data Collection from Free-Ranging Green Turtles
This study was carried out from May 2018 to May 2022 in Grande Anse d’Arlet (14°30.158' N, 61°5.271' W), Anse Noire (14°31.683' N, 61°5.320' W) and Anse Dufour (14°31.562'N, 61°5.425'W), Martinique island (French West Indies). Juvenile green turtles were captured along the coast by freedivers at various sites with depths up to 25 m. The capture of each turtle was performed by up to three freedivers when the turtle was static, i.e. resting or feeding on the sea floor. The freediver silently dived towards the turtle to avoid detection and once close enough and above the animal, seized the nuchal shell and pygales plate. The freediver then positioned the turtle against his chest with the hind flippers against his breastplate and rose to the surface. A second diver held the fore flippers and helped to lift the turtle on to the deck of the boat for measurements and tagging (Bonola et al., 2019; Nivière et al., 2018). Once on a boat, each individuals was identified by scanning its Passive Integrated Transponder (PIT) or tagged with a new PIT if it was unknown, as described in Siegwalt et al. (2020) and Lelong et al. (2024).The cam device CATS (Customized Animal Tracking Solutions, Germany) was attached to the carapace using four suction cups, as described in Jeantet et al. (2020). This suction cup attachment method avoid the use of glue on the carapace. Air was manually expelled from the cups, which were held in place by the use of a galvanic timed-release system, used to limit the duration of the deployment. The dissolving of galvanic timed-release system by seawater and the slightly positive buoyancy of the device (23.3 × 13.5 × 4 cm for 785 g.) led to the remote release of the device several hours to two days later, thus avoiding the need to recapture the turtle and minimising the stress associated with a second capture to recover the device. A CATS cam device included a video-recorder (1920 × 1080 pixels at 30 frames.s−1, viewing angle of 100°) combined with a tri-axial accelerometer, a tri-axial gyroscope, a tri-axial magnetometer, TDR, hydrophone (HTI 96 min, frequency response: 2 Hz to 30 kHz, sensitivity: −165 dB re 1 V/mPa), thermometer, luminosity and a GPS tracker. Devices were recovered using a goniometer (RXG-134, CLS, France) by geolocation of an Argos SPOT-363A tag (MK10, Wildlife Computers Redmond, WA, USA), glued to the CATS device. Due to low light conditions after sunset, the cameras were programmed to record from 05:00 to 19:00. CATS Cam devices were deployed on 23 juvenile green turtles (11 in 2018, 10 in 2021 and 5 in 2022).
All data were collected in the wild from live animals, thus it was not possible to use blinded methods.
Acoustic Recordings and Analyses
CATS cams recorded in mono at a frequency sampling rate of 24 kHz (16 bit). Listening and spectrogram labelling of sound files were performed using Avisoft SASlab Pro (Avisoft Bioacoustics, Version 5.3.01, 14 May 2022). A label starts at the beginning of the sound, the behaviour or external event and stops at its end. To improve visualisation of the sounds on spectrograms (Hamming, Fast Fourier Transform [FFT] size 1024 pts), all sound files were down sampled at 22 kHz, as there was no energy for frequencies above 10 kHz.
Sounds, Behaviours and External Events Labelling
Eleven sound types were considered in the present analysis (Table1). Ten of which (mono, doublet, triplet, multipulse, toc, croak, rumble, FMS, short squeak and long squeak) have been described in the juvenile green turtle sound repertoire in Charrier et al. (2022). They were classified into four main sound categories: pulse, Low Amplitude Call (LAC), FMS and squeak. The grunt is a new sound type and a new sound category not previously described.
