# Shipping Noise Impact on Blue Mussel

D Veillard, S Beauclercq, N Ghafari, Alexandra A. Arnold, B Genard, L Sleno, Frédéric Olivier, Anne Choquet, Dror E. Warschawski, I Marcotte, et al.

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## To cite this version:

D Veillard, S Beauclercq, N Ghafari, Alexandra A. Arnold, B Genard, et al., Shipping Noise Impact on Blue Mussel. Marine Ecology Progress Series, In press, 10.3354/meps14830. hal-05056432

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Distributed under a Creative Commons Attribution 4.0 International License
1 Molecular evidence of shipping noise impact on the blue mussel, a key species

2 for the sustainability of coastal marine environments

3 Delphine Veillard¹,†, Stéphane Beauclercq²,†, Nathan Ghafari², Alexandre A. Arnold², Bertrand

4 Genard³, Lekha Sleno², Frédéric Olivier⁴, Anne Choquet⁵, Dror E. Warschawski⁶, Isabelle

5 Marcotte², and Réjean Tremblay¹,*

6

7 ¹Institut des Sciences de la Mer de Rimouski, Université du Québec à Rimouski, Rimouski, QC,

8 G5L 3A1, Canada.

9 ²Department of Chemistry, Université du Québec à Montréal, Montréal, QC, H3C 3P8, Canada.

10 ³Les laboratoires Iso-BioKem Inc., Rimouski, QC, G5L 3A1, Canada.

11 ⁴Laboratoire de ‘Biologie des Organismes et Écosystèmes Aquatiques’ (BOREA), Muséum

12 national d’Histoire naturelle, Sorbonne Université, Université des Antilles, Centre National de

13 la Recherche Scientifique, Institut de Recherche pour le Développement-207, 75005 Paris,

14 France.

15 ⁵Univ Brest, Ifremer, CNRS, UMR 6308, AMURE, IUEM, 29280 Plouzané, France.

16 ⁶Laboratoire des Biomolécules, LBM, CNRS UMR 7203, Sorbonne Université, École Normale

17 Supérieure, PSL University, 75005 Paris, France.

18

19 *Corresponding author: Réjean Tremblay (rejean_tremblay@uqar.ca)

20

21 †These authors contributed equally to this paper.

22
23 **Abstract**

The global anthropogenic oceanic noise due to shipping is predicted to double every 11.5 years, putting marine organisms at risk. While the impact of noise on marine mammals is well documented, its effect on molluscs, which hold immense economic and ecological importance, remain largely unknown. To investigate the consequences of noise on mollusc metabolism during crucial early life stages, blue mussel larvae were exposed to shipping noise in a laboratory setting until the post-larval stage and their metabolome was analysed. Multivariate analyses of the metabolome showed that shipping noise induces stress-related inflammation with increased energy demand, higher protein turnover, and disrupted nervous system activity. Consequently, noise promoted a delayed metamorphosis in suboptimal habitats with greater metabolic costs, which may impact ecosystem and aquaculture sustainability as competent mussel larvae struggle to select suitable development habitats. Without action to limit underwater noise, such impacts could disrupt population structures and marine biodiversity.

36 **Keywords:** Noise pollution, *Mytilus edulis*, Post-larvae, Metabolism, Metabolomics.

37 **1. Introduction**

Over the last 50 years, shipping noise increased the ocean ambient noise in the Northeast Pacific ocean by 3-12 decibels referenced to a pressure of 1 microPascal (dB re 1 µPa) and may double globally every 11.5 years, essentially due to the rising number of commercial vessels (Andrew et al. 2002, McDonald et al. 2006, Frisk 2012, Jalkanen et al. 2022). In 2017, during the Conference of the Parties of the *Convention on the Conservation of Migratory Species* (United Nations) (Conference of the Parties of the Convention on Migratory Species 2017), anthropogenic marine noise – primarily associated with shipping (Chauvaud et al. 2018) (90%) – was recognized as an emerging and poorly understood source of pollution, representing ‘a potential threat to marine species conservation and welfare’. Boats are known to generate low
47 frequency noise (5–500 Hz up to 10,000 Hz) that can reach 190 dB re 1µPa at 1m and propagate
48 over long distances, affecting marine organisms by a variety of effects ranging from physical
49 damage to masking relevant biological signals (Hawkins & Popper 2017, Duarte et al. 2021).

50 For decades, there has been strong evidence of the negative impact of noise on marine
51 mammals and fish, both of which possess important auditory senses. These include
52 behavioural, habitat, and physiological changes leading to an increase in mortality in the most
53 extreme cases (Duarte et al. 2021). The effects on molluscs are still mostly unknown despite
54 studies showing their ability to produce and perceive sounds through their statocysts (balance
55 sensory receptor) and abdominal sense organs (Solé et al. 2023).

56 Molluscs represent 23% of the life diversity in oceans and among them, bivalves are
57 especially known to be key species for the development, functioning, and sustainability of
58 coastal environments (Smaal et al. 2019). Exposure of molluscs to anthropogenic noise could
59 have a dramatic impact on the entire marine ecosystem and therefore must be assessed. The
60 auditory sensing range of adult molluscs is not fully known; nevertheless adult blue mussels
61 (*Mytilus edulis*) have been shown to respond by valve closure to noise between 5 and 410 Hz
62 (Roberts et al. 2015, Chauvaud et al. 2018). Anthropogenic noise impact in bivalves includes
63 oxygen consumption changes (Wale et al. 2019), increased biochemical stress (Vazzana et al.
64 2016), metabolism adjustment (Peng et al. 2016), reduced growth and development (de Soto et
65 al. 2013), modification of the recruitment process (Jolivet et al. 2016, Cervello et al. 2023) and
66 even increase mortality (Day et al. 2017). From a behavioural perspective, anthropogenic noise
67 could disrupt feeding and feeding rate (Spiga et al. 2016), due to a reduction in valve gape
68 opening (Day et al. 2017, Ledoux et al. 2023). However, very few data are available on early
69 life or larval stages of bivalves, which may be more sensitive (Rayssac et al. 2010) and are
70 critical stages as pediveliger larvae use environmental stimuli to settle in an optimal habitat and
71 undertake their metamorphosis (Hadfield & Paul 2001, Lillis et al. 2013, Jolivet et al. 2016,
Schmidlin et al. 2024). If settlement conditions are unsuitable, pediveliger larvae can prolong their pelagic dispersal life and delay their metamorphosis for several weeks (Pechenik 1990, Martel et al. 2014a). Moreover, anthropogenic sound, like vessel noise, increases mussel settlement (Wilkens et al. 2012, Jolivet et al. 2016). Metamorphosis represents a pivotal life-changing event for numerous molluscan species, marking the transition of free-swimming pelagic larvae to attached (sessile) benthic post-larvae (Joyce & Vogeler 2018). At the pediveliger stage, mussel larvae attain “metamorphic competence”, initiating their transformative process with irreversible morphological and physiological changes (Hadfield 2000). These changes include the development of a foot-like structure, loss of the velum, gill development, and secretion of the juvenile shell. This metamorphic transition relies heavily on environmental cues to guide the larvae in selecting an optimal habitat for settlement and subsequent development (Hadfield & Paul 2001) (Fig. 1).

Among bivalves, the blue mussel holds immense economic and ecological importance with a worldwide aquaculture production surpassing wild catches, reaching almost 170,000 t annually, for a revenue exceeding 300 million US$, according to the Food and Agriculture Organization (FAO 2019). Mussel farming is a source of sustainable proteins and omega-3 fatty acids (FAs). Additionally, it plays a crucial role in ecology by promoting biodiversity. Indeed, *Mytilus* is a filter-feeding organism that limits eutrophication by consuming microalgae, which enhances the luminosity for benthic algae and increases the production of other benthic organisms (Borthagaray & Carranza 2007). The mussel sessility and coastal lifestyle make them particularly exposed to repeated or long-lasting anthropogenic noise. Indeed, their habitat often colocalizes with zones of intense ship traffic as is the case in the Gulf of St. Lawrence where 98% of the Canadian mussels are produced. Canada being the first producer in the Americas and third-largest worldwide (Statistics-Canada 2019). With up to 60,000 ships
96 traversing the Gulf on a monthly basis (Pelo & Wootton 2004), effective management and
97 prevention strategies are imperative.

98 The United Nations Convention on the Law of the Sea (UNCLOS) establishes a general
99 obligation for member states to protect and preserve the marine environment in 1982 (Article
100 192, The United Nations 1982). To address noise pollution from ships, both national and
101 international legal frameworks are evolving. The European Union's Marine Strategy
102 Framework Directive, specifically Descriptor 11, aims to prevent adverse effects of energy
103 introduction, including underwater noise (Directive 2008/56/EC, European Union 2008). The
104 International Maritime Organization (IMO) has also taken steps; in 2023, the Marine
105 Environment Protection Committee approved revised guidelines for reducing underwater
106 radiated noise from shipping, effective from October 1, 2023 (IMO 2023). These guidelines
107 build upon previous ones and encourage member states to apply them in collaboration with
108 relevant stakeholders. However, thresholds specifying the acceptability of sound levels are
109 lacking for the enforcement of those guidelines.

