A Wave Glider for Passive Acoustic Monitoring of Cetaceans and Anthropogenic Sounds in the Central Mediterranean Sea

A Wave Glider for Passive Acoustic Monitoring of Cetaceans and Anthropogenic Sounds in the Central Mediterranean Sea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Wave Glider for Passive Acoustic Monitoring of Cetaceans and Anthropogenic Sounds in the Central Mediterranean Sea Sara Ferri, Anaëlle Evrard, Simonepietro Canese, Teresa Romeo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6222010/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Passive Acoustic Monitoring (PAM) has become a widely used method to study cetaceans, especially for populations facing escalating threats from noisy human activities, including shipping traffic, fishing industry and marine constructions. Here, we conducted the first study using an Autonomous Surface Vehicle (ASV) to explore the distribution and acoustic behavior of cetaceans and to characterize anthropogenic sound sources in the central Mediterranean Sea. A Wave Glider equipped with a single-towed acoustic recorder was deployed from 13th September 2022 to 3rd March 2023. The recording yielded 19,115 files of 460s each (about 2 TB), a third of which was kept for a preliminary analysis based on spectrogram visualization and audio listening. The results showed that nearly half of the dataset contained delphinid signals (Delphinidae), followed by sperm whales ( Physeter macrocephalus ) and fin whales ( Balaenoptera physalus ), with notable hotspots in the southern Tyrrhenian and the Ionian Sea. Moreover, the almost continuous detection of anthropogenic sources highlighted the widespread acoustic impact of human activities in the area. These findings demonstrate the value of passive acoustics in the use of autonomous vehicles as a versatile tool for large-scale and long-term monitoring, offering a promising approach to support conservation efforts for vulnerable species while advancing strategies to mitigate human impacts on marine ecosystems. Biological sciences/Ecology Earth and environmental sciences/Ocean sciences/Marine biology Autonomous vehicles Mediterranean basin Underwater acoustics Marine mammals Cetacean hotspots Marine conservation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The marine soundscape is shaped by physical and biological sounds as well as anthropogenic ones [1], and it is highly variable both in time and space [2]. The biological activities of marine mammals, fish or crustaceans contribute significantly to the ambient sound in marine ecosystems through the production of sounds [3]. Anthropogenic sounds, now grouped together under the term "anthropophony", are derived from a variety of sources that result from human activities [4,5]. In recent years, Passive Acoustic Monitoring (PAM) has increased as a non-invasive method to monitor the acoustic marine environment, allowing continuous detection of vocalizing animals across large spatial and temporal scales [6], in remote and unknown areas and under any weather conditions [7]. The advancement and widespread implementation of modern technologies have improved the performance of PAM. While traditional PAM methods were based on the installation of fixed acoustic mooring platforms or cabled stations [8], the development of Autonomous Surface Vehicles (ASVs), such as the Wave Glider, has expanded PAM applications [9]. Mobile platforms, when developed and equipped with hydrophones, allow long-term acoustic surveys to record ambient sound and to gather critical information about marine ecosystems [10]. Cetaceans are well-suited for acoustics investigations due to their ability to produce different types of sounds essential for vital functions and communication, resulting in a complex and varied acoustic repertoire [11]. Several studies have proven the efficiency of PAM techniques in investigating cetacean diversity [12], their abundance and habitat use [13], behavior [14], as well as in assessing the impact of anthropogenic noise on their biological activities [15]. The introduction of significant levels of underwater noise can add pressures to marine ecosystems and may have harmful effects on cetaceans such as permanent or temporary threshold shifts, behavioral changes and acoustic masking [5]. The latter occurs when a signal emitted by a cetacean is covered by ambient noise or overlapped by another signal with the same frequency [16]. Although the Mediterranean Sea represents only 0.8% of the world's ocean surface, it has an extremely high density of maritime traffic, with 30% of all international shipping concentrated in this area [17]. In this basin, ten species of cetaceans are considered resident, three are occasionally sighted and considered visitors, and seven other species have been classified as vagrants in the Mediterranean, highlighting the region as a cetacean biodiversity hotspot [18]. These species face numerous anthropogenic threats, including noise disturbance, collisions, and bycatch [19]. Noise pollution, primarily from maritime traffic, sonars, seismic exploration, and industrial activities (such as offshore wind farms), represents a growing concern for marine conservation in this semi-enclosed basin [20]. Conservation efforts are therefore increasingly aimed at mitigating these threats and maintaining a favorable conservation status of the Mediterranean populations, as required by the EU Habitats Directive [18]. Key initiatives such as the PELAGOS Sanctuary and ACCOBAMS have been central to cetacean conservation in the region. Specifically, knowing the distribution and spatio-temporal abundance of cetacean species in each area is essential to promote effective conservation measures [21]. This study represents the first-time deployment of a Wave Glider for passive acoustic monitoring of cetaceans in the central Mediterranean Sea. Previous large-scale acoustic surveys, conducted from research vessels, were carried out in this area although limited by weather conditions and extended sampling periods [22,23,24]. The glider provides a more efficient way to detect and classify cetacean sounds, allowing the study of species diversity, acoustic behavior and spatio-temporal distribution. Furthermore, it provides a valuable tool to identify the main anthropogenic noise sources. This approach contributes to the ultimate goal of integrating innovative technologies into marine conservation plans, addressing the dual goals of protecting biodiversity and mitigating human impacts. Results Acoustic detections of cetaceans Preliminary results on cetacean species diversity in the study area, as well as the diversity of signals produced, were obtained on a large scale using a hydrophone-equipped Wave Glider for PAM. In total, over the 814.2 hours of acoustic data manually analyzed, we identified 3069 files with detections (i.e., presence of at least one signal in a file) of delphinids (48.2% of the analyzed dataset), 125 files with detections of sperm whales (1.96%) and 13 files with detections of fin whales (0.2%) (Fig. 1 ). Whistles were the most frequently detected signal produced by delphinids (78.1%), followed by echolocation clicks (60.9%) and pulsed sounds (17.7%). Among sperm whales, regular clicks were found in almost all recordings with detections (99.2%), while creaks and codas accounted respectively for 4.8 and 4% of the sperm whale detections. Spatial distribution of cetaceans The preliminary analysis of the acoustic dataset allows a large-scale characterization of the presence and spatial distribution of cetacean signals along the glider track (Fig. 2 a). Delphinids were detected all along the track, showing a wide distribution among the central Mediterranean basin, with a few less detections in shallower waters. This was confirmed by the chi-squared test that highlighted the effect of both bathymetry (chi-squared = 673.28, df = 2, p-value = < 0.001) and distance to the nearest coast (chi- squared = 21.949, df = 2, p-value = 1.713e-05) on the detection of delphinids signals and therefore on their spatial distribution (Fig. 2 b, c). Sperm whale acoustic signals were detected in specific areas of the central Mediterranean Sea: southern Tyrrhenian Sea, western Ionian Sea and southern Adriatic Sea. Regarding the sperm whale encounters, the chi-squared highlighted the same tendencies with an influence of bathymetry (chi-squared = 39.244, df = 2, p-value = 3.007e-09) and distance to the nearest coast (chi-squared = 78.999, df = 2, p-value < 0.001) on the spatial distribution of this species, with a complete absence of detection in epipelagic waters (Fig. 2 b, c). Finally, fin whale detections were found only in few recordings acquired in a restricted area of the southern Tyrrhenian Sea. Temporal distribution (GAM) of delphinids The temporal distribution of signals emitted by delphinids during all the survey period was studied by looking at the distribution of the signals detected for each category (clicks, whistles and pulsed sounds) according to the time of their detection. Hence, these GAMs present the prediction of occurrence of delphinid signals according to the hour of the day and highlight the significant efficiency (all three p-values < 0.001) of using this variable as a predictor of the probability of presence