# Passive Acoustic Monitoring of fish with Distributed Acoustic Sensing

Antoine Eceiza, Anthony Sladen, Olivier Brunel, Nicolas Bonnet, Vincent

Gaglio, Raphaël Simonet, Frédéric Mittaine, Jean-Michel Cottalorda

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## To cite this version:

Antoine Eceiza, Anthony Sladen, Olivier Brunel, Nicolas Bonnet, Vincent Gaglio, et al. Passive Acoustic Monitoring of fish with Distributed Acoustic Sensing. 2025. hal-05293208

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Passive Acoustic Monitoring of fish with Distributed Acoustic Sensing<sup>a)</sup>

Antoine Eceiza,¹, b) Anthony Sladen,¹ Olivier Brunel,² Nicolas Bonnet,³ Vincent Gaglio,³
Raphaël Simonet,³ Frédéric Mittaine,⁴ and Jean-Michel Cottalorda⁵

1) Université Côte d'Azur, CNRS, Géoazur, Sophia Antipolis, 06560 Valbonne,
France

2) Monaco Oceanographic Institute, Monaco

3) Environment Department, Princely Government of Monaco,
Monaco

4) Cosma, Nice, France

5) Université Côte d'Azur, CNRS, ECOSEAS, 28 Avenue Valrose, 06000 Nice,
France

(Dated: 30 September 2025)
Underwater soundscapes are critical indicators of marine ecosystem health, but traditional passive acoustic monitoring (PAM) with hydrophones is spatially limited and logistically challenging for long-term observations. This study investigates the potential of distributed acoustic sensing (DAS) to overcome these limitations. A field trial was conducted using a bespoke fiber-optic cable deployed off the coast of Monaco, focusing on three sound-producing Mediterranean species: *Ophidion rochei*, *Sciaena umbra*, and *Epinephelus marginatus*, all known for their distinct acoustic signatures during reproductive activity. DAS recordings reveal species-specific patterns consistent with documented behaviors, and validated by simultaneous hydrophone recording. Thanks to its high sensor density, DAS enables the isolation and localization of individual sounds over tens of meters. The results demonstrate that DAS can reliably detect and locate fish sounds under real-world conditions, while also assessing ambient noise levels. If the DAS setup of this experiment may not match the performance of conventional hydrophones at frequencies above 1kHz, the many other advantages of the approach point to a high potential for marine conservation in the context of climate change and biodiversity loss.

<footnote>a) Preliminary results of this work were presented during the talk "Unlocking the Potential of Distributed Acoustic Sensing for Ocean Monitoring and Management", One Science Ocean Congress, Nice, France, June 2025.</footnote>

<footnote>b) antoine.eceiza@geoazur.unice.fr</footnote>
I. THE BIOPHONY KNOWLEDGE GAP AND THE LIMITATIONS OF TRA-
DITIONAL MONITORING

The oceans cover more than 70% of the Earth's surface and play a crucial role in the planet's health by absorbing carbon dioxide (Sabine et al., 2004), producing approximately half of the oxygen (Grégoire et al., 2023), and hosting vast ecosystems. However, they are facing increasing threats from human activities and climate change. Global pressures, including temperature rise, eutrophication, coastal urbanization, invasive species, and over-fishing, modify ecosystem structure worldwide (Boudouresque et al., 2024; Coll et al., 2012, 2010; Klaoudatos et al., 2024; Lejeusne et al., 2010; Tsirintanis et al., 2022). Anthropogenic underwater noise made by dredging, seismic surveys, and naval activities all contribute to acoustic habitat degradation (Carmezim et al., 2022; Di Franco et al., 2020).

These worldwide trends are most evident in highly populated, semi-enclosed seas, such as the Mediterranean, where all these factors have led to an alarming proportion of threatened marine species (Klaoudatos et al., 2024). At least 20% of the fish taxa are now under threat of extinction in the area, combined with a significant lack of data according to the European Red List of Fishes (Nieto et al., 2015).

Sound is a fundamental communication channel in aquatic environments (Radford et al., 2014). Unlike light or chemical signals, acoustic signals can travel more effectively over long distances, making them indispensable to many fish species for essential activities such as reproduction, or territorial defense (Lobel et al., 2010; Rountree et al., 2006). A noisy environment reduces effective signal detection range by several hundred meters (Codarin
et al., 2009). Limiting that acoustic disturbance is increasingly recognized as an essential measure for preserving marine biodiversity (European Commission, 2017; Nedelec et al., 2022).