110 In this study, the response of mussel larvae to cargo-ship sounds at different intensities
111 recorded in the Gulf of St. Lawrence was evaluated. To archive this, acoustic perturbations were
112 applied during larval metamorphosis and the subsequent stress on the post-larvae's
113 metabolome, i.e., the ensemble of all the small molecules found in an organism, examined
114 along three axes. The first axis focused on lipid mediators, such as prostaglandins and
115 oxylipins. These molecules are responsible for processes such as inflammation (Lee et al.
116 2021). A targeted lipidomics approach was used to analyze them. This approach screened for 11
117 prostaglandins and 38 oxylipins. These oxylipins were derived from eicosapentaenoic, α/γ-
118 linolenic, arachidonic, linoleic, and docosahexaenoic acids. The analysis was performed using
119 liquid chromatography coupled to high-resolution mass spectrometry
120 (LC-HRMS/MS).Secondly, the polar metabolites involved in the general metabolism (mainly
121 organic acids, nucleosides, and nucleotides) were characterized by a combination of untargeted
122 nuclear magnetic resonance (NMR) spectroscopy and LC-HRMS/MS-based metabolomics.
123 This approach was enriched by an extensive targeted study of the amino acid (21 compounds)
124 and energy (27 compounds) metabolisms by LC-HRMS/MS. Finally, the third axis studied the
125 changes in larval performance and behaviour in relation to metabolism alterations, considering
126 that noise is a possible cue for larval settlement. This combined approach based on
127 complementary analytical methods provided new insights in the metabolic pathways impacted
128 by this emerging pollutant in bivalves and its implication for the ecosystems and mussel
129 aquaculture sustainability.

130 **2. Materials and methods**

131 **2.1 Shipping noise sampling**

132 The noise from a 120 m long roll-on/roll-off cargo ship built in 2000 (Nohlan Ava),
133 leaving the most intense and distinguishable noise fingerprint in St-Pierre-et-Miquelon's
134 harbour (France), was recorded between November 2020 and April 2021 using an Aural-M2
135 (Multi-Electronique Inc., Rimouski, QC, Canada) underwater acoustic recorder (sampling
136 frequency of 32 kHz, 16 bits resolution) equipped with a HTI-96-MIN hydrophone (High Tech
137 Inc., Long Beach, MS, USA) with a sensitivity of -165 dB re V/µPa and set with a gain of 22
138 dB. The sampling frequency (32 kHz) allowed to capture the entire acoustic signature of the
139 vessel as the maximum of energy is below 10 kHz. The sound sequence was selected with
140 Audacity® software to create an 80 min soundtrack composed of the sound of the cargo ship
141 arrival (11 min), a 39 min break (simulating handling in harbour), its departure (8.5 min), and a
142 new break of 21.5 min (10.5281/zenodo.10910123). The loudest vessel in the archipelago was
143 selected and looped to standardize exposure to anthropogenic noise, as this approach avoided
144 the complexities of replicating the variable acoustic landscape (e.g., day-night cycles, weekly
145 recreational peaks, and weather-driven fluctuations). The equivalent sound levels, expressed in
146 sound pressure level RMS, are detailed in **Table S1**.

147 **2.2 Mussel larval culture and sound exposure**

148 Blue mussel (*Mytilus edulis*) larvae rearing was performed as described in Rayssac *et al.*
149 (2010) (Fig. 2). Briefly, 50 mm adult mussels from St. Peters Bay in Prince Edward Island
150 (46.42948N, -62.66030W, Canada) were individually induced to spawn by 10°C thermal
151 shock. A ratio of 10 spermatozoids per egg was used for the fertilization in UV ultra-filtered (1
152 µm) seawater at 18°C. Eggs were further washed with UV ultra-filtered seawater and
153 maintained at 18°C. The obtained embryos were divided into 6 tanks of 60 L at a density of 5
154 larvae ml⁻¹ maintained at 18°C until over 50% of the population was at the pediveliger stage
155 (competent larvae) at 17 d post-fecundation (dpf) i.e., eyed larvae, foot development and
156 substrate exploration. Then, the six batches were randomly distributed in the 24 cylinders of 5 L
157 in the four Larvosonic mesocosms tanks. Larvosonic is an innovative tank system composed of
158 six circular culture cylinders of 5 L (unit of replication with 25,000 larvae for each cylinder)
159 designed for the exposure of organisms to sounds which limits classical reverberation and
160 resonance problems (Cervello et al. 2023, Olivier et al. 2023). Temperature (18.1 ± 0.1°C),
161 salinity (60.8 ± 0.4 PSU), photoperiod (15h of light and 9h of night) and light intensity (9.0 ±
162 0.2 40 FC; 97.1 ± 1.7 400 Lux) were monitored during the experiments. During rearing and
163 noise exposure experiments, larvae were fed every two days 60 cells.µl⁻¹ of a mixed suspension
164 of microalgae containing *Pavlova lutheri* (CCMP459), *Tisochrysis lutea* (CCMP1324),
165 *Chaetoceros muelleri* (CCMP1317), *Tetraselmis suecica* (CCMP904), and *Nannochloropsis
166 oculate* (CCMP525) at a ratio of 1:1:1:1:1 equivalent biomass (dry weight). Collectors
167 (constituted from two 30 cm polypropylene ropes) were introduced in the cylinders to allow the
168 settlement of the larvae and the exposition to the recorded shipping sound started. During the
169 metamorphosis stage, pediveliger (17 dpf) to post-larvae (25 dpf) were exposed in each
Larvosonic tank to a different sound treatment for nine days (Fig. 2): low (121 ± 4 dB re 1µPa), medium (127 ± 2 dB re 1µPa), high (151 ± 2 dB re 1µPa), and silence playback with ambient room noise (control; 116 ± 1 dB re 1µPa) (Table S1). These sound levels are maximum emission level and related to a distance from the source (cargo ship), with a greater sound intensity the closer the source is and vice versa, thus corresponding to 18.5 m (high sound exposure), 735 m (medium), 1.8 km (low) from the source and compared to ambient lab conditions (control). This distance was calculated according to the sound propagation loss also called transmission loss (TL) formula taking the base of $15\log_{10} (Fig. S1)$. Every two days, collectors were carefully removed from each cylinder, hung up in the air to avoid juvenile detachment and the cylinders cleaned with Virkon VKS10 disinfectant (LANXESS Deutschland GmbH, Cologne, Germany) before water changing with UV ultrafiltered seawater and monitoring of the larval health, growth and survival rate by optical microscopy.

At the end of the sound exposure experiment, collectors were gently rinsed with sprayers on a 100 µm mesh sieve to retrieved post-larvae from the 24 cylinders (6 replicates per sound treatments). Settled growing post-larvae on the cylinder walls were carefully brushed and pooled with the post-larvae from the collectors to estimate the total wet biomass of recruits. Immediately after collection, larval pools were split into three sub-samples, i.e., one for targeted metabolomics, and two for the untargeted metabolomics experiments presented in this article (if the quantity of post-larvae recovered allowed it), flash-frozen in liquid nitrogen, freeze-dried, and stored at -80°C until extraction for metabolomics.

## 2.3 Growth and metamorphosis success

Larval growth was measured throughout the experiment using a Keyence VHX-2000 Series digital microscope with VH-Z100UR objectives (Osaka, Japan, 1 µm resolution and HDR). The size at metamorphosis was determined by the larval shell (prodissoconch II, PII) morphology and morphometry (Bayne 1965, Martel et al. 1995). Furthermore, the
metamorphosis success was calculated by the ratio of total biomass of post-larvae recovered from the collectors over the number of initial larvae put in each cylinder and the larval growth after metamorphosis by the measure of the larval shell (dissocouch, D) on post-larvae (Martel et al. 2014b).

## 2.4 Untargeted metabolomics (¹H-NMR, LC-HRMS/MS)

The freeze-dried post-larvae pools (≈3.4 mg or ≈500 post-larvae) were crushed with a micro-pestle in microtubes before metabolite extraction using an adaptation of Folch's method (1 methanol : 1 chloroform : 1.4 water) including a 10 min. sonication on ice step. Extracts were centrifugated for 10 min at 10,000 × g 4°C to separate the polar fraction. The extractions were repeated three more times and the polar extracts pooled before solvent evaporation in a SpeedVac (Thermo Fisher Scientific) at room temperature.