We defined six main behavioural categories (feeding, gliding, resting, scratching, surface activity and swimming, Table 1). For data collected in 2018, behaviours were visually analysed and defined using the software VLC media player (Version 3.0.18 Vetinari, 13 October 2022; VideoLAN, Paris, France). For the analysis of data collected in 2021 and 2022, we used a deep learning algorithm coded in custom Python scripts, to automatically identify the behaviours from the accelerometer, gyroscope and depth (Jeantet et al., 2021). The algorithm was trained on the behaviours dataset labelled from the visual analysis of the 2018 video files. Details on this algorithm can be found in Jeantet et al. (2021). The behaviours obtained with this method included different variants and were pooled into five behavioural categories (feeding, gliding, resting, scratching, and swimming). In the same manner, the sixth behaviour, surface activity included all activities (e.g. breathing, basking) occurring between 0 and 0.3 m depth (depth data was collected from the CATS logger, sampled every second). With this method, surface activity is not exclusive, and occurs at the same time as the turtle is swimming, like in the study by Jeantet et al. (2020).
In addition to monitoring the behaviour of the juvenile green turtles, three external events (anthropogenic noise and presence of conspecifics or humans, Table 1) were recorded to assess their potential impact on sound production of the turtles. All video files were processed using the software VLC media player so that the timing of each external event of interest was recorded, using slow motion and frame-by-frame modes if necessary. External events including the presence of a conspecific or a human being were considered when at least another turtle other than the equipped one, or at least one human being, was visible in the camera field (with or without direct interaction). If the conspecific or the human being left the camera's field of view and then can be observed again at a later stage (i.e., beyond the next two minutes), then the label ended at the end of the first observation, and the second observation was considered as a new observation and a new label was thus created. Indeed, during the "blind period", we cannot be sure that the same individual stayed in the vicinity of the equipped green turtle. Finally, the anthropogenic noise events (i.e. boat motor noise) were detected on the audio recordings.
Since the different loggers could start in a non-synchronous way, the behaviours detected from the accelerometer data using the automatic classification algorithm (Jeantet et al., 2021) were synchronised with the sound data using external events (e.g., breathing at surface).
|
Sounds |
|
Behaviour |
|
External Events |
||||
|
Pulse |
1. Mono |
|
Grunt |
7. Grunt |
I. Feeding |
A. Anthropogenic noise |
||
|
2. Doublet |
|
LAC |
8. Croak |
II. Gliding |
B. Conspecific |
|||
|
3. Triplet |
|
9. Rumble |
III. Resting |
C. Human |
||||
|
4. Multipulse |
|
Squeak |
10. Long Squeak |
IV. Scratching |
|
|||
|
5. Toc |
|
11. Short Squeak |
V. Surface activity |
|
||||
|
FMS |
6. FMS |
|
|
|
VI. Swimming |
|
||
Table 1 List of the labels for the sounds, behaviours and external events.
Dummy Coding Procedure
To assess the behavioural context of underwater sound production in juvenile green turtles, we investigated the co-occurrence of sounds with behaviours and external events. To do this, each recording day and all label durations were split into 1-second bin (1-s bin), using a "Dummy coding" method coded in R. Basically, the occurrence of a sound or a behaviour or an external event at a given time took the value of 1. If absent, it took the value of 0. This method quantified the number of 1-s bins of a given sound that co-occurred with the 1-s bins of a given behaviour or an external event. Only sounds occurring during a behaviour or an event were considered. From this, two types of average were calculated and are presented in Table 2
First, the average number of 1-s bins of sounds co-occurring with behaviours or external events was calculated, following formula (1), by dividing the total duration of 1-s bins of a given sound (i) co-occurring with a behaviour or an external event (j) by the number of individuals producing that sound (i):
Secondly, the average percentages of 1-s bins of sounds co-occurring with behaviours or external events was calculated, following formula (2), by averaging the individual total duration of the 1-s bins (i) co-occurring with a behaviour or an external event (j) divided by the total individual durations of the 1-s bins of this sound (i), among the individuals who produced this sound (i):
Since a sound can occur during 1-s bins of two different external events, it will be included in both external events as they are not mutually exclusive (e.g., boat noise can occur in the presence of a human being). To estimate the sound time budget (Fig. 1) and the behavioural time budget (Fig. 2) of green turtles, we calculated the average of the percentage of 1-s bins for each individual's sound production and behaviours. All dummy coding procedures were performed in RStudio (version 4.3.0, 2023-04-21).