The ensemble of sounds produced by living organisms, or “biophony”, is an indicator of biodiversity and ecosystem health (La Manna et al., 2021; Staaterman et al., 2017). Early comprehensive overviews of fish sound production and acoustic perception, such as Tavolga (1971) and Hawkins (1986), emphasized the diversity of mechanisms and ecological roles of acoustic communication in marine fishes. Since then, PAM has been employed to detect and track temporally sound-producing fish and marine mammals at relatively low cost (Desiderà et al., 2019; Gannon, 2008; Lindseth and Lobel, 2018; Rountree et al., 2006; Van Parijs et al., 2009). It has been effectively used for detecting spawning sites, determining nocturnal vocal behavior, and assessing behavioral response to noise pollution (Di Iorio et al., 2021; Picciulin et al., 2013a).

Fish produce a wide range of sounds, including frequency-modulated calls and structured pulse sequences (La Manna et al., 2021). These signals have obvious ecological functions, but many of them remain poorly described at the species level. To help fill the gap, a global effort was undertaken to catalog underwater biological sounds (Parsons et al., 2022). This has resulted in the creation of *FishSounds.net*, a platform that groups recordings and references for more than a thousand species. While this website is a valuable resource for scientists working on fish bioacoustics (Looby et al., 2023), the vast majority of fish biophony remains undocumented: 96% according to Looby et al. (2022). This significant knowledge gap is partly due to some limitations of traditional Passive Acoustic Monitoring (PAM). For in-
stance, conventional hydrophone deployments cover limited spatial scales, requiring manual installation and retrieval, and multi-sensor networks are costly and logistically cumbersome.

Consequently, it is impractical to monitor large systems and their dynamic (Van Horn et al., 2025). Distributed Acoustic Sensing (DAS) is a photonic technology with several key advantages that could potentially overcome these constraints (Cavanaugh et al., 2024). It enables any fiber-optic cable to function as a dense array of virtual acoustic sensors by detecting phase changes in Rayleigh backscattered laser pulses ($\phi$-OTDR) (Hartog, 2017; Sladen et al., 2019; Xenaki et al., 2025). With 1.3 million kilometers of submarine cables around the world (TeleGeography, 2025), this passive technology represents a unique potential for the knowledge and management of oceans, combining logistic and economic efficiency (Lindsey et al., 2019). Effectively, with DAS, each meter of a fiber-optic cable can act as an independent channel, offering unprecedented spatial resolution and a frequency bandwidth from sub-hertz to tens of kilohertz, depending on the cable length.

DAS was initially developed in the oil and gas sector, particularly for pipeline monitoring (Tanimola and Hill, 2009). Subsequently, it was applied in both land and ocean geophysics for uses such as seismic detection and monitoring of submarine landslides (Sladen et al., 2019). More recently, its application has been extended to oceanography and ecology, including ship tracking, marine mammal monitoring, and the observation of dynamic processes such as currents and surface waves (Bouffaut et al., 2022; Mata Flores et al., 2023; Meulé et al., 2024; Rivet et al., 2021). The DAS system is even becoming increasingly interesting for capturing and locating a wider range of hydroacoustic signals (Douglass et al., 2023; Rychen et al., 2023).
In this work, we report on a field test using DAS for passive acoustic fish monitoring,
employing a fiber-optic cable deployed in the Mediterranean Sea off the coast of Monaco. We
show that DAS is capable of detecting and distinguishing species-specific signals of known
sound-producing fish such as *Ophidion rochei*, *Sciaena umbra*, and *Epinephelus marginatus*,
with temporal patterns consistent with previously documented behaviors. We also validate
DAS recordings with data from a traditional hydrophone collocated with the cable. Our
results demonstrate that the approach also enables the generation of spatio-temporal maps
of fish activity, allowing for near-real-time acoustic ecosystem surveillance. DAS surpasses
many of the limitations of hydrophone-based PAM, including broad spatial coverage, contin-
uous and real-time operation, minimal maintenance, and high spatial resolution. However,
the technology is relatively recent and still has some limitations, which will be addressed in
the discussion *section IV*.