### 2.4.1 Proton nuclear magnetic resonance (¹H-NMR)

The larval extracts were reconstituted in 500 µL of NMR buffer (0.2 M pH 7.4 potassium phosphate buffer in 99.9% deuterium oxide with 0.13 mM 3-trimethylsilylpropionic acid) and transferred to standard 5mm NMR tubes. The ¹H-NMR spectra from the post-larvae were obtained with a Bruker Avance III spectrometer, operating at 600 MHz, with a broad-band BBFO probe. The spectra were further processed, bucketed and integrated using NMRProcFlow tools (Jacob et al. 2017) and the identification of the metabolites was performed using Chenomx software and was further confirmed by 2D ¹H-NMR COSY and TOCSY.

### 2.4.2 Liquid chromatography in tandem with high resolution mass spectrometry (LC-HRMS/MS)

After NMR acquisition, the samples were recovered and analysed by LC-HRMS/MS using a Shimadzu Nexera UHPLC coupled to a quadrupole time-of-flight system (TripleTOF® 5600+, Sciex, Concord, ON, Canada), equipped with a Duospray ion source operated in positive and negative electrospray mode. Metabolite separation was conducted on a mixed-mode
reverse-phase column using gradient elution with mobile phases of water and acetonitrile (CAN), both containing 0.1% formic acid. Feature annotation for putative metabolite identifications was performed using MS/MS spectral matching with NIST2017 MS/MS spectral library, Sciex "All in one" accurate mass metabolite library, and an in-house spectral library from standard metabolites run under identical MS/MS conditions. Features with a library score ≥85 and an accurate mass measurement for protonated or deprotonated precursors <10 ppm were kept for further processing. Peak integration was verified for each putatively identified metabolite using Sciex OS-Q.

More details about the ¹H-NMR and LC-HRMS/MS-based untargeted metabolomic experiments are provided in the **supplementary file**.

## 2.5 Quantification of amino acids, energy metabolism-related metabolites and polyunsaturated fatty acids oxidation products

Around 3.5 mg of freeze-dried post-larvae (≈ 500 post-larvae) were micro-ground and homogenized with a 2.8 mm ceramic bead at 4,000 rpm for three cycles of 10 s (Precellys 24; Bertin corporation). Analyte extraction for the quantification of amino acids, energy metabolism-related metabolites and oxylipins were performed on the same sample with an extraction process in two steps.

The first one allows extracting the oxylipins. One millilitre of a 5/1 (v/v) mix of methyl tert-butyl ether (MTBE) and 2,2,2-trifluoroethanol (TFE) was added to the homogenizing tubes containing the sample. The samples were vortexed for 30 s and centrifuged for 1 min at 2,460 g. This extraction was performed three times, and supernatants (750 µL) were pooled in 15 mL falcon tubes. For the second and third extraction cycles, 750 µL of 5/1 (v/v) MTBE/TFE was added to the samples. The supernatant was washed three times with 250 µL of LC-MS grade water. The aqueous layer was collected and pooled with supernatant obtained during the
Shipping Noise Impact on Blue Mussel

244 second step of extraction protocol. The organic phase was dried using a vacuum concentrator
245 (SpeedVac, Savant SPD2010, ThermoScientific) for 2 h without heating. Dried samples were
246 reconstituted with 250 µL of a 1/1/1/1 (v/v/v/v) mix of TFE/ACN/MeOH/50 mM ammonium
247 formate at pH 3 (ACN, Acetonitrile; MeOH, methanol). The samples were vortexed for 30 s
248 and centrifuged for 1 min at 2,460 g. Then 250 µL of the supernatant was transferred to 2 mL
249 HPLC vial containing one millilitre of 50 mM ammonium formate buffer at pH 3 with
250 10 ng.mL⁻¹ of internal standard PGE₂-d₉. The sample was analysed directly after the extraction
251 process.

252 During the second step, amino acids and energy metabolism-related metabolites were
253 extracted. One milliliter of a 1/1 (v/v) mix of TFE/LC-MS grade water was added to the
254 homogenizing tubes, vortexed for 30 s and centrifuged for 1 min at 2,460 g. Three extraction
255 cycle was performed, and supernatants (750 µL, except for the third extraction 1 mL) were
256 pooled in 15 mL falcon tube, containing the aqueous layer obtained during oxylipins extraction
257 and 4 mL of ACN. For the second and third extraction cycle, just 750µL of 1/1 (v/v) TFE/ LC-
258 MS grade water was added to the samples. The sample was dryed with a SpeedVac overnight
259 without heating. The sample reconstitution was performed by adding 50 µL of LC-MS grade
260 water following by addition of 200 µL of LC-MS grade ACN. One hundred microliters of each
261 reconstituted sample were transferred to two vials: one vial was for positive analysis and the
262 other for the negative analysis. Compounds valine-d₈ (0.1 µg.mL⁻¹) and pyruvate-d₃ (1 µg.mL⁻¹)
263 were used as internal standard to quantify the compounds in positive and negative ion modes,
264 respectively.

265 Oxylipin analysis was performed with an HPLC 1260 Infinity II device coupled to a
266 6546 QTOF (Agilent Technologies) in negative ionization mode equipped with an Agilent
267 1290 Infinity Flexible Cube. Oxylipins extracts were separated using an InfinityLab Poroshell
268 HPH C-18, 2.1 x 100 mm 1.9 µm (Agilent) and a Gemini C6-Phenyl guard column as on-line
269 SPE (4 x 2.0 mm, SecurityGuard, Phenomenex) with a column temperature maintained at 40°C.

270 Metabolites were separated and quantified in multiple reaction monitoring (MRM)
271 mode using an HPLC 1260 Infinity II device coupled to a 6420 Triple Quad mass spectrometer
272 (Agilent Technologies) in positive and negative ionization mode. Ten microliters of the sample
273 were injected, and the chromatography separation was performed with a InfinityLab Poroshell
274 120 HILIC-Z, 2.7 µm. 10 x 2.1 mm column.

275 Data were processed with MassHunter Quantitative QQQ (Quant-my-Way) software
276 from Agilent Technologies. Two samples in the oxylipins analysis had bad peak shape and a
277 large change in retention time. These two samples were removed from the analysis.

278 More details about the targeted experiments are provided in the **supplementary file**.

279 **2.6 Statistical analyses**

280 **2.6.1 Multivariate statistics (OPLS-DA)**

281 The metabolomics data were analysed by orthogonal projection latent structures
282 discriminant analyses (OPLS-DA) using SIMCA 17 software (Sartorius). All data were scaled
283 to units of variance. The minimum number of features needed for optimal classification of the
284 Control and High samples in the OPLS-DA models was determined by iteratively excluding the
285 variables with low regression coefficients and wide confidence intervals derived from “jack-
286 knifing” combined with low variable importance in the projection (VIP) until maximum
287 improvement of the quality of the models. The model quality was evaluated after 7-fold cross
288 validation by cumulative R²Y (goodness of fit), cumulative Q² (goodness of prediction), CV-
289 ANOVA (test of cross-validated predictive residuals), and by fitting principal component
290 analyses (PCA) which are an indicator of OPLS-DA model reliability(Eriksson et al. 2008).
291 The contribution of each predictor in the model was evaluated through the variable score
292 contribution, i.e., the differences, in scaled units, for all the terms in the model, between the
293 outlying and the normal observation, multiplied by the absolute value of the normalized weight.
294 Metabolites included in the model with a VIP > 1 were considered as important. The effect of
295 the intermediate noise levels (low and medium) on the larval metabolome was predicted from
296 the OPLS-DA models adjusted for the control and high conditions.

297 To identify the most relevant metabolites across the untargeted data sets (¹H-NMR, LC-
298 HRMS/MS), an alternative model was fitted by multiblock OPLS-DA applied and structured in
299 two blocks. In practice, a different block was assigned to the variables of each data set, and each
300 block was scaled to avoid the domination of one block over the others. Thereafter, OPLS-DA
301 was performed on the two blocks iteratively, excluding the least relevant variables from the
302 model as described above.

303 The sets of features included in the OPLS-DA were further tested for their ability to
304 explain the variations related to noise when modelled using principal component analysis, an
305 unsupervised multivariate method (Fig. S2) (Worley & Powers 2016).

2.6.2 Metabolite enrichment analysis

307 To identify the most significantly affected metabolic pathways, the discriminant
308 metabolites (untargeted multiblock OPLS-DA and targeted OPLS-DA amino acid and energy
309 metabolism) were analysed by metabolite-set enrichment analysis (MSEA) (Xia & Wishart
310 2010), implemented in Metaboanalyst, a freely available web-based metabolomics analysis
311 suite (MetaboAnalyst 5.0, http://www.metaboanalyst.ca) (Pang et al. 2021). The metabolite
312 enrichment analysis of each set of metabolites was performed using over-representation
313 analysis with the “pathway-associated metabolite sets” provided, containing 99 metabolite sets
314 based on normal human metabolic pathways. The ORA was implemented using the
315 hypergeometric test to evaluate whether a particular metabolite set was more highly
316 represented than expected by chance within the given compound list. A cutoff was chosen at a
317

P-value controlled for False Discovery Rate (FDR) of 0.01 to retain the most significantly
enriched metabolic pathways.