The behavioural contexts of underwater sound production of 23 juvenile green turtles were investigated. A total of 251h of recorded tag data (sound, video and accelerometer) were analysed. From this dataset, 21 594 sound 1-s bins (representing approximately 6 hours of sound recordings), 905 650 behaviour 1-s bins (~251 hours) and 108 351 external event 1-s bins (~30 hours) were identified (see supplementary data).
Sound Time Budget
Among the eleven recorded sounds, the rumble was the most produced. It accounted for 71% of total sound production (Fig. 1a). The remaining 29% of the total sound production was distributed among the other ten sound categories. Among these, the pulse and the squeak categories were the most represented, each accounting for 12.6% and 12.7% of the total sound production respectively (Fig. 1b). In contrast, the FMS category represented only 0.9% of the total production.
The pulse category (including Mono, Doublet, Triplet, Multipulse and Toc) was weakly produced.
The mono (occurring in 347 1-s bins from 18 individuals, Table 2a) accounted for 1.6% of the total sound production and was produced at 4.5% per individual on average (range: 0 – 14.3%; n=23).
The doublet (occurring in 441 1-s bins from 21 individuals, Table 2a) accounted for 2% of the total sound production and was produced at 5.4% per individual on average (range: 0 – 21.1%; n=23).
The triplet (occurring in 828 1-s bins from 18 individuals, Table 2a) accounted for 3.8% of the total sound production and was produced at 7.3% per individual on average (range: 0 – 35.4%; n=23).
The multipulse (occurring in 95 1-s bins from 12 individuals, Table 2a) accounted for 0.4% of the total sound production and was produced at 1.1% per individual on average (range: 0 – 7.9%; n=23).
The toc (occurring in 1044 1-s bins from 18 individuals, Table 2a) accounted for 4.8% of the total sound production and was produced at 14.7% per individual on average (range: 0 – 76%; n=23).
The LAC category (including the croak and the rumble) was intensively produced, and was over-represented by the rumble. Indeed, the croak (occurring in 192 1-s bins from 17 individuals, Table 2a) accounted for 0.9% of the total sound production and was produced at 4.7% (range: 0 – 21.6%; n=23) per individual on average, while the rumble (produced 15 331 times by 20 individuals, Table 2a) accounted for 71% of the total sound production and was produced at 15.4% (range: 0 – 99.9%; n=23) per individual on average. Additionally, rumbles were produced at 99.3% after sunset.
The FMS (occurring in 195 1-s bins from 11 individuals, Table 2a) accounted for 0.9% of the total sound production and was produced at 6.4% per individual on average (range: 0 – 52.5%; n=23).
The grunt (occurring in 375 1-s bins from 21 individuals, Table 2a) accounted for 1.7% of the total sound production and was produced at 14.6% per individual on average (range: 0 – 66%; n=23).
The squeak category (including the long and the short squeak) was weakly produced. The long squeak (occurring in 1627 1-s bins from 19 individuals, Table 2a) and the short squeak (occurring in 1119 1-s bins from 17 individuals, Table 2a) accounted for 7.5% and 5.2% of the total sound production, respectively, and were produced at 11.7% (range: 0 – 49.7%; n=23) and 14.2% (range: 0 – 61.2%; n=23) per individual on average, respectively.
Behavioural Time Budget
In the total behavioural time budget of green turtles, resting and swimming behaviours were the most frequent among individuals (Fig. 2). Resting (occurring in 385 000 1-s bins from 23 individuals, Table 2a) accounted for 45.6% of the total behavioural time budget and was produced at 43.1% per individual on average (range: 0 – 74.2%; n=23). Swimming (occurring in 319 611 1-s bins from 23 individuals, Table 2a) accounted for 37.8% of the total behavioural time budget and was produced at 41.8% per individual on average (range: 0.2 – 93%; n=23).