## II. EXPERIMENTAL SETUP TO EVALUATE THE DAS CAPABILITIES

### A. The advantages provided by the coastal waters of Monaco

The coastal waters of Monaco have been extensively studied since the 1990s, with regular
biological inventory campaigns conducted in the area (Cottalorda and Francour, 2007).
Every three years, the Department of Environment commissions the "Groupe d'Étude du
Mérou" - "Grouper Study Group", a French association active for more than three decades.
They evaluate the population dynamics of dusky groupers (*Epinephelus marginatus*) and
brown meagres (*Sciaena umbra*), as well as the effectiveness of local conservation measures.
These species have been protected in the Principality since 1993 by sovereign ordinances (Monaco Government, 2024). In September 2024, a survey was conducted directly below the Oceanographic Institute of Monaco. During this campaign, researchers counted 90 dusky groupers and 59 brown meagres (Mettey, 2024).

As these species are well-known for their distinctive sounds, this site is particularly well-suited to evaluate the performance of the DAS approach: *Sciaena umbra* produces beamed pulse trains at dusk, making choruses in spawning aggregations (Colla *et al.*, 2018; Picci-ulin *et al.*, 2024, 2013a, 2020), while *Epinephelus marginatus* emits low-frequency booms and growls around sunset during spawning aggregations too (Bertucci *et al.*, 2015; Di Iorio *et al.*, 2021). We will also consider *Ophidion rochei*, which generates nocturnal calls with stereotyped pulse sequences, using a specialized sonic muscle coupled to the swimming blader (Kéver *et al.*, 2016; Parmentier *et al.*, 2010). Overall, this variety of acoustic fish sounds makes the approach relevant for conservation actions (Desiderà *et al.*, 2019).

## B. The deployed Instrumentation

The experiment was conducted between May 14 and May 21, 2025. We used an Op-toDAS unit from Alcatel Submarine Networks, connected to the Monaco Oceanographic Institute. It was coupled to a 505 m, 5 mm diameter, tight-buffered fiber-optic cable with four single-mode fibers from the FS.com manufacturer. The fibers are protected by a he-logical stainless steel armored tube, a stainless steel wire mesh, a layer of Kevlar yarn, and a TPU outer jacket, all designed to provide robust protection of the fibers. This cable was self-deployed along the rocky coastline under the institute (Figure 1), with the help of
divers. Approximately 400 m of fiber were laid underwater, with the first 250 m embedded in rocky substrate and the remaining 150 m laid over sand, ending near an artificial wreck.

The cable was regularly labeled with colored tape for easy geo-referencing of the DAS channels. After deployment, the cable position was refined as accurately as possible using divers' observations and data from the dive tracks.

<figure><img src="image_3.png" /><figcaption>FIG. 1. Map of the Monaco experiment, carried out from May 14 to 21, 2025. The study area is located in Monaco, beneath the Oceanographic Institute. Numbers appearing next to the cable trace represent the distance in meters from the DAS interrogator. Bathymetric data are from the Litto3D PACA 2015 program (SHOM, 2015).</figcaption></figure>

DAS recordings were performed at a sampling frequency of 3125 Hz, with a spatial channel spacing of 1.02 m and a gauge length of 4.08 m. The gauge length corresponds to the portion of fiber over which backscattered light is integrated to generate each measurement.
To compare DAS signals with traditional acoustic data, we installed a professional RTSYS recorder equipped with an HTI-96-MIN hydrophone (sensitivity: -170 dB re 1 V/µPa; frequency range: 2 Hz-30 kHz; sampling frequency: 64 kHz). It was placed directly above the fiber-optic cable (7.4270765°N, 43.7303533°E, depth of 16 m), in a location where groupers and meagres had been observed during deployment. The nearest DAS channel is number 245, 250 m from the interrogator.