of delphinids (Fig. 3 ). A daily trend was identified for signal presence in function of the hour of the day following a circadian rhythm where all three signals but specifically clicks and pulsed sounds were more present during nighttime. Anthropogenic sound detections The main types of anthropogenic sounds detected by the Wave Glider were categorized into ships, sonar and low-frequency pulses (Fig. 4 ). Shipping noise represented the predominant type of sound identified, with 4810 files accounting for 75.6% of the total acoustic file. Next were low-frequency pulses, detected in 1639 files, accounting for 25.7% of the total detections and sonar signals detected in 548 files with 8.6% of the total. Spatial distribution of anthropogenic sound sources The spatial distribution of anthropogenic sound sources recorded by the glider in the central Mediterranean Sea was investigated. Overall, the data revealed an almost continuous presence of anthropogenic noise along the glider's track, suggesting that anthropogenic activities contribute significantly to the marine soundscape of this region (Fig. 5 a). The chi-squared test highlighted the effect of both bathymetry (chi-squared = 31.961, df = 2, p-value = 1.148e-07) and distance to the nearest coast (chi-squared = 323.4, df = 2, p-value < 0.001) on the detection of shipping noise and therefore on its spatial distribution along the Wave Glider track (Fig. 5 b, c). Sonar was predominantly found in the southern Tyrrhenian Sea with few other sporadic detections. The chi-squared showed an effect of both parameters on sonar detections (for bathymetry chi-squared = 163.75 and for distance to the coast chi-squared = 86.512, df = 2, p-values < 0.001). As for shipping noise, the low-frequency pulses were spread in all the study area, with few detections in the Sicily Channel and with an effect of bathymetry (chi-squared = 61.102, df = 2, p-value = 5.394e-14) and distance to the coast (chi-squared = 104.59, df = 2, p-value < 0.001) on their distribution. Temporal distribution (GAM) of anthropogenic sound sources The temporal distribution of anthropogenic signals during all the survey period (Fig. 6 ) was studied by looking at the distribution of the sound sources detected for each category (ships, sonar and low-frequency pulses) according to the time of their detection. Here, the hour of the day was a suitable predictor for low-frequency pulses presence (p-value = 0.0091) that occurs more in early morning and for sonar (p-value = 0.449). However, the continuous presence of shipping noise was observed over the day and the GAM did not highlight any temporal tendency for this signal (p-value = 0.0547). Discussion The use of Autonomous Surface Vehicles (ASVs) has demonstrated their versatility and potential in a wide range of scientific fields [8]. The Wave Glider has proven its versatility in underwater acoustic research, demonstrating the ability to collect high quality data on marine soundscapes and applications in diverse marine environments [10,25]. Previous studies have shown its efficiency in gathering data on cetaceans: investigating acoustic presence and habitat exploitation in relation with environmental variables of humpback whales ( Megaptera novaeangliae ) in the Hawaiian archipelago (Pacific Ocean) [26]; assessing delphinid diversity, abundance, and acoustic activity in the southwestern Atlantic Ocean [13]. Although Autonomous Underwater Vehicles (AUVs), such as profiling gliders, have previously been employed in the Mediterranean to detect and monitor cetaceans [27,28], this work represents the first application of a hydrophone-equipped Autonomous Surface Vehicle (ASV) in the region. Specifically, the Wave Glider used in this study represents a novel approach to cetacean monitoring in the basin. This long-term large-scale survey allowed us to detect the presence of different species of cetaceans based on their vocalizations confirming their potential applications in both coastal and oceanic environments [29,30]. Delphinids signals were detected almost everywhere along the path of the glider, with less detections in shallower waters, as highlighted by the chi-squared analysis of bathymetry influence. Although this study did not distinguish between species, it is well known that the striped dolphin is the most abundant cetacean species in the Mediterranean Sea [31], with a marked preference for pelagic waters off the continental shelf [18]. Moreover, its abundance tends to be lower in the Sicily Channel than in the Tyrrhenian and Ionian Seas [32]. Taking the delphinids in general, a decrease in both abundance and species diversity from west to east of the Mediterranean Sea basin has been previously observed [31,33]. While our study concentrated on overall trends, further analyses could use advanced techniques to analyse narrow-band signals, such as whistles. This approach could help identify different species and improve our understanding of their distribution and behavior. Regarding the temporal distribution of the types of signals emitted by delphinids, the GAMs revealed that the hour of the day seems to be a relevant predictor of their presence, with evidence of a clear daily pattern covering both day and night. Specifically, echolocation clicks and pulsed sounds, which are related to foraging activity, are more abundant during dark hours, possibly due to trophic chain dynamics, such as nychthemeral migrations [34]. This finding of a notable diel pattern in click rates is further supported by the study of Papale et al. [35] in the Sicily Channel. Furthermore, whistles are more frequently produced during early morning and early night, likely reflecting the use of social interactions during traveling and daytime activities, as well as during foraging when associated with clicks and pulsed sounds [36]. Therefore, PAM is confirmed to be an efficient technique to identify circadian cycles in the acoustic activity of delphinids, gathering more information on their behavior than traditional visual methods [11]. The wide distribution of dolphins also provides valuable insights into their ecological role in different ocean zones as fundamental top predators of marine ecosystems. Moreover, the Wave Glider was also able to detect acoustic signals from a deep diving species with specific habitat preference, the sperm whale. Sounds from sperm whales were identified in three main areas: the Tyrrhenian Sea, the Ionian Sea, and the Southern Adriatic Sea. These are in line with previous acoustic detections of the species [23], reinforcing the ecological importance of these regions as potential hotspots for sperm whales. Furthermore, acoustic and visual detections of the species have previously been shown not to follow a uniform distribution in each area of the Mediterranean [37,38], explaining the influence of environmental variables leading to habitat partitioning. In this study, the chi-squared highlighted as well the influence of both bathymetry and distance to the coast on the probability of detection. The results are consistent with earlier studies showing a strong association between sperm whale distribution and specific topographic features, such as submarine canyons and seamounts [39]. For example, in the Tyrrhenian Sea, Mussi et al. [40] observed the presence of sperm whales in areas characterized by sharp bathymetric gradients, while similar patterns were observed in the Ionian Sea [41,42] and Ligurian Sea [43]. The ecological importance of submarine canyons and seamounts for marine mammals is well established. These features are recognized as keystone habitats due to their role in increasing food availability through oceanographic processes such as nutrient upwelling [44]. The results of this study highlight the need of preserving these remote areas in the Mediterranean Sea. Fin whales’ detections were few during this dynamic PAM survey and probably underestimated as only mature males produce vocalizations [45] that are usually detected in low background noise conditions [46]. Indeed, low-frequency anthropogenic noise such as shipping noise or low-frequency pulses could lead to the masking of their calls and prevent their detection [47], especially with sensors close to the surface. Nonetheless, this study allowed us to monitor the presence of fin whales in the southern Tyrrhenian Sea, which can coincide with their transit route between a summer feeding ground in the Ligurian Sea [48] and a winter-feeding ground around Lampedusa [49]. This transition was also demonstrated through satellite tagging [50]. The species occurrence is higher during late spring/summer period in the northwestern Mediterranean Sea, following an aggregation-dispersion migration scheme [51]. Then, traveling southwards to reach the Sicily Channel during winter [50]. The acoustic detections reported here highlight the potential of PAM methods in monitoring fin whale movements even in a noisy environment. Among anthropogenic sources, shipping noise was the most prevalent, followed by low-frequency pulses and sonar signals. These types of human-noisy activities were detected almost continuously along the glider's path, with spatial variability influenced by bathymetry and distance to the coast. Sonar detections were concentrated in the southern Tyrrhenian