Others behaviours were much rarer (Fig. 2). Feeding (occurring in 72 936 1-s bins from 22 individuals, Table 2a) accounted for 8.6% of the total behavioural time budget and was produced at 7% per individual on average (range: 0 – 20.4%; n=23). Gliding (occurring in 25 195 1-s bins from 23 individuals, Table 2a) accounted for 3% of the total behavioural time budget and was produced at 3.2% per individual on average (range: 0 – 6%; n=23). Scratching (occurring in 41 931 1-s bins from 21 individuals, Table 2a) accounted for 5% of the total behavioural time budget and was produced at 4.9% per individual on average (range: 0 – 15.1%; n=23).
External Event Occurrences
The conspecific events (occurring in 17 276 1-s bins from 22 individuals, Table 2b) and the human being events (occurring in 1 069 1-s bins from 23 individuals, Table 2b), were monitored on video.
The anthropogenic noise events (occurring in 90 006 1-s bins from 23 individuals, Table 2b) were detected on the audio file, and thus during both night-time and daylight.
Co-Occurrences of Sounds with Behaviours
Table 2a presents the behavioural contexts of the different sound productions.
The five sound types of the pulse category were all produced mostly during resting and swimming (Fig. 3a, Table 2a). Indeed, the mono (occurring in 347 1-s bins from 18 individuals) was produced during resting by 41.3% (18 individuals) and swimming by 55.6% (18 individuals) of the individual mono production on average (Table 2a). The doublet (occurring in 441 1-s bins from 21 individuals) was produced during resting by 47.4% (21 individuals) and swimming by 49.3% (21 individuals) of the individual doublet production on average (Table 2a). The triplet (occurring in 828 1-s bins from 18 individuals) was produced during resting by 48.3% (18 individuals) and swimming by 51.7% (18 individuals) of the individual triplet production on average (Table 2a). The multipulse (occurring in 95 1-s bins from 12 individuals) was produced during swimming by 81.3% (12 individuals) of the individual multipulse production on average (Table 2a). The toc (occurring in 1 044 1-s bins from 18 individuals) was produced during resting by 56.8% (18 individuals) and swimming by 30.9% (18 individuals) of the individual toc production on average (Table 2a). If both multipulse and triplet were mostly heard during resting and swimming as other pulse sounds, they were also produced during surface activity by 25% (12 individuals) and 32.7% (18 individuals) of the individual multipulse and triplet production on average, respectively (Table 2a). However, they were only heard a maximum of two and six times, up to four to eight individuals in this context, respectively.
The two sound types of the LAC category were both produced mostly during resting and swimming (Fig. 3b, Table 2a). Indeed, the croak (occurring in 192 1-s bins from 17 individuals) was produced during resting by 49.4% (17 individuals) and swimming by 45.9% (17 individuals) of the individual croak production on average (Table 2a). The rumble (occurring in 15 331 1-s bins from 20 individuals) was produced during resting by 67.6% (20 individuals) and swimming by 26.4% (20 individuals) of the individual rumble production on average (Table 2a). Rumbles were mostly recorded in three individuals whose showed an intense rumble’s production after sunset, mainly during resting (Fig. 4a,b): 983 1-s bins from 20:11 to 21:27, 7 183 1-s bins from 18:44 to 21:41 and 7 019 1-s bins from 18:41 to 21:31. However, only 12 out of 23 individuals were recorded after sunset.
The FMS was quite rare (195 1-s bins recorded in total on 11 individuals, Table 2a) as well as the scratching behaviour. However, the FMS was highly associated with this scratching behaviour (Fig. 3c). It was found to be produced during scratching behaviour by 83.9% (11 individuals) of the individual FMS production on average (Table 2a).
The grunt (occurring in 375 1-s bins from 21 individuals) was produced during resting by 30.2% (21 individuals) and swimming by 61.1% (21 individuals) of the individual grunt production on average (Fig. 3d, Table 2a).