<figure><img src="image_4.png" /><figcaption>FIG. 2. Illustrations of equipment used: (a) OptoDAS system connected at the Monaco Oceanographic Institute; (b) RTSYS recorder with hydrophone and mooring near the cable.</figcaption></figure>

### C. Data processing

DAS data are extremely large, as the optical fiber effectively provides hundreds of virtual sensors that record continuously. To efficiently import and process this high-volume dataset in Python, we used the Xdas library (Trabattoni et al., 2025). The data is then converted to
141 WAV files by normalizing the signal channel by channel and exporting it as 16-bit PCM audio
142 using the scipy.io.wavfile module. To visualize and annotate spectrograms from DAS and
143 hydrophones data, we used RavenPro 1.6.5 software (Cornell Lab of Ornithology, 2024). For
144 spectrograms, we chose a 256-point FFT for DAS data and a 6144-point FFT for hydrophone
145 data, with a Hann window and 50% overlap.

146 III. RESULTS

147 A. Raw observation of fish signals with DAS

148 Several fish calls were successfully detected with the DAS. The sound files for DAS spec-
149 trograms are included as supplementary materials. Among these signals, we identified calls
150 from *Epinephelus marginatus*, *Sciaena umbra*, and *Ophidion rochei* (Figure 3). In addition,
151 we consistently observed a strong recurring signal along the section of cable near the ar-
152 tificial wreck, corresponding to the “long tonal call” (LT Call) described in the literature
153 (Desiderà *et al.*, 2019; La Manna *et al.*, 2021). No fish species has yet been associated with
154 this sound.
155

155 The signals recorded are consistent with those reported in the literature: *S. umbra* pro-
156 duce a repetitive sound in the form of a train of 4 to 7 pulses lasting on average 0.5 s. The
157 dominant frequency is between 200 and 400 Hz, with most of the acoustic energy below 2 kHz
158 (Picciulin *et al.*, 2013a,b, 2020). *E. marginatus* emits sound with a fundamental frequency
159 under 100 Hz with harmonics. These growls have an average duration of 100 to 400 ms
160 (Bertucci *et al.*, 2015). *O. rochei* makes sounds of 1 to 5 seconds in duration, consisting of
<figure><img src="image_5.png" /><figcaption>FIG. 3. Spectrogram comparison between DAS-detected signals (left) and signals documented in the literature when possible (right): (a) Regular pulse series by *Sciaena umbra* (SU); (b) Low-frequency downsweeps by *Epinephelus marginatus* (EM); (c) Pulse series with alternating pulse period by *Ophidion rochei* (OR); (d) Long Tonal Calls by unknown fish (LT). The characteristics of signals, such as peak frequency, mean duration, and signal-to-noise ratio (SNR), are listed in Table I.</figcaption></figure>
tens of pulses, with two alternating inter-pulse intervals. The acoustic energy is distributed chiefly between 200 and 400 Hz (Parmentier et al., 2010). However, the long tonal call is rarely mentioned in the literature. We only know that it is a sound with little frequency modulation lasting about a second or more (Desiderà et al., 2019; La Manna et al., 2021).

<table><caption>TABLE I. Characteristics of fish signals in Figure 3 for DAS and literature signals (when found).</caption><thead><tr><th>Fish</th><th>Origin</th><th>Peak Freq.[Hz]</th><th>Mean Duration[s]</th><th>SNR</th></tr></thead><tbody><tr><td rowspan="2">SU</td><td>DAS</td><td>330 ± 17</td><td>0.5 ± 0.1</td><td>15 ± 3</td></tr><tr><td>Literature <sup>a</sup></td><td>287 ± 3</td><td>0.53 ± 0.05</td><td>27 ± 3</td></tr><tr><td rowspan="2">EM</td><td>DAS</td><td>68 ± 2</td><td>0.25 ± 0.14</td><td>19 ± 3</td></tr><tr><td>Literature <sup>a</sup></td><td>81 ± 12</td><td>0.2 ± 0.1</td><td>15 ± 4</td></tr><tr><td rowspan="2">OR</td><td>DAS</td><td>230</td><td>4.3</td><td>15</td></tr><tr><td>Literature <sup>b</sup></td><td>375</td><td>2.9</td><td>16</td></tr><tr><td>LT</td><td>DAS</td><td>68 ± 1</td><td>1.7 ± 0.5</td><td>22 ± 2</td></tr></tbody></table>

## B. Spatio-temporal distribution of fish signals along the fiber-optic cable

The density of DAS sensors enables precise location through antenna processing (beam-forming, differences in signal arrival times, etc.), but this type of analysis is beyond the scope

<footnote>a Private communication</footnote>

<footnote>b (Picciulin et al., 2019)</footnote>
of this study. As a first step to estimate the spatial distribution of fish activity, we visually
examine every five channels to determine whether a given fish species is detected and how
the distribution varies in space and time (Figure 4). Long tonal calls are only heard near
the artificial wreck, where the fiber lies on a sandy seabed. *E. marginatus* and *S. umbra*
are commonly found throughout the area, unlike *O. rochei*, which is found in more specific
locations.