318

319

2.6.3 **Permanova**

320

The growth and metamorphosis success data were analysed on PRIMER7 software, using
analyses of variance with 9999 permutations of residuals under a reduced model
(PERMANOVA). Permutational method is a randomization which allows us to get rid of the
distributional assumptions as normality and can be applied to very small samples. Similarities
matrices based on ratios were produced using the Bray-Curtis distance. A one-way Permanova
tested the effect of sound levels (control, low, medium, and high) on growth (size at
metamorphosis and post-metamorphosis growth) and metamorphosis success. When
differences were significant (p-perm ≤0.05), multiple comparison pairwise tests were used to
determine which groups significantly differed. Distance-based tests for homogeneity of
multivariate dispersions (PERMDISP) were carried out to evaluate the homogeneity of ratios in
each condition.

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3. Results

332

Spectrograms (Fig. 3a) highlighted sustained low-frequency energy during arrival, while energy diminished more rapidly during departure. Power spectral density (PSD) analysis of noise from the *Nohlan Ava*, a 120 m roll-on/roll-off cargo ship, revealed distinct acoustic profiles during its arrival (11 min) and departure (8.5 min) phases (Fig. 3b). The ship's noise exhibited slightly higher energy across most frequencies during arrival. The departure phase showed reduced and more uniform sound intensity. PSD comparisons across exposure conditions (high, medium, low, and control) demonstrated significantly elevated sound intensities in high-exposure conditions (Fig. 3b), with medium and low exposures showing intermediate levels, and the control the lowest.

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**3.1 Post-larval prostaglandins and fatty acid oxidation products**
The stress induced by the noise treatment could promote the oxidation of polyunsaturated fatty acids (PUFAs) from phospholipids notably involved in inflammatory processes. Accordingly, the cyclopentenone prostaglandins (cyPGs) derived from arachidonic acid, as well as oxylipins were quantified in the blue mussels' post-larvae (Table S2). An orthogonal partial least squares discriminant analysis model (OPLS-DA) (Fig. 4, Table S3) was fitted to identify the compounds from the oxidative metabolism that were differentially regulated between ambient (control, n=5) and high noise (~151 dB re 1 µPa, n=4) exposure. This model (R²Y = 0.97, Q² = 0.95, CV-ANOVA = 0.0016), composed of one predictive and one orthogonal component, was able to explain the variability induced by the sound treatment (Fig. 4a) and contained 14 compounds (Fig. 4b). The larval pools exposed to low noise (~121 dB re 1µPa, n=6) were also correctly discriminated from those from the high sound treatment. The intermediate level (~127 dB re 1µPa, n=5) induced a similar response to the high sound treatment.

As shown in Fig. 4, the OPLS-DA reveal that five classes of lipid mediators were affected by noise, namely (1) the cyPGs; (2) the omega-3 eicosapentaenoic acid (EPA)-derived oxylipin; (3) the α-linolenic acid (ALA)-derived oxylipin; (4) the linoleic acid (LA)-derived oxylipin; and (5) hydroxy docosahexaenoic acids (HDHAs). CyPGs are derived from C20-unsaturated FAs with a cyclopentane ring, formed from arachidonic acid (C20:4 ω-6) or eicosatetraenoic acid (C20:4 ω-3) through cyclooxygenation. They are rapidly metabolized by enzymatic reactions to inactive forms. The OPLS-DA show the presence of two active cyPGs, i.e., PGA2 more concentrated in mussels exposed to noise – possibly indicating inflammation or oxidative stress (Lee et al. 2010) – and PGF3α which were more prominent in control samples. Inactivated prostaglandins 15-keto-PGF2α and 13,14-dihydro-15-keto-PGF2α were also detected in mussels' larvae exposed to medium-to-high noise. The inactivation product of PGE2, i.e. 15-keto-PGE2 and 15-deoxy-Δ12,14-prostaglandin J2, were also more concentrated
367 in the controls, indicating the termination of proinflammatory reactions (Kim et al. 2021).
368 Oxylipins – resulting from the oxidation of eicosapentaenoic acid (EPA, C20:5 ω-3), i.e., α-369 linolenic acid (C18:3 ω-3) and linoleic acid (C18:2 ω-6) – were generally higher in controls.
370 Finally, four HDHAs, which derive from docosahexaenoic acid (DHA, C22:6 ω-3), were
371 retained in the OPLS-DA.

372 **3.2 Post-larval polar metabolites**

373 The response to stress could also impact general metabolism as amino acid synthesis or
374 catabolism in relation with energy metabolism, the synthesis/degradation of DNA, RNA and
375 involved multiple classes of molecules. Therefore, untargeted metabolomics, a technique
376 facilitating the simultaneous exploration of multiple molecules in a biological sample
377 employing proton nuclear magnetic resonance (¹H-NMR) and LC-HRMS/MS, was
378 implemented. This approach enabled the relative quantification and identification of 133
379 metabolites (67 and 84 by ¹H-NMR and LC-HRMS/MS, respectively) in post-larvae exposed to
380 noise. Notably, only 18 compounds were found to be common to both instrumental methods,
381 which had similar individual classification performances (Fig. S3, S4). A multiblock OPLS-DA
382 with an explicative ability (R²Y) of 1.00 and a predictive ability (Q²) of 0.98 for a CV-ANOVA
383 of 0.0010 was fitted to reveal the alterations induced to the larval metabolome by exposure to
384 the ship noise at high level (n=5) (Fig. 5a, b, Table S4). This model, composed of one
385 predictive and one orthogonal component, included 26 metabolites (11 detected by ¹H-NMR,
386 13 by LC-HRMS/MS, and 2 by both methods). It was able to discriminate the larvae under
387 control condition and those exposed to all the noise levels, thus confirming that the metabolites
388 included in the model are effectively correlated to the noise level. The discriminant metabolites
389 included 15 amino acids, 4 nucleosides and derivatives, 2 cholines, and 5 other molecules,
390 including one prostaglandin.
This untargeted exploration of the impact of noise on post-larvae's metabolome was completed by a systematic characterization of the amino acid and energy metabolism, based on the absolute quantification of a set of 48 metabolites (Table S5). These metabolites were subjected to a multivariate screening, as performed with the untargeted dataset, to identify the molecules with concentrations affected by an exposition to the high shipping noise level. Twenty metabolites were conserved in the OPLS-DA model composed of one predictive and three orthogonal components ($R^2_{Y_{cum}} = 1.00$, $Q^2_{cum} = 0.99$, CV-ANOVA = 0.0245) (Fig. 5c, Table S6). Five of these metabolites (all of which were amino acids) were also conserved in the untargeted model (glutamate, glutamine, glycine, leucine and methionine; Fig. 5a).

The impact on biochemical pathways of the mussel post-larvae exposure to noise during metamorphosis was determined by Metabolite Set Enrichment Analysis (Fig. 5d) based on the combination of the discriminant metabolites identified in the untargeted and targeted approaches. This analysis highlighted 17 pathways significantly enriched in metabolites (P-value FDR <0.01). Among them, pathways related to amino acids, including the urea cycle, glutamate and alanine metabolism, were the most extensively impacted. They were followed by pathways connected to energy metabolism, such as the transfer of acetyl groups into mitochondria, the Warburg effect (anaerobic glycolysis), pyruvate metabolism, gluconeogenesis, as well as β-oxidation of very long chain FAs. Finally, the oxidative stress response via glutathione metabolism was also affected.

### 3.3 Post-larval performance

Post-larval performances were evaluated using a Keyence VHX-2000 Series digital microscope. Those observations showed that the success of metamorphosis increased significantly with the sound treatment (+21% High vs. Control; P-value = 0.01) in mussels sampled on collectors (Fig. 5e). Furthermore, the size at metamorphosis (PII measurements)
415 (Fig. 5f) was significantly impacted by the noise (+18 µm Low vs. Control; P-value = 0.02, -17 µm Low vs. High; P-value = 0.04) while the post-metamorphosis growth (D) did not differ significantly between the sound treatments (Fig. 5f).

418 **4. Discussion**

419 **4.1 Noise treatment**

420 The single ship's noise profile from the loudest vessel in the archipelago, consistent with
421 measurements of commercial ships (McKenna et al. 2012), was used aiming at standardizing
422 the anthropogenic sound exposure, ensuring that the primary variable in the experiment was the
423 intensity of the noise. While a broader acoustic landscape might more accurately reflect real-
424 world conditions, such an approach would require a long-duration sound file, which could be
425 challenging given the brief lifespans of the larval stages studied. Furthermore, previous
426 calibration work on the Larvosonic system revealed that both kinetic (particle velocity) and
427 potential (pressure) energy decrease proportionally as source levels increase (Olivier et al.
428 2023). Given this uniform decrease across treatments, the design ensured that trends in
429 exposure levels (low, medium, high) were consistent between pressure and particle motion
430 which is valuable since mussels are likely to respond to particle motion rather than sound
431 pressure (Popper & Hawkins 2018).