Sea, while shipping noise and low-frequency pulses were more widely distributed. In particular, these occurred more frequently in the early hours of the morning, while sonar and shipping noise did not show a clear daily pattern according to the temporal analysis. These results highlight the need for effective management strategies to mitigate anthropogenic noise, such as changing shipping routes or limiting activities in key habitats. Autonomous Surface Vehicles like the Wave Glider are valuable tools for continuous passive acoustic monitoring, gathering data on sound sources, which can be used to assess mitigation measures' effectiveness, such as regulating vessel routes [52]. These vehicles are very useful to monitor remote and unknown areas, especially for large and long-term surveys, filling gaps left by traditional monitoring methods. Based on our preliminary results, the Wave Glider can be considered suitable platforms to monitor the spatio-temporal distribution and acoustic behavior of cetaceans, as well as to study potential overlap with the presence of anthropogenic sounds. Indeed, many areas of the Mediterranean Sea are subject to human activities including shipping traffic and offshore operations posing a significant threat to marine life, especially for cetaceans. Human-made noise affects cetacean populations through habitat loss [53] and changes in acoustic behavior [54]. Despite this, recent studies demonstrate that noise pollution, classified as a sensory pollutant, is relatively possible to manage [55]. In this context, the track of the glider has been defined in the frame of Environmental Impact Assessment (EIA) plans before the potential implementation of offshore wind farms. Therefore, this study demonstrates the need to designate Important Marine Mammal Areas (IMMAs) in remote and offshore waters, which are critical ecological regions already heavily affected by human activities. Methods Wave Glider and Study Area The Wave Glider is a remotely controlled autonomous surface vehicle made of two parts connected to one another by an 8-meter umbilical cable (Fig. 7 ). The floating part on the surface is equipped with solar panels to provide power to the system, a navigation system programmable and modifiable, satellite communication, and multi-parameter customizable oceanographic sensors (e.g. CTD and fluorometry). The underwater part - called the sub- that captures the wave surface motion and through the rising and falling of its six wings transforms it into forward motion to propel the vehicle forward. In addition, a solar electrical thruster is available for extra speed and mobility in case of challenging ocean conditions. The Wave Glider used in this study is the SV3 074 model developed by Liquids Robotics, which is particularly suitable for long-term missions with an endurance of up to one year. For our study, it was also equipped with a single towed hydrophone (Porpoise, RS Aqua, UK) with a sensibility of -193.3 dB re 1V/µPa. The vehicle was deployed in the central Mediterranean Sea from the 13th of September 2022 from the island of Minorca (Spain) to 17th December 2022, and from 15th January 2023 to 3rd March 2023 (Fig. 8 ). The acoustic survey started on the 30th of September, the previous period being a test with no acoustic acquisition of data. This ASV crossed the southern Tyrrhenian Sea, the Sicily Channel, and the Ionian Sea for about 2,000 nautical miles at an average speed of about 1 knot, before reaching the southern Adriatic Sea. The glider transects on the path were defined based on potential future offshore wind farm development areas in the central Mediterranean Sea. Data Collection Acoustic data acquisition started on the 30th September 2022 (the previous period being a navigation test with no data acquisition) and recordings were acquired continuously except for the following days due to system failure and a time interval between deployments: from 26th to 30th November, the 10th December, from 13th to 15th December, from 18th December 2022 to 14th January 2023, and from 24th February to 26th February 2023. The continuous recording setting yielded a total of 19,115 files of 460 s in a .flac format (around 2 TB), acquired at a sampling rate of 192 kHz (Table 1 ). Table 1 Summary of data collection effort. Key metrics include the duration of the campaign, days of Passive Acoustic Monitoring (PAM), number of files recorded, data storage requirements, total recording hours, nautical miles covered, and the average speed of the survey vessel during the observation period. Start End N° days N° days of PAM N° files Memory Hours recorded Nautical miles Average Speed 13th Sep 2022 3rd March 2023 144 115 19,115 ~ 2 TB 2,442 ~ 2,000 ~ 1kt Data analysis A subset considered representative of the original dataset (6,372 files) was kept for manual analysis, that consisted in spectrogram visualization (2048-point FFT and 1012-point Hanning Window) and audio listening using the open-source software Raven Lite (Bioacoustics Research Program, Cornell Lab of Ornithology). We assessed the presence/absence of cetaceans and anthropogenic sources in each recording with a distinction between broadband and low-frequency acoustic sources. Acoustic signals emitted by odontocetes (delphinids – Fam. Delphinidae or sperm whales - Physeter macrocephalus ) were distinguished into social and echolocation sounds. These were classified into different categories: echolocation clicks, whistles and pulsed sounds for delphinids; regular clicks, creaks and codas for sperm whales. In addition, to identify the low-frequency calls of mysticetes (fin whales - Balaenoptera physalus ), a downsample of each acoustic file, set to 1.6 kHz, was performed in MATLAB (MathWorks, Natick, MA, USA). Each recording was then displayed as a spectrogram, and the audio file was listened to at accelerated speed (5x). This method improves the clarity of vocalizations by making them more audible and easier to identify. The objective was to report the presence of different cetacean species and anthropogenic sound sources (ships, sonar, low-frequency pulses) along the Wave Glider track. The entire dataset was analyzed by three expert PAM operators to limit the observer bias. To assess consistency among observers, interrater reliability was assessed on a 1% sample of the dataset using Cohen's kappa, as described in McHugh [56]. The analysis resulted in a kappa value of 0.95, indicating an 'almost perfect' level of agreement, with 82–100% of the data considered reliable. Statistical analysis To have more insights on the spatial distribution of cetaceans and anthropogenic sources along the Wave Glider track, we recovered mean depth data derived from the EMODnet Bathymetry portal ( http://www.emodnet-bathymetry.eu ), and calculated the nearest distance to the coast (mainland and islands) on QGIS software v.3.28.15 (QGIS Development Team, 2024). These two parameters were used to discriminate three corresponding zones based on their historical use in marine research [57]: Epipelagic ( 1000m) for the bathymetry; 50 km for the coast distance. A chi-squared test was conducted using the R software (R Foundation for Statistical Computing) to explore the effect of both bathymetry and distance to the coast on the probability of detection and therefore on the presence of delphinids and sperm whales along the Wave Glider track. Fin whale detections were not included due to the few detections reported, and regarding the long propagation range of their calls [46,58]. Moreover, the same statistical analysis was performed on the three different anthropogenic sound sources identified (ships, sonar and low frequency pulses). Finally, the temporal patterns of signals emitted by the most common cetacean source, delphinids (clicks, whistles, and pulsed sounds), and anthropogenic sources were examined using a Generalized Additive Model (GAM) [59]. This approach was used to study the variation of occurrence of each specific signal according to the hour of the day. Binomial based (presence/absence) GAMs with a logit link function and the hour of the day as a smoothed fixed effect were performed using the mgcv R package [60]. Declarations Data Availability Data is available on request to the corresponding author. Acknowledgements This research would not have been possible without the work of many colleagues at the Stazione Zoologica Anton Dohrn. In particular, the authors would like to thank Robin Caron, Florence Rappin and Gerardo Sorrentino. During the writing of this manuscript, Sara Ferri was supported by the PhD program of the Stazione Zoologica Anton Dohrn and by the University of Turin through a SUSTNET PhD scholarship (n°39-033-31-DOT21BX3F7-8932). The PhD funder had no role in study design, data collection, or data analysis. Author contributions statement F.C., S.F., A.E. conceptualization. F.C., S.C., A.P., S.F., A.E. methodology. F.C., S.F., A.E. data analysis. S.F., A.E. writing and original draft preparation. F.C., L.F. writing and review and editing. F.C. supervision. S.C., A.P., T.R. project administration. T.R., S.G. funding acquisition. All authors have read and agreed to the published version of the manuscript. 