Another way to examine the data is to compare the time series as a function of distance
to assess the detection range of each signal. Figure 5 represents two types of signals detected
over several tens of channels. This representation thus provides an estimate of the detection
range.

C. Comparison between DAS sensors and hydrophone

The observed signals are consistent with those reported in the literature. However, it
is still essential to examine how both DAS and hydrophone perceive the same signal. We
compare the two instruments for a *Sciaena umbra* call, as shown in Figure 6. Although
the overall signal shape is similar across both instruments, we observe some differences: the
hydrophone provides a clearer resolution of individual pulses in a train, but the DAS picks
up the signal from *Sciaena umbra* over a wider frequency range, down to 200 Hz and up to
800 Hz.

More generally, regarding daily noise variations, the octave-band analysis Figure 7 reveals
that the strain rate level recorded by DAS (dB re 1µε.s⁻¹) has a similar shape to the sound
levels measured by the hydrophone (dB re 1µPa) at lower frequencies (≤500 Hz). Both
<figure><img src="image_6.png" /><figcaption>FIG. 4. Images of the different environments encountered (top), Frequency-Distance Diagram (middle), and localization of fish acoustic detections (bottom) along the DAS channels, the night of May 20-21, 2025, 4 pm to 7 am UTC. The red arrows indicate the location of the fiber-optic cable. Around channels 230 and 320, the huge rock blocks suspend most of the cable, which becomes subject to swell-induced vibrations. Simply having smaller rock blocks (middle photo, orange section) significantly reduces the environmental noise. The sandy seabed provides the best recording conditions. These variable seafloor coupling conditions affect the detection capabilities, yet *E. marginatus* and *S. umbra* remain detectable along almost the entire length of the cable.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>FIG. 5. Multi-channel time series: (a) Two Long Tonal calls arriving at 00:21:52 and 00:21:59 UTC on May 21, 2025, detected over 40 meters; (b) *Sciaena umbra* call at 18:33 UTC on May 20, 2025, detected over 25 meters.</figcaption></figure>

instruments particularly reveal an apparent increase in noise during the day, mainly due to coastal and maritime traffic. It is worth noting that an exceptional noise peak was also measured on the night of May 19, due to the massive arrival of boats for the beginning of the Monaco F1 Grand Prix week. Because the interrogator operated at a sampling frequency of 3125 Hz, it was unable to capture the nocturnal rise in high-frequency noise detected by the hydrophone, which corresponds to increased biophony. However, the noise at the
<figure><img src="image_8.png" /></figure>

FIG. 6. Comparison between DAS (left) and RTSYS hydrophone (right) for *Sciaena umbra* signals, on May 21 at 4:36 a.m. UTC. Signals from DAS channels 250 to 260 have been stacked to improve the signal-to-noise ratio.

1000 Hz octave band is significantly stronger in the DAS data, with the daily variations barely noticeable. Above the 500 Hz octave band, the average strain-rate level is too high to observe the nocturnal drop in noise present in the hydrophone data. Because 500 Hz is high for seismic signals, we suspect this background noise level to be instrumental.

The comparison between DAS data and the hydrophone data is inherently limited. While the hydrophone records only hydroacoustic signals, the fiber-optic cable is also susceptible to seismic waves propagating through the seafloor. This distinction is particularly relevant near the coast of Monaco, where anthropogenic activity is high and likely remains significant at night.