432 **4.2 Shipping noise treatment impact on post-larval metabolism**

433 Noise modified the life cycle of mussel larvae by stimulating their settlement and
434 modulating their metabolome. Those alterations may result from a direct effect of the sound or
435 different level of metamorphosis development, including tissues morphology, induced by the
436 sound treatment.

437 **4.2.1 Modification of polyunsaturated fatty acid oxidation patterns indicated an**
438 **inflammatory response.**
The responses to noise-induced stress triggered a modification of the lipid mediators' production (oxylipins and prostaglandins) by oxidation of PUFAs through direct interaction with reactive oxygen species (ROS), produced during oxidative stress, or ROS-activated enzymatic pathways, i.e., cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450. Therefore, noise could induce, directly or indirectly through the regulation of the metamorphosis process, a modification of lipid mediators' patterns known to play essential roles in inflammation, reproduction, regulation of ion flux, thermoregulation, and activation of the immune response in aquatic invertebrate (Canesi et al. 2002, Gagné et al. 2007, Di Costanzo et al. 2019). Several oxylipins and prostaglandins were retained in the OPLS-DA which could suggest a modification of the pro-inflammatory / pro-resolving balance, although their role in stress and inflammation in aquatic organisms is not well understood (Gabbs et al. 2015).

Besides these physiological processes, oxylipins and CyPGs levels may also affect the larval development, especially during the metamorphosis. Indeed, metamorphosis is associated with the development of juvenile-specific structures including the loss of the velum and the development of the gills (Yang et al. 2013). During this transition, the innate immune system is solicited for the resorption and reorganization of larval tissues through the ROS regulated process (Redza-Dutordoir & Averill-Bates 2016) of apoptosis and ROS-generating phagocytosis which may impact the lipid oxidation patterns of the post-larvae (Winston et al. 1996, Davidson & Swalla 2002, Wang et al. 2023). Interestingly, CyPGs and oxylipins may also be involved in marine invertebrates settlement (Knight et al. 1999), suggesting that elevated ship noise levels could trigger this action on larvae, with potential impact on the ecosystems as well as aquaculture.

Overall, noise-induced stress appeared to modulate the production of lipid mediators from PUFAs possibly leading to different inflammatory profiles between control and noise-
463 exposed post-larvae. However, it was not clear whether its impact was direct or linked to
464 different ontogenic development at metamorphosis.

465 **4.1.2 Increased energy requirement and protein turnover are coupled with disrupted nervous**
466 **system activity.**

467 **Gluconeogenesis and energy production is increased in post-larvae exposed to**
468 **noise.** Amino acids and their derivatives, comprising 22 out of 42 compounds affected by noise
469 in both untargeted and targeted metabolomes, showed significant changes, akin to the response
470 observed in adult mussels when exposed to pollutants, leading to stress and reduced feeding
471 rates (Wale et al. 2019) due to a reduction in valve gape opening (Day et al. 2017, Ledoux et al.
472 2023). In response to food restriction, mussels increased gluconeogenesis to produce glucose
473 from oxidizable amino acids, which helps maintain intracellular osmolarity (Moyes et al.
474 1990). Consequently, lower levels of alanine, arginine, glutamate, glutamine, methionine,
475 phenylalanine, and proline in noise-exposed post-larvae suggest higher oxidation rates to
476 compensate for reduced feeding (Spiga et al. 2016) and greater energy demands caused by
477 noise-related stress and the extension of the prospecting pelagic phase (Vazzana et al. 2016,
478 Gigot et al. 2023).

479 Additionally, lipid β-oxidation may be heightened in noise-exposed post-larvae,
480 indicated by lower levels of certain acylcarnitines, such as acetylcarnitine and
481 palmitoylcarnitine, suggesting increased transport of FAs into mitochondria for breakdown,
482 generating glucose and energy (Longo et al. 2016). This elevated gluconeogenesis rate led to
483 higher glucose levels and greater depletion of glycolysis products (pyruvate,
484 phosphoenolpyruvate), accompanied by increased levels of ATP, ADP, and Krebs cycle
485 intermediates (acetyl-CoA, oxaloacetate, α-ketoglutarate), reflecting intensified aerobic energy
486 production and amino acids turnover. The aerobic energy production is complemented by the
487 transformation via the anaerobic glycolysis of glucose into lactate, which is higher in post-
larvae exposed to noise. However, the level of octopine (a derivative of arginine and alanine) and strombine [N-(carboxymethyl)-D-alanine] involved in maintaining the redox balance under anaerobic conditions and anaerobic respiration end-products, respectively, were higher in the controls suggesting higher energy demands. These findings indicate lower metamorphosis development in the control group, as evidenced by morphometric data, correlating with metabolic activity, specifically the NADH/NAD+ ratio. The elevated ratio observed in the high sound condition may suggest an increased reliance on anaerobic metabolism, potentially related to the demands of gill development and/or the stress induced by sound exposure.

**Protein and cellular turnover increased in post-larvae exposed to noise.** In post-larvae exposed to noise, five proteogenic amino acids (asparagine, aspartate, threonine, tyrosine, valine) were found at higher levels. Threonine, tyrosine as well as aspartate and asparagine may be precursors for the biosynthesis of new proteins in response to the stress-induced formation of ROS provoking protein damage or the increased protein demand for the remodelling occurring during the metamorphosis. The branched-chain amino acid valine is essential for the immune system by providing energy and being a precursor for the biosynthesis of protective molecules (Calder 2006), but it also plays an important role in the regulation of protein turnover processes as observed in *Mytilus galloprovincialis* challenged by *Vibrio anguillarum* (Ji et al. 2013). The post-larvae immune system was likely to be triggered by the response to stress, involving prostaglandins and oxylipins (Canesi et al. 2002). The exposure of mussel larvae to noise, or the regulation of their metamorphosis by noise, may thus have an impact on their protein turnover, in combination with a higher energy level due to increased gluconeogenesis, which could lead to a tendency to grow faster after metamorphosis (Fig. 5f).

**Action on nervous system activity.** The level of the neurotransmitter acetylcholine – which plays an important role in the cholinergic system involved in the digestion, control of
heartbeat and movement – is dependent on its rate of inactivation via hydrolysis into choline by acetylcholinesterase (AchE). The activity of this enzyme is considered the most effective biomarker in assessing neurological changes caused by xenobiotics (Fontes et al. 2022). The higher level of acetylcholine (decreased hydrolysis by AchE) in post-larvae exposed to noise, which is consistent with the decrease of AchE activity after exposition to pollutants such as cocaine (Fontes et al. 2022) and the typical decreased enzyme activity in mussels exposed to environmental stress (Ricciardi et al. 2006), could have a stimulatory effect. The formation of ROS might be the cause of the decreased hydrolysis by AchE similarly to what has been observed during the exposure of mussels to waste water (Gagné et al. 2011). Acetylcholine is also an inhibitor of some ctenidial lateral cilia that reduce water pumping rate in *Mytilus edulis* (Jones & Richards 1993), in good agreement with the reduced respiration and feeding rates observed during noise exposition (Wale et al. 2019). Additionally, this neurotransmitter induces both settlement behaviour and metamorphosis development in bivalves (Beiras & Widdows 1995, Bayne 2017). Its higher level in post-larvae exposed to noise could therefore explain the increase settlement level and faster metamorphosis development.

## 4.3 Noise induces settlement in bad habitats and in an altered physiological condition.

Competent veliger mussel larvae can delay their metamorphosis and remain adrift up to a size of 400 µm – almost 100% increase from the 230 µm observed for the most precocious ones – due to potential absence of a suitable environment required for stimulating settlement, thus forcing larvae to continue planktonic growth (Martel et al. 2014b). At an average size of 300 µm, the larvae were already in a delayed metamorphosis condition, undoubtedly linked to the absence of turbulence in the Larvosonic systems to limit noise perturbation (Olivier et al. 2023). Indeed, settlement success is positively correlated with centrifugal and advective flow velocity and turbulence (Pernet et al. 2003). As highlighted by the metabolomic characterization, the post-larvae exposed to high sound level have higher energy expenditures
than the others, which potentially results in a decrease in energy reserves and therefore activates the oxidative energy pathways. Those larvae would thus consume considerably more energy daily than the others (Bayne 1965, Gigot et al. 2023), which will result in them becoming "desperate larvae" faster (Knight-Jones 1951, Toonen & Pawlik 1994), i.e., forced to settle without any positive settlement cue, or even on unfavourable environment, at a smaller size. Alternatively, a second hypothesis could be that there existed a disparity in the metamorphosis stage of the larvae between the control and high sound exposure groups that the latter group may exhibit a more advanced metamorphic state compared to the former (Cannuel et al. 2009).