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Modelling sperm whale habitat preference: a novel approach combining transect and follow data. Mar. Ecol. Prog. Ser. 436 , 257–272 (2011). Tepsich, P., Rosso, M., Halpin, P. & Moulins, A. Habitat preferences of two deep-diving cetacean species in the northern Ligurian Sea. Mar. Ecol. Prog. Ser. 508 , 247–260 (2014). Mussi, B., Miragliuolo, A., Zucchini, A. & Pace, D. S. Occurrence and spatio‐temporal distribution of sperm whale ( Physeter macrocephalus ) in the submarine canyon of Cuma (Tyrrhenian Sea, Italy). Aquatic Conservation 24 , 59–70 (2014). Bellomo, S. et al. Photo-identification of Physeter macrocephalus in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea). IMEKO TC (2019). Caruso, F. et al. Size Distribution of Sperm Whales Acoustically Identified during Long Term Deep-Sea Monitoring in the Ionian Sea. PLoS ONE 10 , e0144503 (2015). Aïssi, M. et al. Large-scale seasonal distribution of fin whales ( Balaenoptera physalus ) in the central Mediterranean Sea. J. Mar. Biol. Ass. 88 , 1253–1261 (2008). Würtz, M. Mediterranean pelagic habitat: oceanographic and biological processes, an overview. Gland, Switzerland: IUCN (2010). Castellote, M., Clark, C. W. & Lammers, M. O. Fin whale (Balaenoptera physalus) population identity in the western Mediterranean Sea. Marine Mammal Science 28 , 325–344 (2012). Sciacca, V. et al. Annual Acoustic Presence of Fin Whale (Balaenoptera physalus) Offshore Eastern Sicily, Central Mediterranean Sea. PLoS ONE 10 , e0141838 (2015). Putland, R. L., Merchant, N. D., Farcas, A. & Radford, C. A. Vessel noise cuts down communication space for vocalizing fish and marine mammals. Global Change Biology 24 , 1708–1721 (2018). Forcada, J., Aguilar, A., Hammond, P., Pastor, X. & Aguilar, R. Distribution and abundance of fin whales ( Balaenoptera physalus ) in the western Mediterranean sea during the summer. 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Anthropogenic noise effects on Risso’s dolphin vocalizations in the Gulf of Taranto (Northern Ionian sea, central Mediterranean sea). Ocean & Coastal Management 254 , 107177 (2024). Dominoni, D. M. et al. Why conservation biology can benefit from sensory ecology. Nat Ecol Evol 4 , 502–511 (2020). McHugh, M. L. Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 22 , 276–282 (2012). Sverdrup, H. U., Johnson, M. W., & Fleming, R. H. The Oceans, their physics, chemistry, and general biology. Printice-Hall, Inc., 1087p (1942). Širović, A., Hildebrand, J. A. & Wiggins, S. M. Blue and fin whale call source levels and propagation range in the Southern Ocean. The Journal of the Acoustical Society of America 122 , 1208–1215 (2007). Hastie, T. & Tibshirani, R. Generalized Additive Models. Statist. Sci. 1 , (1986). Pedersen, E. J., Miller, D. L., Simpson, G. L. & Ross, N. Hierarchical generalized additive models in ecology: an introduction with mgcv. PeerJ 7 , e6876 (2019). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Aug, 2025 Reviews received at journal 19 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviews received at journal 17 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers agreed at journal 23 Mar, 2025 Reviewers invited by journal 21 Mar, 2025 Editor assigned by journal 21 Mar, 2025 Editor invited by journal 21 Mar, 2025 Submission checks completed at journal 20 Mar, 2025 First submitted to journal 13 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6222010","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":432705844,"identity":"c432b254-e215-4894-b9c4-92df66e9552c","order_by":0,"name":"Sara Ferri","email":"","orcid":"","institution":"Stazione Zoologica Anton Dohrn","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Ferri","suffix":""},{"id":432705847,"identity":"0fd3257b-d831-4962-8e4f-1259ecc4421f","order_by":1,"name":"Anaëlle Evrard","email":"","orcid":"","institution":"Stazione Zoologica Anton Dohrn","correspondingAuthor":false,"prefix":"","firstName":"Anaëlle","middleName":"","lastName":"Evrard","suffix":""},{"id":432705849,"identity":"751d02e9-cebd-4f5e-80dd-e8471466bcf5","order_by":2,"name":"Simonepietro Canese","email":"","orcid":"","institution":"Stazione Zoologica Anton Dohrn","correspondingAuthor":false,"prefix":"","firstName":"Simonepietro","middleName":"","lastName":"Canese","suffix":""},{"id":432705850,"identity":"fe687979-4406-4bfb-8e8f-df2b511af56d","order_by":3,"name":"Teresa Romeo","email":"","orcid":"","institution":"Stazione Zoologica Anton Dohrn","correspondingAuthor":false,"prefix":"","firstName":"Teresa","middleName":"","lastName":"Romeo","suffix":""},{"id":432705851,"identity":"c337e617-1055-4312-bb7d-be5737279a3e","order_by":4,"name":"Silvestro Greco","email":"","orcid":"","institution":"Stazione Zoologica Anton Dohrn","correspondingAuthor":false,"prefix":"","firstName":"Silvestro","middleName":"","lastName":"Greco","suffix":""},{"id":432705853,"identity":"d380d7ba-aa6f-44d3-b09c-3fb1fbb2c99a","order_by":5,"name":"Augusto Passarelli","email":"","orcid":"","institution":"Stazione Zoologica Anton Dohrn","correspondingAuthor":false,"prefix":"","firstName":"Augusto","middleName":"","lastName":"Passarelli","suffix":""},{"id":432705854,"identity":"e975b9c2-a7b8-44ce-a176-831f5c1493df","order_by":6,"name":"Livio Favaro","email":"","orcid":"","institution":"University of Torino","correspondingAuthor":false,"prefix":"","firstName":"Livio","middleName":"","lastName":"Favaro","suffix":""},{"id":432705855,"identity":"289aa3a2-5270-4e62-bfcc-114687117005","order_by":7,"name":"Francesco Caruso","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBAC9gZ0EX4waYBbC88BdBFJsCEGuPVgajE4QMAaHunDzx78qKiT52dgfvi5sq1Ozvj88WcPGAr+4NbCl2Zu2HPmsOHMBjZjybNth43NbiSkG+BzmD0Pg5k0Y9uBBIMDPAySjW0HErfdYDgmgdcvPOzfpBn/1YG0MP9sbKtL3Nx/sI2AFh6gLQ3MIC1sQFuYEzcwJLMR0lIm2XMM6JdmNjPLhnOHjSVupLFJJBgY43PYNokfNcAQY29+fLOhrE6Ov//4M4kPf+RwakEAZmROAhEaRsEoGAWjYBTgBgBq4Edga4GcGgAAAABJRU5ErkJggg==","orcid":"","institution":"Stazione Zoologica Anton Dohrn","correspondingAuthor":true,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Caruso","suffix":""}],"badges":[],"createdAt":"2025-03-13 17:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6222010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6222010/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-32142-3","type":"published","date":"2025-12-21T15:58:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79128547,"identity":"8e9c9b8d-2b57-4001-b0f7-abacf2f3e163","added_by":"auto","created_at":"2025-03-24 18:04:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1046104,"visible":true,"origin":"","legend":"\u003cp\u003eSpectrograms with acoustic signals of \u003cstrong\u003ea\u003c/strong\u003e) delphinids (nfft=2048, overlap=50, Hann window) , \u003cstrong\u003eb\u003c/strong\u003e) sperm whale (nfft=4096, overlap=75, Hann window) and \u003cstrong\u003ec\u003c/strong\u003e) fin whale whale (nfft=4096, overlap=50, Hamming window). Spectrograms generated using MATLAB R2023b\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6222010/v1/231e2287fc9a17b80ce00552.png"},{"id":79129381,"identity":"a43de455-4ab4-4a57-8366-eb40dbdae30c","added_by":"auto","created_at":"2025-03-24 18:12:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":460177,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Spatial distribution of cetaceans, where each symbol represents a different species: delphinids, sperm whale, and fin whale detections along the path of the glider. (\u003cstrong\u003eb\u003c/strong\u003e) Bar plot with error bars showing the mean detection of acoustic signals by both delphinids and sperm whales as a function of the bathymetry, and (\u003cstrong\u003ec\u003c/strong\u003e) as a function of the distance to the nearest coast. Map generated using QGIS 3.28.15.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6222010/v1/489cee2f755385e2a2d05683.png"},{"id":79128293,"identity":"29bfb710-de1c-46c2-a45b-28347bc45274","added_by":"auto","created_at":"2025-03-24 17:56:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68001,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal distribution of delphinids signals. GAMs plots with hour of the day as fixed effect. The shaded areas represent the 95% confidence intervals. \u003cstrong\u003ea\u003c/strong\u003e) clicks presence (R\u003csup\u003e2\u003c/sup\u003e = 0.968, deviance explained [d.e.] = 97.8%); \u003cstrong\u003eb\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003ewhistle presence (R\u003csup\u003e2\u003c/sup\u003e = 0.75, d.e.= 83.1%); \u003cstrong\u003ec\u003c/strong\u003e) pulsed sound (R\u003csup\u003e2\u003c/sup\u003e = 0.885, d.e.= 93.3%).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6222010/v1/05fb736a33881d6c6213f364.png"},{"id":79128295,"identity":"ed7d87f5-2289-4f05-bda0-828643dc33fb","added_by":"auto","created_at":"2025-03-24 17:56:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":847248,"visible":true,"origin":"","legend":"\u003cp\u003eSpectrograms with acoustic signals of \u003cstrong\u003ea\u003c/strong\u003e) ship, \u003cstrong\u003eb\u003c/strong\u003e) sonar (nfft=2048, overlap=50, Hann window) and \u003cstrong\u003ec\u003c/strong\u003e) low- frequency pulses (nfft=4096, overlap=75, Blackman-Harris window). Spectrograms generated using MATLAB R2023b.