The temporal evolution of the transfer function between both DAS and hydrophone signals, calculated for octave bands from 63 to 1000 Hz, is represented in Figure 7(c). For each octave band, the transfer function remains relatively stable throughout the week, with
<figure><img src="image_9.png" /><figcaption>FIG. 7. Octave-band analysis of the acoustic and strain-rate signals over the study period: (a) DAS strain-rate level (dB re 1µε.s⁻¹); (b) Hydrophone sound pressure level (dB re 1µPa); (c) Transfer function (DAS/Hydrophone) expressed as the ratio between the two level estimates for each octave band. Shaded backgrounds indicate daytime (yellow) and nighttime (blue) in UTC.</figcaption></figure>
variations of approximately 0.1 around the average. The ratio between DAS sensors and the hydrophone varies from 0.68 to 0.75, but the average is remarkably stable below 250 Hz (0.685 ± 0.01), and then increases with frequency, reaching 0.71 at 500 Hz and 0.75 at 1 kHz. No difference is observed between day and night, suggesting that the evolution of the ratio at high frequencies is more related to the inherent noise of the DAS sensors than to the ambient noise level. These results indicate the relative stability and linearity of the DAS response, as well as the ability to estimate sound pressure levels from DAS data alone (Bouffaut et al., 2025). Optimizing the cable's physical design is a clear path to improving the response of the DAS sensors. Specifically, we hypothesize that the stainless steel armoring used in the current fiber-optic cable may have attenuated the acoustic signal, thereby limiting the overall sensitivity observed in this study.

## IV. DISCUSSION

The results of our experiment support the idea of DAS as a promising approach for monitoring biophony activity, identifying individuals over extended areas and times with reduced logistical constraints. This work is based on a manually deployed fiber-optic cable, but it clearly demonstrates the potential of DAS in the field of marine bioacoustics. Future work could build on these results by testing existing, more robust submarine telecommunications cables, which already cover the entire oceans. Applying DAS to fish biophony and ecosystem soundscapes could enable large-scale continuous ecological monitoring at a much lower cost than traditional observatories. Another significant benefit of using DAS for PAM is its ability to encompass a range of other processes that have a direct impact on the ecosystem.
In particular, DAS can precisely quantify ocean surface gravity waves (Meulé et al., 2024), seafloor currents (Lin et al., 2024; Mata Flores et al., 2023; Song et al., 2024), or temperature variations down to millikelvins (Pelaez Quiñones et al., 2023). DAS can therefore provide, at no additional logistical and financial cost, a more comprehensive study of the ecosystems.

However, the technology has not been extensively explored at frequencies above a few hundred Hz due to certain limitations. Fundamentally, one of the main technical constraints lies in the relationship between the cable length and the temporal resolution. To avoid range ambiguity, the DAS interrogator must wait for a light pulse to travel to the distal end of the fiber and for its backscattered signal to return before emitting the next pulse. This minimum time interval is the round-trip time, $t_{round-trip}$, which is dependent on the fiber length, $L$. This establishes the maximum possible sampling frequency, $f_s$, of the system. According to the Nyquist-Shannon sampling theorem, the maximum detectable acoustic frequency, $f_{max}$, is half the sampling frequency. Therefore, a longer fiber enforces a lower maximum pulse repetition frequency (PRF), which in turn reduces the bandwidth of the acoustic measurement. This relationship is governed by the following:

$$f_{max} = \frac{f_s}{2} \le \frac{1}{2 \cdot t_{round-trip}} = \frac{c}{4Ln} \quad (1)$$

Where $f_{max}$ is the maximum detectable acoustic frequency, $f_s$ is the sampling frequency (i.e., the PRF), $L$ is the total length of the fiber-optic cable, $c$ is the speed of light in a vacuum (~3 × 10⁸ m/s), and $n$ is the effective refractive index of the fiber-optic core (~1.46). If it remains a general limitation of commercial DAS systems, it would be possible to overcome this limitation through the multiplexing of light pulses, as demonstrated in
(Hong *et al.*, 2025). Yet, all recorded bioacoustic signals in the present study are at less than 1 kHz, which means the cable length limit would only be a concern for cables longer than approximately 50 km.