In summary, marine traffic noise delays metamorphosis – leading to post-larval size differences – while increasing its success in a suboptimal habitat. The post-larvae that settled and underwent metamorphosis in this suboptimal habitat show increased metabolomic demands and an altered prostaglandin profile, indicating stress and inflammatory responses, which may reduce their chances of survival. However, these results need to be enriched by long-term studies.

**Perspectives for sessile bivalve aquaculture, ecosystems, and regulations.**

Anthropogenic noise, associated to maritime traffic, modulates larval metamorphosis dynamics and recruitment processes, potentially altering larval selectivity and decreasing survival rates to adulthood, with potential cascading effect on population dynamics and marine biodiversity (Pechenik 2006, Gigot et al. 2023). Those perturbations may, additionally, carry socio-economic implications, particularly for aquaculture systems reliant on spat capture (Kamermans & Capelle 2019). Our innovative approach coupling metabolomic monitoring of *Mytilus edulis* larvae stress responses and acoustic studies provides the first metabolic dose-response data about the impact of shipping noise on sessile bivalve development. These results could be used by regulators and decision-makers. Indeed, no binding regulations currently require the reduction of manmade underwater noise. There are, nevertheless, some regulations
563 and recommendations to help maritime professionals reduce noise pollution, but with limited
564 legal status, i.e., the International Maritime Organization Guidelines (Maruf & Chang 2023).
565 Those initiatives are based on thresholds – defined by the noise level at which individual
566 animals start to have adverse effects – as it is the case for the ‘level of Onset of Biological
567 Adverse Effect (LOBE)’ proposed by the European Union (Technical Group on Underwater
568 Noise 2022). The definition of thresholds for a variety of organisms is crucial for the
569 implementation of precautionary management and mitigation measures – such as the reduction
570 vessel speed (Lajaunie et al. 2023), the modulation of ship traffic and speed in reproduction
571 zone during spawning season, or the promotion of ship design that reduce propeller, machinery,
572 and flow noises (Arveson & Vendittis 2000) – to limit the impacts on the biodiversity, and the
573 management of fisheries and aquaculture.

574 **5. Conclusion**

575 This study highlights that marine traffic noise disrupts the metamorphosis and
576 settlement of *Mytilus edulis* larvae, resulting in size differences and increased stress-related
577 metabolomic expenditures in suboptimal habitats. Those findings emphasize the significant
578 impact of anthropogenic noise on larval development, with potential negative effects on bivalve
579 aquaculture, ecosystem health, and marine biodiversity.

580 Acknowledgements

581 The authors acknowledge access, for method development purposes, to the biomolecular NMR
582 platform of the Institut de biologie physico-chimique (IBPC) supported by the CNRS, the
583 Labex DYNAMO (ANR-11-LABX-0011), the Equipex CACSICE (ANR-11-EQPX-0008)
584 and the Conseil Régional d'Île-de-France (SESAME grant). Delphine Mathias is acknowledged
585 for her contribution to the recording of the shipping noise soundtrack. Leanne Ohlund
586 (UQAM), Nathalie Gauthier (UQAR-ISMER), and Mathieu Millour (Iso-BioKem Inc) are
587 acknowledged for their technical support. D.V., S.B., B.G., F.O., D.E.W., I.M., R.T. are
588 members of Ressources Aquatiques Québec Research Network.

589 **Data availability**

590 The untargeted metabolomic datasets generated and analysed during the current study are
591 available in the MetaboLights repository hosted by the EMBL-EBI(Haug et al. 2020),
592 http://www.ebi.ac.uk/metabolights/MTBLS5678. All the quantitative data extracted from the
593 targeted metabolomics are provided as supplementary data. The soundtrack used during the
594 experiment was deposited under the DOI 10.5281/zenodo.10910123 in the Zenodo repository
595 (https://zenodo.org) hosted by the European Organization for Nuclear Research (CERN).

596 **Authorship contribution**

597 S.B. and D.V. drafted the manuscript and performed the chemometric and functional analyses.
598 D.V. reared the mussel larvae, performed post-larval performance measurement, and
599 contributed to the targeted mass spectrometry analysis. S.B. designed and carried out the NMR-
600 based metabolomics experiments. N.G. and B.G. performed the untargeted and targeted mass
601 spectrometry experiments, respectively. L.S. supervised the untargeted mass spectrometry
602 experiments. A.A.A. provided technical support with NMR spectroscopy. A.C. contributed to
603 the discussion on laws and regulations. S.B., F.O., D.E.W., I.M., and R.T. designed and
604 supervised the study. All authors contributed to write the manuscript and approved the final
605 article.

606 **Ethics declarations**

607 *Competing interests*

608 The authors declare no competing interests.

609
610 **Funding**

611 This study was funded by the Fonds de la Recherche du Québec Nature et Technologies

612 (FRQNT 280057) and the Agence Nationale de la Recherche (ANR-19-FQSM-0005) through

613 the French-Québec maritime funding program. Supplementary funds were also obtained from

614 Ressources Aquatiques Québec Research Network (FRQNT, #2014-RS-171172). This study is

615 a contribution to the BeBEST2 International Research Project (CNRS INEE/LEMAR and

616 UQAR/ISMER).

617 **References**

618 Andrew RK, Howe BM, Mercer JA, Dzieciuch MA (2002) Ocean ambient sound:

619 Comparing the 1960s with the 1990s for a receiver off the California coast.

620 Acoust Res Lett Online 3:65-70.

621 Arveson PT, Vendittis DJ (2000) Radiated noise characteristics of a modern cargo ship. J

622 Acoust Soc Am 107:118-129.

623 Bayne BL (2017) Biology of Oysters. Academic Press.

624 Bayne BL (1965) Growth and the delay of metamorphosis of the larvae of *Mytilus edulis*

625 (L.). Ophelia 2:1-47.

626 Beiras R, Widdows J (1995) Induction of metamorphosis in larvae of the oyster

627 Crassostrea gigas using neuroactive compounds. Mar Biol 123:327-334.

628 Borthagaray AI, Carranza A (2007) Mussels as ecosystem engineers: Their contribution

629 to species richness in a rocky littoral community. Acta Oecologica 31:243-250.

630 Calder PC (2006) Branched-chain amino acids and immunity. J Nutr 136:288S-93S.

631 Canesi L, Scarpato A, Betti M, Ciacci C, Pruzzo C, Gallo G (2002) Bacterial killing by

632 mytilus hemocyte monolayers as a model for investigating the signaling

633 pathways involved in mussel immune defence. Mar Environ Res 54:547-551.

634 Cannuel R, Beninger PG, McCombie H, Boudry P (2009) Gill Development and Its

635 Functional and Evolutionary Implications in the Blue Mussel *Mytilus edulis*

636 (Bivalvia: Mytilidae). Biol Bull 217:173-188.

637 Cervello G, Olivier F, Chauvaud L, Winkler G, Mathias D, Juanes F, Tremblay R (2023)

638 Impact of anthropogenic sounds (pile driving, drilling and vessels) on the

639 development of model species involved in marine biofouling. Front Mar Sci 10.

640 Chauvaud S, Chauvaud L, Jolivet A (2018) Impacts des sons anthropiques sur la faune

641 marine. Éditions Quae, Paris.

642 Conference of the Parties of the Convention on Migratory Species (2017)

643 UNEP/CMS/Resolution 12.14, Adverse Impacts of Anthropogenic noise on

644 cetaceans and other Migratory Species.

645 Davidson B, Swalla BJ (2002) A molecular analysis of ascidian metamorphosis reveals

646 activation of an innate immune response. Development 129:4739-4751.
647 Day RD, McCauley RD, Fitzgibbon QP, Hartmann K, Semmens JM (2017) Exposure to seismic air gun signals causes physiological harm and alters behavior in the scallop Pecten fumatus. Proc Natl Acad Sci 114:E8537-E8546.

650 Di Costanzo F, Di Dato V, Ianora A, Romano G (2019) Prostaglandins in Marine Organisms: A Review. Mar Drugs 17:428.

652 Duarte CM, Chapuis L, Collin SP, Costa DP, Devassy RP, Eguiluz VM, Erbe C, Gordon TAC, Halpern BS, Harding HR, Havlik MN, Meekan M, Merchant ND, Miksis-Olds JL, Parsons M, Predragovic M, Radford AN, Radford CA, Simpson SD, Slabbekoorn H, Staaterman E, Van Opzeeland IC, Winderen J, Zhang X, Juanes F (2021) The soundscape of the Anthropocene ocean. Science 371:eaba4658.