The two sound types of the squeak category were both produced mostly during resting and swimming (Fig. 3e, Table 2a). Indeed, the long squeak (occurring in 1627 1-s bins from 19 individuals) was produced during swimming by 65.7% (19 individuals) of the individual long squeak production on average (Table 2a). The short squeak (occurring in 1119 1-s bins from 17 individuals) was produced during resting by 33.3% (17 individuals) and swimming by 55.2% (17 individuals) of the individual short squeak production on average (Table 2a).
Table 2b presents the context of the sound production regarding the external events around sea turtles.
Very few sounds were heard during external event-driven contexts. For example, the long squeak (occurring in 1 627 1-s bins from 19 individuals) was produced while the turtles were in the presence of human beings by 12.1% (19 individuals) of the individual long squeak production on average (Table 2b). This is the sound heard the most during such event. Long squeaks were recorded for three individuals during human avoidance events. Two produced long squeaks just after being released from the tagging boat and one produced a long squeak while escaping from three swimmers. Long squeaks were also observed on one other individual. Thanks to the hydrophone pressure logger and 3D-accelerometer, we found out that the sea turtle was about to surface, stopped, then dove rapidly after producing such sound, suggesting an avoidance behaviour.
|
(a) |
|
|
Surface activity |
Feeding |
Gliding |
Resting |
Scratching |
Swimming |
|
||||||||
|
Pulse |
Mono |
n ind |
5 |
3 |
5 |
12 |
3 |
17 |
|||||||||
|
Doublet |
n ind |
4 |
2 |
3 |
15 |
3 |
17 |
||||||||||
|
Triplet |
n ind |
8 |
3 |
4 |
13 |
1 |
15 |
||||||||||
|
Multipulse |
n ind |
4 |
1 |
1 |
3 |
No co-occurrence |
12 |
||||||||||
|
Toc |
n ind |
2 |
6 |
5 |
15 |
3 |
15 |
||||||||||
|
LAC |
Croak |
n ind |
5 |
2 |
1 |
11 |
2 |
13 |
|||||||||
|
Rumble |
n ind |
3 |
5 |
7 |
18 |
4 |
14 |
||||||||||
|
FMS |
|
n ind |
No co-occurrence |
6 |
No co-occurrence |
4 |
11 |
6 |
|||||||||
|
Grunt |
|
n ind |
4 |
12 |
6 |
17 |
8 |
20 |
|||||||||
|
Squeak |
Long Squeak |
n ind |
11 |
4 |
7 |
6 |
2 |
16 |
|||||||||
|
Short Squeak |
n ind |
11 |
2 |
9 |
10 |
2 |
15 |
||||||||||
|
(b) |
|
|
Human |
Conspecific |
Anthropogenic noise |
|
Pulse |
Mono |
n ind |
No co-occurrence |
3 |
8 |
|
Doublet |
n ind |
No co-occurrence |
2 |
9 |
|
|
Triplet |
n ind |
No co-occurrence |
2 |
9 |
|
|
Multipulse |
n ind |
No co-occurrence |
2 |
4 |
|
|
Toc |
n ind |
No co-occurrence |
3 |
4 |
|
|
LAC |
Croak |
n ind |
No co-occurrence |
2 |
6 |
|
Rumble |
n ind |
No co-occurrence |
2 |
6 |
|
|
FMS |
|
n ind |
1 |
No co-occurrence |
4 |
|
Grunt |
|
n ind |
No co-occurrence |
4 |
7 |
|
Squeak |
Long Squeak |
n ind |
3 |
No co-occurrence |
6 |
|
Short Squeak |
n ind |
No co-occurrence |
No co-occurrence |
7 |
Table 2 Co-occurrence of sounds with (a) behaviours and (b) external events. For each cell, the first line corresponds to the number of individuals in which the co-occurrence happened (n ind), the second line is the average number of 1-s bins of sounds co-occurring with (a) behaviours or (b) external events, (min-max values) among the individuals who produced the sound (I.A. 1-s bins), and the third line is the average percentages of 1-s bins of sounds, co-occurring with (a) behaviours or (b) external events (min-max values) among the individuals who produced the sound (I.A.% 1-s bins).