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6222010/v1/573a75ddf3c48e7dec11b74c.png"},{"id":79128302,"identity":"5a5143cd-ac56-49ef-9347-1f2cc2fd6b09","added_by":"auto","created_at":"2025-03-24 17:56:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":325133,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Spatial distribution of anthropogenic sound sources, where each symbol represents a different source: ships, low-frequency pulses, and sonar along the glider track. (\u003cstrong\u003eb\u003c/strong\u003e) Bar plot with error bars showing the mean detection of acoustic signals from shipping noise, low-frequency pulses, and sonar as a function of bathymetry, and (\u003cstrong\u003ec\u003c/strong\u003e) as a function of the distance to the nearest coast. Map generated using QGIS 3.28.15.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6222010/v1/a1261ae15af4a99e60a790cb.png"},{"id":79128298,"identity":"30d72252-5b7d-4edb-b707-43957467389b","added_by":"auto","created_at":"2025-03-24 17:56:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":38560,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal distribution of anthropogenic sources. GAMs with daytime as a fixed effect. The shaded areas represent the 95% confidence intervals. Plot (\u003cstrong\u003ea\u003c/strong\u003e) represents shipping noise (R² = 0.0947, deviance explained [d.e.] = 13.4%), (\u003cstrong\u003eb\u003c/strong\u003e) sonar signals (R² = 0.17, d.e. = 20.6%), and (\u003cstrong\u003ec\u003c/strong\u003e) low-frequency pulses (R² = 0.671, d.e. = 72.6%).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6222010/v1/9e66a191e0e2973df6d11402.png"},{"id":79128548,"identity":"e3335714-bde5-4f71-9b5d-40210249ba5b","added_by":"auto","created_at":"2025-03-24 18:04:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":187320,"visible":true,"origin":"","legend":"\u003cp\u003eWave Glider device: \u003cstrong\u003ea\u003c/strong\u003e) floating surface part;\u0026nbsp;\u003cstrong\u003eb\u003c/strong\u003e) towed hydrophone;\u0026nbsp;\u003cstrong\u003ec\u003c/strong\u003e) general view of the glider, modified from https://liquid-robotics.com/.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6222010/v1/9c392cb61be1603b63167c50.png"},{"id":79128300,"identity":"137046d4-8b9e-452d-bd66-81976589309d","added_by":"auto","created_at":"2025-03-24 17:56:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":444129,"visible":true,"origin":"","legend":"\u003cp\u003eWave Glider track in the central Med Sea. The map shows the acoustic data collection (dashed line no data, solid line acoustic data). Map generated using QGIS 3.28.15\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6222010/v1/c0d9975aa0c363d1bda1198e.png"},{"id":98813983,"identity":"e3565fd3-5e49-4b04-86ad-247d1ab3a22a","added_by":"auto","created_at":"2025-12-22 16:09:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4356567,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6222010/v1/e8fabb48-3472-427f-90f1-6fb63d8b27c2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Wave Glider for Passive Acoustic Monitoring of Cetaceans and Anthropogenic Sounds in the Central Mediterranean Sea","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe marine soundscape is shaped by physical and biological sounds as well as anthropogenic ones [1], and it is highly variable both in time and space [2]. The biological activities of marine mammals, fish or crustaceans contribute significantly to the ambient sound in marine ecosystems through the production of sounds [3]. Anthropogenic sounds, now grouped together under the term \"anthropophony\", are derived from a variety of sources that result from human activities [4,5].\u003c/p\u003e \u003cp\u003eIn recent years, Passive Acoustic Monitoring (PAM) has increased as a non-invasive method to monitor the acoustic marine environment, allowing continuous detection of vocalizing animals across large spatial and temporal scales [6], in remote and unknown areas and under any weather conditions [7]. The advancement and widespread implementation of modern technologies have improved the performance of PAM. While traditional PAM methods were based on the installation of fixed acoustic mooring platforms or cabled stations [8], the development of Autonomous Surface Vehicles (ASVs), such as the Wave Glider, has expanded PAM applications [9]. Mobile platforms, when developed and equipped with hydrophones, allow long-term acoustic surveys to record ambient sound and to gather critical information about marine ecosystems [10].\u003c/p\u003e \u003cp\u003eCetaceans are well-suited for acoustics investigations due to their ability to produce different types of sounds essential for vital functions and communication, resulting in a complex and varied acoustic repertoire [11]. Several studies have proven the efficiency of PAM techniques in investigating cetacean diversity [12], their abundance and habitat use [13], behavior [14], as well as in assessing the impact of anthropogenic noise on their biological activities [15]. The introduction of significant levels of underwater noise can add pressures to marine ecosystems and may have harmful effects on cetaceans such as permanent or temporary threshold shifts, behavioral changes and acoustic masking [5]. The latter occurs when a signal emitted by a cetacean is covered by ambient noise or overlapped by another signal with the same frequency [16].\u003c/p\u003e \u003cp\u003eAlthough the Mediterranean Sea represents only 0.8% of the world's ocean surface, it has an extremely high density of maritime traffic, with 30% of all international shipping concentrated in this area [17]. In this basin, ten species of cetaceans are considered resident, three are occasionally sighted and considered visitors, and seven other species have been classified as vagrants in the Mediterranean, highlighting the region as a cetacean biodiversity hotspot [18]. These species face numerous anthropogenic threats, including noise disturbance, collisions, and bycatch [19]. Noise pollution, primarily from maritime traffic, sonars, seismic exploration, and industrial activities (such as offshore wind farms), represents a growing concern for marine conservation in this semi-enclosed basin [20]. Conservation efforts are therefore increasingly aimed at mitigating these threats and maintaining a favorable conservation status of the Mediterranean populations, as required by the EU Habitats Directive [18]. Key initiatives such as the PELAGOS Sanctuary and ACCOBAMS have been central to cetacean conservation in the region. Specifically, knowing the distribution and spatio-temporal abundance of cetacean species in each area is essential to promote effective conservation measures [21].\u003c/p\u003e \u003cp\u003eThis study represents the first-time deployment of a Wave Glider for passive acoustic monitoring of cetaceans in the central Mediterranean Sea. Previous large-scale acoustic surveys, conducted from research vessels, were carried out in this area although limited by weather conditions and extended sampling periods [22,23,24]. The glider provides a more efficient way to detect and classify cetacean sounds, allowing the study of species diversity, acoustic behavior and spatio-temporal distribution. Furthermore, it provides a valuable tool to identify the main anthropogenic noise sources. This approach contributes to the ultimate goal of integrating innovative technologies into marine conservation plans, addressing the dual goals of protecting biodiversity and mitigating human impacts.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAcoustic detections of cetaceans\u003c/h2\u003e \u003cp\u003ePreliminary results on cetacean species diversity in the study area, as well as the diversity of signals produced, were obtained on a large scale using a hydrophone-equipped Wave Glider for PAM. In total, over the 814.2 hours of acoustic data manually analyzed, we identified 3069 files with detections (i.e., presence of at least one signal in a file) of delphinids (48.2% of the analyzed dataset), 125 files with detections of sperm whales (1.96%) and 13 files with detections of fin whales (0.2%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Whistles were the most frequently detected signal produced by delphinids (78.1%), followed by echolocation clicks (60.9%) and pulsed sounds (17.7%). Among sperm whales, regular clicks were found in almost all recordings with detections (99.2%), while creaks and codas accounted respectively for 4.8 and 4% of the sperm whale detections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpatial distribution of cetaceans\u003c/h3\u003e\n\u003cp\u003eThe preliminary analysis of the acoustic dataset allows a large-scale characterization of the presence and spatial distribution of cetacean signals along the glider track (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Delphinids were detected all along the track, showing a wide distribution among the central Mediterranean basin, with a few less detections in shallower waters. This was confirmed by the chi-squared test that highlighted the effect of both bathymetry (chi-squared\u0026thinsp;=\u0026thinsp;673.28, df\u0026thinsp;=\u0026thinsp;2, p-value\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and distance to the nearest coast (chi- squared\u0026thinsp;=\u0026thinsp;21.949, df\u0026thinsp;=\u0026thinsp;2, p-value\u0026thinsp;=\u0026thinsp;1.713e-05) on the detection of delphinids signals and therefore on their spatial distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c). Sperm whale acoustic signals were detected in specific areas of the central Mediterranean Sea: southern Tyrrhenian Sea, western Ionian Sea and southern Adriatic Sea. Regarding the sperm whale encounters, the chi-squared highlighted the same tendencies with an influence of bathymetry (chi-squared\u0026thinsp;=\u0026thinsp;39.244, df\u0026thinsp;=\u0026thinsp;2, p-value\u0026thinsp;=\u0026thinsp;3.007e-09) and distance to the nearest coast (chi-squared\u0026thinsp;=\u0026thinsp;78.999, df\u0026thinsp;=\u0026thinsp;2, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on the spatial distribution of this species, with a complete absence of detection in epipelagic waters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c). Finally, fin whale detections were found only in few recordings acquired in a restricted area of the southern Tyrrhenian Sea.