657 Eriksson L, Trygg J, Wold S (2008) CV-ANOVA for significance testing of PLS and OPLS models. J Chemom 22:594-600.

659 European Union (2008) Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy.

662 FAO (2019) Mussels Aquaculture production by species and country or area. https://www.fao.org/fishery/static/Yearbook/YB2019_USBcard/root/aquaculture/b54.pdf (accessed May 17, 2024)

665 Fontes MK, Dourado PLR, Campos BG de, Maranho LA, Almeida EA de, Abessa DM de S, Pereira CDS (2022) Environmentally realistic concentrations of cocaine in seawater disturbed neuroendocrine parameters and energy status in the marine mussel Perna perna. Comp Biochem Physiol Part C Toxicol Pharmacol 251:109198.

670 Frisk GV (2012) Noiseconomics: The relationship between ambient noise levels in the sea and global economic trends. Sci Rep 2:437.

672 Gabbs M, Leng S, Devassy JG, Monirujjaman M, Aukema HM (2015) Advances in Our Understanding of Oxylipins Derived from Dietary PUFAs12. Adv Nutr 6:513-540.

674 Gagné F, André C, Cejka P, Hausler R, Fournier M (2011) Evidence of neuroendocrine disruption in freshwater mussels exposed to municipal wastewaters. Sci Total Environ 409:3711-3718.

677 Gagné F, Gagnon C, Turcotte P, Blaise C (2007) Changes in Metallothionein Levels in Freshwater Mussels Exposed to Urban Wastewaters: Effects from Exposure to Heavy Metals? Biomark Insights 2:117727190700200012.

680 Gigot M, Olivier F, Cervello G, Tremblay R, Mathias D, Meziane T, Chauvaud L, Bonnel J (2023) Pile driving and drilling underwater sounds impact the metamorphosis dynamics of Pecten maximus (L., 1758) larvae. Mar Pollut Bull 191:114969.

683 Hadfield MG (2000) Why and how marine-invertebrate larvae metamorphose so fast. Semin Cell Dev Biol 11:437-443.

685 Hadfield MG, Paul VJ (2001) Natural Chemical Cues for Settlement and Metamorphosis of Marine-Invertebrate Larvae. In: Marine Chemical Ecology. CRC Press

687 Haug K, Cochrane K, Nainala VC, Williams M, Chang J, Jayaseelan KV, O'Donovan C (2020) MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res 48:D440-D444.

690 Hawkins AD, Popper AN (2017) A sound approach to assessing the impact of underwater noise on marine fishes and invertebrates. ICES J Mar Sci 74:635-651.
692 IMO (2023) International Maritime Organization, Revised Guidelines for the Reduction of Underwater Radiated Noise from Shipping to Address Adverse Impacts on Marine Life, MEPC.1/Circ.906, 22 August 2023.

695 Jacob D, Deborde C, Lefebvre M, Maucourt M, Moing A (2017) NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics. *Metabolomics* 13:36.

698 Jalkanen J-P, Johansson L, Andersson MH, Majamäki E, Sigray P (2022) Underwater noise emissions from ships during 2014–2020. *Environ Pollut* 311:119766.

700 Ji C, Wu H, Wei L, Zhao J, Wang Q, Lu H (2013) Responses of Mytilus galloprovincialis to bacterial challenges by metabolomics and proteomics. *Fish Shellfish Immunol* 35:489–498.

703 Jolivet A, Tremblay R, Olivier F, Gervaise C, Sonier R, Genard B, Chauvaud L (2016) Validation of trophic and anthropic underwater noise as settlement trigger in blue mussels. *Sci Rep* 6:33829.

706 Jones HD, Richards OG (1993) The effects of acetylcholine, dopamine and 5-hydroxytryptamine on water pumping rate and pressure in the mussel Mytilus edulis L. *J Exp Mar Biol Ecol* 170:227–240.

709 Joyce A, Vogeler S (2018) Molluscan bivalve settlement and metamorphosis: Neuroendocrine inducers and morphogenetic responses. *Aquaculture* 487:64–82.

712 Kamermans P, Capelle JJ (2019) Provisioning of Mussel Seed and Its Efficient Use in Culture. In: *Goods and Services of Marine Bivalves*. Smaal AC, Ferreira JG, Grant J, Petersen JK, Strand Ø (eds) Springer International Publishing, Cham, p 27–49

715 Kim W, Jang J-H, Zhong X, Seo H, Surh Y-J (2021) 15-Deoxy-Δ12,14-Prostaglandin J2 Promotes Resolution of Experimentally Induced Colitis. *Front Immunol* 12.

717 Knight J, Rowley AF, Yamazaki M, Clare AS (1999) Eicosanoids are modulators of larval settlement in the barnacle, Balanus amphitrite. *J Mar Biol Assoc U K* 80:113–117.

719 Knight-Jones EW (1951) Gregariousness and Some other aspects of the setting behaviour of Sipirorbis. *J Mar Biol Assoc U K* 30:201–222.

721 Lajaunie M, Ollivier B, Ceyrac L, Dellong D, Le Courtois F (2023) Large-Scale Simulation of a Shipping Speed Limitation Measure in the Western Mediterranean Sea: Effects on Underwater Noise. *J Mar Sci Eng* 11:251.

724 Ledoux T, Clements JC, Comeau LA, Cervello G, Tremblay R, Olivier F, Chauvaud L, Bernier RY, Lamarre SG (2023) Effects of anthropogenic sounds on the behavior and physiology of the Eastern oyster (Crassostrea virginica). *Front Mar Sci* 10.

727 Lee BR, Paing MH, Sharma-Walia N (2021) Cyclopentenone Prostaglandins: Biologically Active Lipid Mediators Targeting Inflammation. *Front Physiol* 12.

729 Lee S-Y, Ahn J-H, Ko KW, Kim J, Jeong SW, Kim I-K, Kim J, Kim H-S (2010) Prostaglandin A2 activates intrinsic apoptotic pathway by direct interaction with mitochondria in HL-60 cells. *Prostaglandins Other Lipid Mediat* 91:30–37.

732 Lillis A, Eggleston DB, Bohnenstiehl DR (2013) Oyster Larvae Settle in Response to Habitat-Associated Underwater Sounds. *PLOS ONE* 8:e79337.

734 Longo N, Frigeni M, Pasquali M (2016) Carnitine transport and fatty acid oxidation. *Biochim Biophys Acta* 1863:2422–2435.
736 Martel AL, Hynes TM, Buckland-Nicks J (1995) Prodissoconch morphology, planktonic
737 shell growth, and size at metamorphosis in Dreissena polymorpha. Can J Zool
738 73:1835-1844.

739 Martel AL, Tremblay R, Toupoint N, Olivier F, Myrand B (2014a) Veliger Size at
740 Metamorphosis and Temporal Variability in Prodissoconch II Morphometry in
741 the Blue Mussel (Mytilus edulis): Potential Impact on Recruitment.
742 https://doi.org/102983/0350330213 33:443-455.

743 Martel AL, Tremblay R, Toupoint N, Olivier F, Myrand B (2014b) Veliger Size at
744 Metamorphosis and Temporal Variability in Prodissoconch II Morphometry in
745 the Blue Mussel (Mytilus edulis): Potential Impact on Recruitment. J Shellfish Res
746 33:443-455.

747 Maruf, Chang Y-C (2023) Further Development of the Law of the Sea Convention in the
748 Anthropocene Era: The Case of Anthropogenic Underwater Noise. Sustainability
749 15:9461.

750 McDonald MA, Hildebrand JA, Wiggins SM (2006) Increases in deep ocean ambient
751 noise in the Northeast Pacific west of San Nicolas Island, California. J Acoust Soc
752 Am 120:711-718.

753 McKenna MF, Ross D, Wiggins SM, Hildebrand JA (2012) Underwater radiated noise
754 from modern commercial ships. J Acoust Soc Am 131:92-103.

755 Moyes CD, Suarez RK, Hochachka PW, Ballantyne JS (1990) A comparison of fuel
756 preferences of mitochondria from vertebrates and invertebrates. Can J Zool
757 68:1337-1349.

758 Olivier F, Gigot M, Mathias D, Jezequel Y, Meziane T, L'Her C, Chauvaud L, Bonnel J
759 (2023) Assessing the impacts of anthropogenic sounds on early stages of
760 benthic invertebrates: The "Larvosonic system." Limnol Oceanogr Methods
761 21:53-68.

762 Pang Z, Chong J, Zhou G, De Lima Morais DA, Chang L, Barrette M, Gauthier C, Jacques
763 PÉ, Li S, Xia J (2021) MetaboAnalyst 5.0: narrowing the gap between raw spectra
764 and functional insights. Nucleic Acids Res 49:W388-W396.

765 Pechenik JA (1990) Delayed metamorphosis by larvae of benthic marine invertebrates:
766 Does it occur? Is there a price to pay? Ophelia 32:63-94.

767 Pechenik JA (2006) Larval experience and latent effects—metamorphosis is not a new
768 beginning. Integr Comp Biol 46:323-333.