Gauge length is also an important parameter. Longer gauge lengths improve the signal-to-noise ratio by averaging the response over a greater number of fiber scatterers. In return, they induce a stronger spatial filtering effect on the recorded signals and should reduce the detection of high-frequency signals (Dean *et al.*, 2017). However, the proper response of DAS at frequencies above a few hundred Hertz doesn't seem to follow the conventional theory (Taweesintananon *et al.*, 2021). Indeed, hydroacoustic signals at up to 2.5kHz, equivalent to a wavelength of about 1.6m ($\frac{1500 \text{ m/s}}{2500 \text{ Hz}} \approx 1.6 \text{ m}$), were recorded during a DAS acquisition with a gauge length of 10m (Rychen *et al.*, 2023). This suggests that the spatial integration of the signal over the gauge length does not limit the response to hydroacoustic signals. The gap in sensitivity with respect to hydrophones could be partially addressed by enhancing the fiber-optic cable's sensitivity to hydroacoustic perturbations. This could be done in several ways which could be implemented in synergy: by improving the ability of DAS interrogators to sense at smaller gauge lengths, by enhancing the mechanical response of the cable's jacket to acoustic perturbations (Celli *et al.*, 2023), wounding the fiber inside the cable to increase the number of channels per meter (He *et al.*, 2025), or optimize scatterers inside the fiber to improve the SNR (Shatalin *et al.*, 2021).

The coupling between the cable and the seabed also plays a key role (Igel *et al.*, 2024). As aforementioned, the conversion of hydroacoustic signals into deformation depends on complex coupling mechanisms that remain relatively unexplored (Bouffaut *et al.*, 2025;
Taweesintananon et al., 2021), particularly in heterogeneous environments. Signals are gen-
erally better perceived when cables are taut and laid (or even buried) on soft substrates such
as sand or mud. The deployment on irregular or rocky bottoms in Monaco resulted in poor
coupling in some cable sections, degrading signal clarity and hampering the identification of
biological sources (Figure 4).

The volume of data generated by the DAS is a valid concern: the 7-day deployment generated 1.6 terabytes of raw data, with one channel per meter of fiber, resulting in hundreds of virtual sensors. We selected a 500-meter cable with a sampling rate of 3125 Hz, providing a suitable compromise between resolution and data volume. Processing and manually annotating such datasets would be highly time-consuming. However, this challenge also highlights opportunities for the development of automated approaches, such as machine learning, which are already being tested for marine soundscape monitoring (Duarte et al., 2024; Dufourq et al., 2022).

Noise pollution in these coastal areas is another major limitation. With probably hundreds of boats passing through Monaco daily, we encountered persistent high noise levels that overlapped with fish sounds below 1000 Hz. This background noise must have masked the faint or irregular sounds, making detection more difficult.

V. CONCLUSION

This study demonstrates that DAS can be successfully applied to passive acoustic moni-
toring of sound-producing fish species under real-world conditions. By transforming existing
fiber-optic infrastructure into dense sensor networks, DAS overcomes many of the limita-
tions of traditional hydrophone-based monitoring, particularly in terms of spatial coverage, temporal continuity, and logistical cost. The field experiment off Monaco confirmed that DAS can reliably detect and differentiate species-specific calls, track their spatio-temporal distribution, and characterize ambient noise conditions.

While conventional hydrophones remain superior at higher frequencies and for fine-resolution pulse detection, DAS offers unique advantages in terms of scalability and integration with existing submarine cables. The observed limitations—such as sensitivity at frequencies above 1 kHz, coupling with heterogeneous seafloor substrates, and large data volumes—represent technical challenges that can be addressed through advances in fiber design, interrogator technology, and automated signal analysis.

Beyond fish biophony, DAS holds promise as a holistic ecological observatory, simultaneously capturing information on surface gravity waves, seafloor currents, and other physical processes that shape marine ecosystems. In an era of accelerating biodiversity loss and climate change, the ability to leverage global submarine cable networks for continuous, large-scale monitoring offers an unprecedented opportunity to bridge critical data gaps. Thus, DAS stands as a complementary and transformative tool for next-generation ocean observation, with direct applications to contribute to knowledge acquisition, marine conservation, resource management, and a better understanding of ecosystem resilience.

## 309 ACKNOWLEDGMENTS

This research is part of the DEEP-C project funded by BPIFrance / France 2030, AAP
'Grands Fonds Marins', under grant number DOS0217799. AE is funded by this project.
This research benefited from the technical and logistical support of Monaco's Department of the Environment and of the Monaco Oceanographic Institute. The authors gratefully acknowledge the ACCOBAMS Secretariat for initiating this meaningful collaboration. Finally, we thank Léa Bouffaut from the Cornell Lab of Ornithology for her valuable comments in the preparation of this article.

## 317 AUTHOR DECLARATIONS

### 318 Conflict of Interest

The authors have no conflicts to disclose.

## 320 DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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