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTemporal distribution (GAM) of delphinids\u003c/h3\u003e\n\u003cp\u003eThe temporal distribution of signals emitted by delphinids during all the survey period was studied by looking at the distribution of the signals detected for each category (clicks, whistles and pulsed sounds) according to the time of their detection. Hence, these GAMs present the prediction of occurrence of delphinid signals according to the hour of the day and highlight the significant efficiency (all three p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of using this variable as a predictor of the probability of presence of delphinids (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A daily trend was identified for signal presence in function of the hour of the day following a circadian rhythm where all three signals but specifically clicks and pulsed sounds were more present during nighttime.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAnthropogenic sound detections\u003c/h3\u003e\n\u003cp\u003eThe main types of anthropogenic sounds detected by the Wave Glider were categorized into ships, sonar and low-frequency pulses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Shipping noise represented the predominant type of sound identified, with 4810 files accounting for 75.6% of the total acoustic file. Next were low-frequency pulses, detected in 1639 files, accounting for 25.7% of the total detections and sonar signals detected in 548 files with 8.6% of the total.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSpatial distribution of anthropogenic sound sources\u003c/h3\u003e\n\u003cp\u003eThe spatial distribution of anthropogenic sound sources recorded by the glider in the central Mediterranean Sea was investigated. Overall, the data revealed an almost continuous presence of anthropogenic noise along the glider's track, suggesting that anthropogenic activities contribute significantly to the marine soundscape of this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The chi-squared test highlighted the effect of both bathymetry (chi-squared\u0026thinsp;=\u0026thinsp;31.961, df\u0026thinsp;=\u0026thinsp;2, p-value\u0026thinsp;=\u0026thinsp;1.148e-07) and distance to the nearest coast (chi-squared\u0026thinsp;=\u0026thinsp;323.4, df\u0026thinsp;=\u0026thinsp;2, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on the detection of shipping noise and therefore on its spatial distribution along the Wave Glider track (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, c). Sonar was predominantly found in the southern Tyrrhenian Sea with few other sporadic detections. The chi-squared showed an effect of both parameters on sonar detections (for bathymetry chi-squared\u0026thinsp;=\u0026thinsp;163.75 and for distance to the coast chi-squared\u0026thinsp;=\u0026thinsp;86.512, df\u0026thinsp;=\u0026thinsp;2, p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As for shipping noise, the low-frequency pulses were spread in all the study area, with few detections in the Sicily Channel and with an effect of bathymetry (chi-squared\u0026thinsp;=\u0026thinsp;61.102, df\u0026thinsp;=\u0026thinsp;2, p-value\u0026thinsp;=\u0026thinsp;5.394e-14) and distance to the coast (chi-squared\u0026thinsp;=\u0026thinsp;104.59, df\u0026thinsp;=\u0026thinsp;2, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on their distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTemporal distribution (GAM) of anthropogenic sound sources\u003c/h2\u003e \u003cp\u003eThe temporal distribution of anthropogenic signals during all the survey period (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) was studied by looking at the distribution of the sound sources detected for each category (ships, sonar and low-frequency pulses) according to the time of their detection. Here, the hour of the day was a suitable predictor for low-frequency pulses presence (p-value\u0026thinsp;=\u0026thinsp;0.0091) that occurs more in early morning and for sonar (p-value\u0026thinsp;=\u0026thinsp;0.449). However, the continuous presence of shipping noise was observed over the day and the GAM did not highlight any temporal tendency for this signal (p-value\u0026thinsp;=\u0026thinsp;0.0547).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe use of Autonomous Surface Vehicles (ASVs) has demonstrated their versatility and potential in a wide range of scientific fields [8]. The Wave Glider has proven its versatility in underwater acoustic research, demonstrating the ability to collect high quality data on marine soundscapes and applications in diverse marine environments [10,25]. Previous studies have shown its efficiency in gathering data on cetaceans: investigating acoustic presence and habitat exploitation in relation with environmental variables of humpback whales (\u003cem\u003eMegaptera novaeangliae\u003c/em\u003e) in the Hawaiian archipelago (Pacific Ocean) [26]; assessing delphinid diversity, abundance, and acoustic activity in the southwestern Atlantic Ocean [13]. Although Autonomous Underwater Vehicles (AUVs), such as profiling gliders, have previously been employed in the Mediterranean to detect and monitor cetaceans [27,28], this work represents the first application of a hydrophone-equipped Autonomous Surface Vehicle (ASV) in the region. Specifically, the Wave Glider used in this study represents a novel approach to cetacean monitoring in the basin. This long-term large-scale survey allowed us to detect the presence of different species of cetaceans based on their vocalizations confirming their potential applications in both coastal and oceanic environments [29,30].\u003c/p\u003e \u003cp\u003eDelphinids signals were detected almost everywhere along the path of the glider, with less detections in shallower waters, as highlighted by the chi-squared analysis of bathymetry influence. Although this study did not distinguish between species, it is well known that the striped dolphin is the most abundant cetacean species in the Mediterranean Sea [31], with a marked preference for pelagic waters off the continental shelf [18]. Moreover, its abundance tends to be lower in the Sicily Channel than in the Tyrrhenian and Ionian Seas [32]. Taking the delphinids in general, a decrease in both abundance and species diversity from west to east of the Mediterranean Sea basin has been previously observed [31,33]. While our study concentrated on overall trends, further analyses could use advanced techniques to analyse narrow-band signals, such as whistles. This approach could help identify different species and improve our understanding of their distribution and behavior. Regarding the temporal distribution of the types of signals emitted by delphinids, the GAMs revealed that the hour of the day seems to be a relevant predictor of their presence, with evidence of a clear daily pattern covering both day and night. Specifically, echolocation clicks and pulsed sounds, which are related to foraging activity, are more abundant during dark hours, possibly due to trophic chain dynamics, such as nychthemeral migrations [34]. This finding of a notable diel pattern in click rates is further supported by the study of Papale et al. [35] in the Sicily Channel. Furthermore, whistles are more frequently produced during early morning and early night, likely reflecting the use of social interactions during traveling and daytime activities, as well as during foraging when associated with clicks and pulsed sounds [36]. Therefore, PAM is confirmed to be an efficient technique to identify circadian cycles in the acoustic activity of delphinids, gathering more information on their behavior than traditional visual methods [11]. The wide distribution of dolphins also provides valuable insights into their ecological role in different ocean zones as fundamental top predators of marine ecosystems.