769 Pelo R, Wootton D (2004) Maritime traffic distribution in Atlantic Canada to support an
770 evaluation of a Sensitive Sea Area proposal.

771 Peng C, Zhao X, Liu S, Shi W, Han Y, Guo C, Jiang J, Wan H, Shen T, Liu G (2016) Effects of
772 anthropogenic sound on digging behavior, metabolism, Ca2+/Mg2+ ATPase
773 activity, and metabolism-related gene expression of the bivalve Sinonovacula
774 constricta. Sci Rep 6:24266.

775 Pernet F, Tremblay R, Bourget E (2003) Settlement success, spatial pattern and
776 behavior of mussel larvae Mytilus spp. in experimental "downwelling" systems
777 of varying velocity and turbulence. Mar Ecol Prog Ser 260:125-140.

778 Popper AN, Hawkins AD (2018) The importance of particle motion to fishes and
779 invertebrates. J Acoust Soc Am 143:470-488.
780 Rayssac N, Pernet F, Lacasse O, Tremblay R (2010) Temperature effect on survival, growth, and triacylglycerol content during the early ontogeny of Mytilus edulis and M. trossulus. Mar Ecol Prog Ser 417:183-191.

783 Redza-Dutordoir M, Averill-Bates DA (2016) Activation of apoptosis signalling pathways by reactive oxygen species. Biochim Biophys Acta BBA - Mol Cell Res 1863:2977-2992.

786 Ricciardi F, Binelli A, Provini A (2006) Use of two biomarkers (CYP450 and acetylcholinesterase) in zebra mussel for the biomonitoring of Lake Maggiore (northern Italy). Ecotoxicol Environ Saf 63:406-412.

789 Roberts L, Cheesman S, Breithaupt T, Elliott M (2015) Sensitivity of the mussel Mytilus edulis to substrate-borne vibration in relation to anthropogenically generated noise. Mar Ecol Prog Ser 538:185-195.

792 Schmidlin S, Parcerisas C, Hubert J, Watson MS, Mees J, Botteldooren D, Devos P, Debusschere E, Hablützel PI (2024) Comparison of the effects of reef and anthropogenic soundscapes on oyster larvae settlement. Sci Rep 14:12580.

795 Smaal AC, Ferreira JG, Grant J, Petersen JK, Strand Ø (eds) (2019) Goods and Services of Marine Bivalves. Springer International Publishing, Cham.

797 Solé M, Kaifu K, Mooney TA, Nedelec SL, Olivier F, Radford AN, Vazzana M, Wale MA, Semmens JM, Simpson SD, Buscaino G, Hawkins A, Aguilar de Soto N, Akamatsu T, Chauvaud L, Day RD, Fitzgibbon Q, McCauley RD, André M (2023) Marine invertebrates and noise. Front Mar Sci 10.

801 de Soto NA, Delorme N, Atkins J, Howard S, Williams J, Johnson M (2013) Anthropogenic noise causes body malformations and delays development in marine larvae. Sci Rep 3:2831.

804 Spiga I, Caldwell GS, Bruintjes R (2016) Influence of Pile Driving on the Clearance Rate of the Blue Mussel, Mytilus edulis (L.). Proc Meet Acoust 27:040005.

806 Statistics-Canada (2019) Statistique Canada. (2019). Production d'aquaculture en quantité et en valeurs. https://www.dfo-mpo.gc.ca/stats/aqua/aqua19-fra.htm.

808 Technical Group on Underwater Noise (2022) European Marine Strategy Framework, recommendations from Technical group on underwater noise for EU threshold values for continuous noise.

811 The United Nations (1982) United Nations Convention on the Law of the Sea. Part XII Protection and Preservation of the Marine Environment.

813 Toonen RJ, Pawlik JR (1994) Foundations of gregariousness. Nature 370:511-512.

814 Vazzana M, Celi M, Maricchiolo G, Genovese L, Corrias V, Quinci EM, de Vincenzi G, Maccarrone V, Cammilleri G, Mazzola S, Buscaino G, Filiciotto F (2016) Are mussels able to distinguish underwater sounds? Assessment of the reactions of Mytilus galloprovincialis after exposure to lab-generated acoustic signals. Comp Biochem Physiol A Mol Integr Physiol 201:61-70.

819 Wale MA, Briers RA, Hartl MGJ, Bryson D, Diele K (2019) From DNA to ecological performance: Effects of anthropogenic noise on a reef-building mussel. Sci Total Environ 689:126-132.

822 Wang Y-Q, Liu Q, Zhou Y, Chen L, Yang Y-M, Shi X, Power DM, Li Y-F (2023) Stage-Specific Transcriptomes of the Mussel Mytilus coruscus Reveals the Developmental Program for the Planktonic to Benthic Transition. Genes 14:287.
825 Wilkens SL, Stanley JA, Jeffs AG (2012) Induction of settlement in mussel (Perna canaliculus) larvae by vessel noise. Biofouling 28:65-72.

827 Winston GW, Moore MN, Kirchin MA, Soverchia C (1996) Production of reactive oxygen
828 species by Hemocytes from the marine mussel, Mytilus edulis: Lysosomal
829 localization and effect of xenobiotics. Comp Biochem Physiol C Pharmacol
830 Toxicol Endocrinol 113:221-229.

831 Worley B, Powers R (2016) PCA as a practical indicator of OPLS-DA model reliability.
832 Curr Metabolomics 4:97.

833 Xia J, Wishart DS (2010) MSEA: a web-based tool to identify biologically meaningful
834 patterns in quantitative metabolomic data. Nucleic Acids Res 38:W71-7.

835 Yang J-L, Shen P-J, Liang X, Li Y-F, Bao W-Y, Li J-L (2013) Larval settlement and
836 metamorphosis of the mussel Mytilus coruscus in response to monospecific
837 bacterial biofilms. Biofouling 29:247-259.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Diagram illustrating blue mussels’ development in an environment perturbed by shipping noise.</figcaption></figure>

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844 **Fig. 2.** Experimental procedure developed for the exposition of blue mussel larvae during

845 metamorphosis to shipping noise. dpf: day post-fecundation.

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<figure><img src="image_5.png" /><figcaption>a</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>b</figcaption></figure>

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851 **Fig. 3. a,** Spectrograms of the cargo ship original *in situ* sound during the arrival (left) and the departure (right). **b,** Power spectral density (PSD) analysis of the cargo ship original sound recorded *in situ* and at the center of the middle cylinder below the water surface, for all different experimental conditions and the control, during the arrival and the departure (window length: 1 second, window type: Hann).
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 4.** Impact of the shipping noise on blue mussel post-larvae's prostaglandins and fatty acid oxidative metabolism. **a**, OPLS-DA scores plot based upon the fitted model (R²Y = 0.97, Q² = 0.95, CV-ANOVA = 0.0016) of targeted lipid oxidation products quantification as a function of shipping noise level. Green circles refer to ambient noise control (n=5), yellow inversed triangles to low (~121 re 1µPa) (n=6), orange triangles to medium (~127 re 1µPa) (n=5), and red boxes to high (~151 dB re 1 µPa) (n=4) shipping noise. **b**, Contribution and importance in the model (VIP) of the compounds identified by the OPLS-DA-based screening to explain the impact of shipping noise upon post-larval mussel metabolome.</figcaption></figure>

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**Fig. 5.** Impact of the shipping noise on the blue mussel post-larvae's amino acids, energy and oxidative metabolisms. **a,** Contribution of the metabolites identified by the multiblock OPLS-DA-based screening of the untargeted ¹H-NMR and MS data, as well as the targeted LC-MS data. **b,** Multi-block OPLS-DA scores plot based upon the fitted model (R²Y = 1.00, Q² = 0.98, CV-ANOVA = 0.0010) on untargeted ¹H-NMR and MS data. And **c,** OPLS-DA scores plot based upon the fitted model (R²Ycum = 1.00, Q²cum = 0.99, CV-ANOVA = 0.0245) on the targeted LC-MS amino acid and energy metabolism data. Green circles refer to ambient noise control (n=4), yellow inversed triangles to low (~121 re 1µPa) (n=6), orange triangles to medium (~127 re 1µPa) (n=5), and red boxes to high (~151 re 1 µPa) (n=4) shipping noise. **d,** Metabolite Set Enrichment Analysis performed using the list of metabolites obtained from the untargeted and targeted metabolomics OPLS-DA models. The bubble sizes represent the fold enrichment of the metabolic pathways identified and the blue colour gradient corresponds to the P-value corrected for the False Discovery Rate. **e,** Boxplot (minimum, first quartile, median, third quartile, maximum) representing the percentage of metamorphosis success of the larvae exposed to different noise levels (*P-value* <0.05). **f,** boxplot (minimum, first quartile, median, third quartile, maximum) representing the size of the larvae at the metamorphosis stage (PII) and their post-metamorphosis growth (D) (*P-value* <0.05, outliers are represented by dots).