This study provides new knowledge on the acoustic behaviour of green sea turtles based on a long-term investigation focusing on immature green turtle population in the Lesser Antilles. The investigations on the co-occurrences of sounds and behaviours of juveniles at their foraging grounds are key to understanding the social needs of this endangered reptile. Altogether, these findings offer new and innovative perspectives for improving sea turtles conservation.
Not all sound types could have been recorded for every individual during the deployments. The sounds most commonly produced among juvenile green turtles were the doublet, the grunt and the rumble. However, although doublets and grunts were commonly produced among individuals, they were produced far less frequently than the rumble. Indeed, the doublet and the grunt accounted for 2.04% and 1.74% of the total sound production, respectively, while the rumble accounted for 71% of the total sound production. In contrast, the FMS and the Multipulse were the sounds the least commonly produced among and within individuals. The croak was also weakly produced within individuals. However, since the tags were recorded for less than 24 hours at a time, it is likely that all sounds, behaviours and external events could not be sampled within a recording session.
This study provided the first investigation focusing on the relationship between behaviours, external events and underwater sounds produced by juvenile green turtles. Seven out of the eleven described sounds were not produced in a specific context. Indeed, the pulse, the LAC, the grunt and the squeak categories were mostly produced while juvenile green turtles were resting or swimming. These prevailing co-occurrences can be attributed to their predominant activity patterns during the tag deployment which was mainly resting and swimming. Due to the lack of context-specificity, it is therefore not possible to suggest any biological function of these sounds. They might be a by-product of the physiological activities of the juvenile green turtle, and thus not an acoustic cue or signal for social communication. Furthermore, as no sound was recorded during physical interactions between conspecifics (affiliative or agonistic), we suggest that sound may not be the medium used for direct interaction, as sight or smell remain effective at close range.
Among the context-specific sound productions, the FMS was mostly produced during scratching behaviour. Both FMS and scratching are individually rare, however, the average percentage of co-occurrence is very high (83.9%, Table 2a). The FMS was generally produced when sea turtles seem to be alone and undisturbed. We thus suggest that FMS could correspond to the expression of a positive emotional state. However, we are not able to assess any biological function to this sound.
The observation of stereotyped retreat head movements, coinciding with the production of grunts, suggests a potential link between this sound and specific behavioural responses in juvenile green turtles. This head movement could be a visual agonistic component analogous to aggressive threat signals observed in others species, such as lizard waving their tails at approaching predators (Bradbury & Vehrencamp, 1998). Given that grunts were produced when sea turtles were vigilant or approaching conspecifics, we suggest that grunts could function as warning signals, playing a role in intra-specific acoustic communication among juvenile green turtles. This interpretation is supported by previous studies describing short grunts as distress calls in desert tortoises Gopherus agassizii (Campbell & Evans, 1967).
We previously assessed that the squeak might be a good candidate for intra-specific communication due to their individual stereotypy (i.e., individual-specific) (Charrier et al., 2022). The observation of long squeaks produced by green turtles during avoidance behaviour in the presence of humans provides interesting insights into the potential link between this sound in response to perceived threats and anti-predator or avoidance behaviour. The limitations of the camera's field of view (100°), highlight the possibility that important contextual cues, such as the presence of conspecifics or another potential threats (animal or a human), may not have been captured, thus complicating the interpretation of behavioural responses. Indeed, we often saw that the turtle was vigilant and looking around, but we could not explain why it remained alert. The long squeak could thus constitute a first evidence of an alarm acoustic signal in juvenile green turtles, and thus could be used to alert conspecifics from a threat. While evidence of alarm acoustic signals has been shown in other reptile species (e.g. leopard lizard Gambelia wislizenii, Wever et al., 1966; fossorial snakes produce distinctive body vibrations when threatened, Young et al., 2013), confirmation of the alarm function of the long squeak in green turtles would require experimental validation using playback experiments.