\u003c/p\u003e \u003cp\u003eMoreover, the Wave Glider was also able to detect acoustic signals from a deep diving species with specific habitat preference, the sperm whale. Sounds from sperm whales were identified in three main areas: the Tyrrhenian Sea, the Ionian Sea, and the Southern Adriatic Sea. These are in line with previous acoustic detections of the species [23], reinforcing the ecological importance of these regions as potential hotspots for sperm whales. Furthermore, acoustic and visual detections of the species have previously been shown not to follow a uniform distribution in each area of the Mediterranean [37,38], explaining the influence of environmental variables leading to habitat partitioning. In this study, the chi-squared highlighted as well the influence of both bathymetry and distance to the coast on the probability of detection. The results are consistent with earlier studies showing a strong association between sperm whale distribution and specific topographic features, such as submarine canyons and seamounts [39]. For example, in the Tyrrhenian Sea, Mussi et al. [40] observed the presence of sperm whales in areas characterized by sharp bathymetric gradients, while similar patterns were observed in the Ionian Sea [41,42] and Ligurian Sea [43]. The ecological importance of submarine canyons and seamounts for marine mammals is well established. These features are recognized as keystone habitats due to their role in increasing food availability through oceanographic processes such as nutrient upwelling [44]. The results of this study highlight the need of preserving these remote areas in the Mediterranean Sea.\u003c/p\u003e \u003cp\u003eFin whales\u0026rsquo; detections were few during this dynamic PAM survey and probably underestimated as only mature males produce vocalizations [45] that are usually detected in low background noise conditions [46]. Indeed, low-frequency anthropogenic noise such as shipping noise or low-frequency pulses could lead to the masking of their calls and prevent their detection [47], especially with sensors close to the surface. Nonetheless, this study allowed us to monitor the presence of fin whales in the southern Tyrrhenian Sea, which can coincide with their transit route between a summer feeding ground in the Ligurian Sea [48] and a winter-feeding ground around Lampedusa [49]. This transition was also demonstrated through satellite tagging [50]. The species occurrence is higher during late spring/summer period in the northwestern Mediterranean Sea, following an aggregation-dispersion migration scheme [51]. Then, traveling southwards to reach the Sicily Channel during winter [50]. The acoustic detections reported here highlight the potential of PAM methods in monitoring fin whale movements even in a noisy environment.\u003c/p\u003e \u003cp\u003eAmong anthropogenic sources, shipping noise was the most prevalent, followed by low-frequency pulses and sonar signals. These types of human-noisy activities were detected almost continuously along the glider's path, with spatial variability influenced by bathymetry and distance to the coast. Sonar detections were concentrated in the southern Tyrrhenian Sea, while shipping noise and low-frequency pulses were more widely distributed. In particular, these occurred more frequently in the early hours of the morning, while sonar and shipping noise did not show a clear daily pattern according to the temporal analysis. These results highlight the need for effective management strategies to mitigate anthropogenic noise, such as changing shipping routes or limiting activities in key habitats.\u003c/p\u003e \u003cp\u003eAutonomous Surface Vehicles like the Wave Glider are valuable tools for continuous passive acoustic monitoring, gathering data on sound sources, which can be used to assess mitigation measures' effectiveness, such as regulating vessel routes [52]. These vehicles are very useful to monitor remote and unknown areas, especially for large and long-term surveys, filling gaps left by traditional monitoring methods. Based on our preliminary results, the Wave Glider can be considered suitable platforms to monitor the spatio-temporal distribution and acoustic behavior of cetaceans, as well as to study potential overlap with the presence of anthropogenic sounds. Indeed, many areas of the Mediterranean Sea are subject to human activities including shipping traffic and offshore operations posing a significant threat to marine life, especially for cetaceans. Human-made noise affects cetacean populations through habitat loss [53] and changes in acoustic behavior [54]. Despite this, recent studies demonstrate that noise pollution, classified as a sensory pollutant, is relatively possible to manage [55]. In this context, the track of the glider has been defined in the frame of Environmental Impact Assessment (EIA) plans before the potential implementation of offshore wind farms. Therefore, this study demonstrates the need to designate Important Marine Mammal Areas (IMMAs) in remote and offshore waters, which are critical ecological regions already heavily affected by human activities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWave Glider and Study Area\u003c/h2\u003e \u003cp\u003eThe Wave Glider is a remotely controlled autonomous surface vehicle made of two parts connected to one another by an 8-meter umbilical cable (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The floating part on the surface is equipped with solar panels to provide power to the system, a navigation system programmable and modifiable, satellite communication, and multi-parameter customizable oceanographic sensors (e.g. CTD and fluorometry). The underwater part - called the sub- that captures the wave surface motion and through the rising and falling of its six wings transforms it into forward motion to propel the vehicle forward. In addition, a solar electrical thruster is available for extra speed and mobility in case of challenging ocean conditions. The Wave Glider used in this study is the SV3 074 model developed by Liquids Robotics, which is particularly suitable for long-term missions with an endurance of up to one year. For our study, it was also equipped with a single towed hydrophone (Porpoise, RS Aqua, UK) with a sensibility of -193.3 dB re 1V/\u0026micro;Pa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe vehicle was deployed in the central Mediterranean Sea from the 13th of September 2022 from the island of Minorca (Spain) to 17th December 2022, and from 15th January 2023 to 3rd March 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The acoustic survey started on the 30th of September, the previous period being a test with no acoustic acquisition of data. This ASV crossed the southern Tyrrhenian Sea, the Sicily Channel, and the Ionian Sea for about 2,000 nautical miles at an average speed of about 1 knot, before reaching the southern Adriatic Sea. The glider transects on the path were defined based on potential future offshore wind farm development areas in the central Mediterranean Sea.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eAcoustic data acquisition started on the 30th September 2022 (the previous period being a navigation test with no data acquisition) and recordings were acquired continuously except for the following days due to system failure and a time interval between deployments: from 26th to 30th November, the 10th December, from 13th to 15th December, from 18th December 2022 to 14th January 2023, and from 24th February to 26th February 2023. The continuous recording setting yielded a total of 19,115 files of 460 s in a .flac format (around 2 TB), acquired at a sampling rate of 192 kHz (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of data collection effort. Key metrics include the duration of the campaign, days of Passive Acoustic Monitoring (PAM), number of files recorded, data storage requirements, total recording hours, nautical miles covered, and the average speed of the survey vessel during the observation period.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026deg; days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026deg; days of PAM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026deg; files\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMemory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHours recorded\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNautical miles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAverage Speed\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13th Sep\u003c/p\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3rd March 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19,115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;2 TB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e~\u0026thinsp;2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e~\u0026thinsp;1kt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eA subset considered representative of the original dataset (6,372 files) was kept for manual analysis, that consisted in spectrogram visualization (2048-point FFT and 1012-point Hanning Window) and audio listening using the open-source software Raven Lite (Bioacoustics Research Program, Cornell Lab of Ornithology). We assessed the presence/absence of cetaceans and anthropogenic sources in each recording with a distinction between broadband and low-frequency acoustic sources. Acoustic signals emitted by odontocetes (delphinids \u0026ndash; Fam. Delphinidae or sperm whales - \u003cem\u003ePhyseter macrocephalus\u003c/em\u003e) were distinguished into social and echolocation sounds. These were classified into different categories: echolocation clicks, whistles and pulsed sounds for delphinids; regular clicks, creaks and codas for sperm whales. In addition, to identify the low-frequency calls of mysticetes (fin whales - \u003cem\u003eBalaenoptera physalus\u003c/em\u003e), a downsample of each acoustic file, set to 1.6 kHz, was performed in MATLAB (MathWorks, Natick, MA, USA). Each recording was then displayed as a spectrogram, and the audio file was listened to at accelerated speed (5x). This method improves the clarity of vocalizations by making them more audible and easier to identify. The objective was to report the presence of different cetacean species and anthropogenic sound sources (ships, sonar, low-frequency pulses) along the Wave Glider track. The entire dataset was analyzed by three expert PAM operators to limit the observer bias. To assess consistency among observers, interrater reliability was assessed on a 1% sample of the dataset using Cohen's kappa, as described in McHugh [56]. The analysis resulted in a kappa value of 0.95, indicating an 'almost perfect' level of agreement, with 82\u0026ndash;100% of the data considered reliable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo have more insights on the spatial distribution of cetaceans and anthropogenic sources along the Wave Glider track, we recovered mean depth data derived from the EMODnet Bathymetry portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.emodnet-bathymetry.eu\u003c/span\u003e\u003cspan address=\"http://www.emodnet-bathymetry.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and calculated the nearest distance to the coast (mainland and islands) on QGIS software v.3.28.15 (QGIS Development Team, 2024). These two parameters were used to discriminate three corresponding zones based on their historical use in marine research [57]: Epipelagic (\u0026lt;\u0026thinsp;200m), Mesopelagic (200-1000m) and Bathypelagic (\u0026gt;\u0026thinsp;1000m) for the bathymetry; \u0026lt;25 km, 25\u0026ndash;50 km and \u0026gt;\u0026thinsp;50 km for the coast distance.\u003c/p\u003e \u003cp\u003eA chi-squared test was conducted using the R software (R Foundation for Statistical Computing) to explore the effect of both bathymetry and distance to the coast on the probability of detection and therefore on the presence of delphinids and sperm whales along the Wave Glider track. Fin whale detections were not included due to the few detections reported, and regarding the long propagation range of their calls [46,58]. Moreover, the same statistical analysis was performed on the three different anthropogenic sound sources identified (ships, sonar and low frequency pulses).\u003c/p\u003e \u003cp\u003eFinally, the temporal patterns of signals emitted by the most common cetacean source, delphinids (clicks, whistles, and pulsed sounds), and anthropogenic sources were examined using a Generalized Additive Model (GAM) [59]. This approach was used to study the variation of occurrence of each specific signal according to the hour of the day. Binomial based (presence/absence) GAMs with a logit link function and the hour of the day as a smoothed fixed effect were performed using the \u003cem\u003emgcv\u003c/em\u003e R package [60].\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003ch1\u003eData Availability\u003c/h1\u003e\n\u003cp\u003eData is available on request to the corresponding author.\u003c/p\u003e\n\u003ch1\u003eAcknowledgements\u0026nbsp;\u003c/h1\u003e\n\u003cp\u003eThis research would not have been possible without the work of many colleagues at the Stazione Zoologica Anton Dohrn. In particular, the authors would like to thank Robin Caron, Florence Rappin and Gerardo Sorrentino. During the writing of this manuscript, Sara Ferri was supported by the \u0026nbsp;PhD program of the Stazione Zoologica Anton Dohrn and \u0026nbsp;by the University of Turin through a SUSTNET PhD scholarship (n\u0026deg;39-033-31-DOT21BX3F7-8932). The PhD funder had no role in study design, data collection, or data analysis.\u003c/p\u003e\n\u003ch1\u003eAuthor\u0026nbsp;contributions statement\u003c/h1\u003e\n\u003cp\u003eF.C., S.F., A.E. conceptualization. F.C., S.C., A.P., S.F., A.E. methodology. F.C., S.F., A.E. data analysis. S.F., A.E. writing and original draft preparation. F.C., L.F. writing and review and editing. \u0026nbsp; F.C. supervision. S.C., A.P., T.R. project administration. \u0026nbsp;T.R., S.G. funding acquisition. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch1\u003eAdditional\u0026nbsp;information\u003c/h1\u003e\n\u003cp\u003eTo\u0026nbsp;include,\u0026nbsp;in\u0026nbsp;this\u0026nbsp;order: \u003cstrong\u003eAccession codes\u0026nbsp;\u003c/strong\u003e(where\u0026nbsp;applicable); \u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e(mandatory statement).\u003c/p\u003e\n\u003cp\u003eThe corresponding author is responsible for submitting a competing interests statement on behalf of all authors of the paper.\u003c/p\u003e\n\u003cp\u003eThis statement must be included in the submitted article file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eErbe, C., McCauley, R. \u0026amp; Gavrilov, A. Characterizing Marine Soundscapes. in \u003cem\u003eThe Effects of Noise on Aquatic Life II\u003c/em\u003e (eds. Popper, A. 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Hierarchical generalized additive models in ecology: an introduction with mgcv. \u003cem\u003ePeerJ\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, e6876 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Autonomous vehicles, Mediterranean basin, Underwater acoustics, Marine mammals, Cetacean hotspots, Marine conservation","lastPublishedDoi":"10.21203/rs.3.rs-6222010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6222010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePassive Acoustic Monitoring (PAM) has become a widely used method to study cetaceans, especially for populations facing escalating threats from noisy human activities, including shipping traffic, fishing industry and marine constructions. Here, we conducted the first study using an Autonomous Surface Vehicle (ASV) to explore the distribution and acoustic behavior of cetaceans and to characterize anthropogenic sound sources in the central Mediterranean Sea. A Wave Glider equipped with a single-towed acoustic recorder was deployed from 13th September 2022 to 3rd March 2023. The recording yielded 19,115 files of 460s each (about 2 TB), a third of which was kept for a preliminary analysis based on spectrogram visualization and audio listening. The results showed that nearly half of the dataset contained delphinid signals (Delphinidae), followed by sperm whales (\u003cem\u003ePhyseter macrocephalus\u003c/em\u003e) and fin whales (\u003cem\u003eBalaenoptera physalus\u003c/em\u003e), with notable hotspots in the southern Tyrrhenian and the Ionian Sea. Moreover, the almost continuous detection of anthropogenic sources highlighted the widespread acoustic impact of human activities in the area. These findings demonstrate the value of passive acoustics in the use of autonomous vehicles as a versatile tool for large-scale and long-term monitoring, offering a promising approach to support conservation efforts for vulnerable species while advancing strategies to mitigate human impacts on marine ecosystems.\u003c/p\u003e","manuscriptTitle":"A Wave Glider for Passive Acoustic Monitoring of Cetaceans and Anthropogenic Sounds in the Central Mediterranean Sea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 17:56:49","doi":"10.21203/rs.3.rs-6222010/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-19T08:51:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T07:50:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210394656424059512435173897435465600403","date":"2025-08-09T03:49:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T12:23:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253990949463503729701034059337112537683","date":"2025-04-07T01:00:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236453789728884782540877899810853897056","date":"2025-03-23T13:33:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-21T12:59:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-21T12:55:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-21T11:13:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-20T15:36:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-13T17:10:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8facb122-6146-4b72-914c-049a91c9a3b5","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46082175,"name":"Biological sciences/Ecology"},{"id":46082176,"name":"Earth and environmental sciences/Ocean sciences/Marine biology"}],"tags":[],"updatedAt":"2025-12-22T16:02:36+00:00","versionOfRecord":{"articleIdentity":"rs-6222010","link":"https://doi.org/10.1038/s41598-025-32142-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-21 15:58:15","publishedOnDateReadable":"December 21st, 2025"},"versionCreatedAt":"2025-03-24 17:56:49","video":"","vorDoi":"10.1038/s41598-025-32142-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-32142-3","workflowStages":[]},"version":"v1","identity":"rs-6222010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6222010","identity":"rs-6222010","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}