We recorded long sequences of rumbles after sunset and during resting behaviour. The overlapping and varied amplitude levels of these rumbles suggest that several individuals in the vicinity may contribute to these intense acoustic interactions, potentially engaging in some form of coordinated sound production. It could thus constitute a first evidence of a social acoustic communication among juvenile green turtles during night-time resting periods. Similar production, called choruses, are observed in other taxa like birds, frogs and fishes (Farina & Ceraulo, 2017). It highlights the potential for social communication and coordination among green turtles during night-time resting periods. The only example is the Travancore tortoise Indotestudo travancorica (i.e., several individuals called together, individual sound productions seemed to be regularly spaced, it was most often heard at night, Campbell & Evans, 1972). While chorusing behaviour is well documented among animals and has associated with diverse ecological functions (e.g. energetic and behavioural matters in birds, Farina & Ceraulo, 2017), its occurrence in reptiles, particularly Chelonians, remains relatively unknown.
Our findings show there is no specific sound produced, nor any behaviour that stopped during anthropogenic noise events. This suggests that juvenile green turtles may not alter their acoustic behaviour in direct response to boat activities. However, the absence of behavioural stress through their sound production or behaviour does not mean that boat activities do not induce physiological stress. Although green turtles have not altered their sound production in response to boat noise, they appear to surface less often to breathe (L. Maucourt, pers. com.). This alteration in surfacing behaviour may indicate a potential physiological response to the presence of boats and associated noise, suggesting a possible impact on their respiratory patterns or diving behaviour. A comparable behavioural response has previously been observed in a juvenile green turtle, wherein it appears to remain stationary on or near the sea floor when ships pass nearby (Tyson et al., 2017). Most studies of sea turtle responses to anthropogenic noises have focused on exposure to high-intensity seismic airguns in a closed or semi-closed environment, which limits the ability to assess the behaviour of free-ranging turtles exposed to different anthropogenic noises (McCauley et al., 2000; Moein et al., 1994; O’Hara & Wilcox, 1990). The rare studies on free-ranging turtles exposed to seismic airgun surveys reported contrasting results. Indeed, DeRuiter & Larbi Doukara (2012) reported that loggerhead turtles dived immediately following an airgun shot, while Weir (2007) reported that 83% of sea turtles (including olive ridley, leatherback and loggerhead turtles) continued to bask at the surface during airguns exposure and as the vessel and towed equipment moved past. We know that the variability of behavioural responses to the noisy event is highly complex in many species including reptiles. For example, freezing is a typical stress response in reptiles, as in the Eastern blue tongued lizard Tiliqua scincoides, exposed to the noise of mining machinery (Mancera Alarcon, 2016). Given impact of anthropogenic noise on reptile behaviour remains relatively understudied compared to other taxa (Jerem & Mathews, 2021; Simmons & Narins, 2018), the interpretation of the results regarding the effect of boat noise on the behaviour of juvenile green turtles is constrained. Further research is required to compare the behaviour of green turtles occurring in areas with low and high-levels of boat traffic.
Thanks to this study, the biological function of three sounds types (rumble, grunt and long squeak) produced by juvenile green turtles have been suggested. These sounds, produced in specific behavioural contexts, offer very interesting potential applications for sea turtle conservation. Indeed, our findings highlight intra-specific social interactions, but also alert or vigilance sounds. It provides a strong baseline to test free-ranging green turtle with playback experiments using the long squeak, the grunt or the rumble to assess if we can elicit a behavioural reaction of the tested individuals. It has been shown that some reptiles are sensitive to heterospecific calls and can adapt their behavioural response from threat signals (e.g. brown anoles Anolis sagrei, Cantwell & Forrest, 2013; marine iguana Amblyrhynchus cristatus, Vitousek et al., 2007). Some of these sounds could be used in deterrent systems aiming to reduce by-catch of sea turtles, that prevent them from being entangled in fishing nets (McClellan et al., 2011). Thus, our findings could potentially have strong application in conservation issue for sea turtles.
Indeed, the use of acoustic pingers on harbour porpoise Phocoena phocoena for instance has shown that acoustic signals can reduce the occurrence of by-catches at least at short-term (Gearin et al., 2000; Kraus et al., 1997; Trippel et al., 1999). As part of the TOPASE program (Martinique-Guadeloupe) aiming to reduce by-catch of sea turtles in fishing nets, recent preliminary investigations based on the findings of this present study, involved using green sea turtle-specific sounds (squeaks and rumbles) in playback experiments on free-ranging green turtles during their foraging activities in Martinique (Chevallier et al., 2024). Encouragingly, the playback tests showed that a majority of green turtles responded to these sea turtle sounds by exhibiting behavioural alertness. Further investigations are now required to confirm such findings. Specifically, attention can be directed towards examining specific sounds such as the grunt and the long squeak to confirm their role in avoiding danger from conspecifics or humans, and to explore the biological function of the rumble in social interactions and social grouping pattern at night.
Finally, to gain a deeper understanding of the biological function of these sounds, it would be valuable to perform similar recordings using animal-borne tags on adult green turtles. By comparing the sound repertoire of juvenile to those of adult green turtles, we could potentially highlight any variations in acoustic parameters and reveal ontogenetic information regarding the development of sound-producing organs. Moreover, this approach could reveal new sound productions used only during the breeding period, providing further insights into the acoustic behaviour of adult green turtles.
Acknowledgments
This study was carried out within the framework of the Plan National d’Action Tortues Marines Antilles. We are grateful to the numerous volunteers and free divers for their participation in the field operations.
Funding
This work was co-funded by the FEDER Martinique (Fonds Européen de Développement Régional PO/FEDER/FSE 2014-2020, Conventions 246239), Collectivité Territoriale de Martinique (CTM, convention 258342), the Direction de l′Environnement, de l′Aménagement et du Logement (DEAL, Martinique, France (Convention N°2017/164894), the Office De l′Eau (ODE) Martinique, France (Convention n°180126) and the Office Français de la Biodiversité (OFB, Parc Naturel Marin de Martinique, OFB-23-0563), the town of Les Anses d’Arlet and ANSLO’S. Léo Maucourt PhD scholarship was supported by the Collectivité Territoriale of Martinique (CTM).
Author Contributions
DC, IC and LM designed research; DC, NA, OB, LJ, NL, FL, PL, ML, JM and SR performed data collection; LM and CH designed the code of the Dummy Coding method; LM, IC, LJ and DC analysed data; DC acquired funding; LM wrote the original version of the paper; IC, DC and CH edited the paper.
Data Avaibility
The data used for this study are available in the supplementary material.
Ethics approval
Fieldwork was performed in accordance with the French legal and ethical requirements. Specifically, the protocol was approved by the Conseil National de la Protection de la Nature and the French Ministry for Ecology (permit numbers: 201710-0005 and R02-2020-08-10-006) and followed the recommendations of the Police Prefecture of Martinique. Fieldwork was carried out under the certification of Damien Chevallier (prefectural authorisations’ owner) under strict compliance of the Police of Martinique’s recommendations to minimize animal disturbance. Indeed, the capture, although stressful for the animal, respects the safety conditions for the divers as well as for the sea turtle. The handling time on the boat did not exceed 10 minutes in order to minimise the stress associated with capture. The use of suction cups instead of glue makes the attachment system much less invasive. The slightly positive buoyancy and hydrodynamics of the camera ensure that the turtle's movements are not constrained when diving or surfacing. The automatic release of the device after a maximum of two days avoids the stress of a second capture.
Conflict of interest
The authors declare no conflict of interest.
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