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. 2023 Apr 17;170(5):61. doi: 10.1007/s00227-023-04206-3

‘Habitat-associated soundscape’ hypothesis tested on several coral reefs within a lagoon (Bora-Bora Island, French Polynesia)

Lana Minier 1,2,#, Xavier Raick 3,#, Emma Gairin 1,4, Tehani Maueau 5, Vincent Sturny 6, Eric Blin 7, Eric Parmentier 3, Frédéric Bertucci 1,3,8,✉,#, David Lecchini 1,2,#
PMCID: PMC10108810  PMID: 37089665

Abstract

Coral reefs encompass different habitats that have their own living communities. The present study aimed to test the hypothesis that these different kinds of habitats were characterized by specific soundscapes. Within the lagoon of Bora-Bora, acoustic recordings and visual surveys of substrate type and fish communities were conducted on four reef sites belonging to the three main geomorphological habitats (fringing reef, channel reef, barrier reef) from February to April 2021. Two acoustic parameters were measured for each site and month, during the day and at night: the peak frequency (Fpeak, in Hz) and the corresponding power spectral density (PSDpeak, in dB re 1 µPa2 Hz−1). Our results showed that each geomorphological unit could be characterized by these two parameters and therefore had a specific acoustic signature. Moreover, our study showed that a higher living coral cover was significantly positively correlated with Fpeak in the low-frequency band (50–2000 Hz) during day-time. Although biodiversity indices based on visual surveys did not differ significantly, fish communities and soundscapes were significantly different between sites. Overall, our study underlines the importance of passive acoustics in coral reef monitoring as soundscapes are habitat specific.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00227-023-04206-3.

Keywords: Coral reefs, Biophony, Passive acoustic monitoring, Remote sensing, Fish sounds

Introduction

In the context of the current global environmental changes, being able to monitor biodiversity in endangered ecosystems such as coral reefs is a necessary challenge for ecologists and conservationists (Barnosky et al. 2011; Wilkinson et al. 2013; Lecchini et al. 2021a). Several methods of biodiversity evaluation such as trawling, visual observations and counting, or camera trapping have been used over the past decades (Jackson et al. 2014; Moritz et al. 2018). Unfortunately, they often only consider the most noticeable species and are often time-consuming, invasive, and limited to accessible sites (Zenone et al. 2017). Moreover, numerous ecosystems, notably coral reefs, are complex three-dimensional habitats with many cryptic invertebrate or fish species (Plaisance et al. 2011; Galzin et al. 2016; Lammers and Munger 2016), which can be overlooked by traditional visual surveys. In recent years, the use of sounds produced by animals, i.e., Passive Acoustic Monitoring (PAM) (Sueur and Farina 2015; Sugai et al. 2019), has provided new and complementary insights into the monitoring of biodiversity patterns within dense and complex ecosystems both in terrestrial (Obrist et al. 2010; Blumstein et al. 2011), and more recently, marine environments (Bertucci et al. 2016, 2020a; Di Iorio et al. 2018; Bolgan and Parmentier 2020; Bolgan et al. 2020; Pieretti and Danovaro 2020; Raick et al. 2021, 2023; Havlik et al. 2022).

In coral reefs, many sympatric fish species produce sounds in various social contexts, such as during agonistic interactions with competitors, as well as during courtship and spawning (Fish and Mowbray 1970; Tricas and Boyle 2014). Worldwide, 27% of the 179 fish families that live on tropical coral reefs are currently considered as vocal (Lobel et al. 2010). Recently, Parmentier and collaborators (2021) estimated that approximately half of the fish families (32 of 66) found on Moorea Island (French Polynesia) may produce sounds. This high diversity of vocal fishes is reflected by an important sonic diversity, which constitutes a key part of the biophony (Pijanowski et al. 2011; Bertucci et al. 2020b). The biophony also encompasses sounds generated by many other marine animals such as crustaceans, molluscs, or echinoderms (Cato 1978; Radford et al. 2008; Staaterman 2016; Coquereau et al. 2016). In addition to the biophony, sounds produced by geological/meteorological events (i.e., the geophony) and sounds produced by human activities (i.e., the anthropophony) are part of soundscapes (Wenz 1962; Kinda 2013; Buscaino et al. 2016; Ferrier-Pagès et al. 2021). The Acoustic Habitat Hypothesis states that the habitats that sound-dependent species choose have unique acoustic characteristics, based on their functional needs and their ability to produce and detect sounds (Mullet et al. 2017). One of its basic foundations is the hypothesis of “habitat-associated soundscapes”. Bertucci and collaborators (2015) showed that different neighbouring habitats of Moorea’s reef (French Polynesia) had different intensities in their power-spectra within the 20–5000 Hz frequency bandwidth. These variations in sound levels could be linked either to the density and diversity of fish and invertebrate species (Nedelec et al. 2015; Bertucci et al. 2016; Pieretti et al. 2017; Wilson et al. 2020), or to physical environmental properties such as the type of substrate, structural habitat complexity, or health status of the reef (Lammers et al. 2008; Kennedy et al. 2010). Despite these recent studies, detailed knowledge about the links between marine biodiversity and acoustic features are still scarce. It is particularly important because underwater soundscapes are strongly impacted by noise pollution generated by human activities (Duarte et al. 2021; Ferrier-Pagès et al. 2021).

Bora-Bora is one of the most famous international travel destinations and is considered as the tourism showcase of the French Polynesian territory (Blondy 2016; Lecchini et al. 2021a). In 2020/2021, the COVID-19 pandemic led to drastic restrictions on human activities worldwide, and tourism was one of the most impacted economic sectors (Utkarsh and Sigala 2021). All tourism activities ceased in French Polynesia during multiple lockdowns due to social and travel restrictions (Lecchini et al. 2021b). The present study aimed at taking advantage of the absence of tourism activities on Bora-Bora from February to April 2021 to test the hypothesis of “habitat-associated soundscapes” with different kinds of reefs within the lagoon and to explore a possible link between marine biodiversity and soundscapes in coral reefs.

Methods

Study sites

The study was conducted in the lagoon of Bora-Bora (French Polynesia) from February to April 2021, during the warm season. Bora-Bora is a 20 km2 tropical volcanic island circled by a 70 km2 barrier reef (Lepresle et al. 2016). Four reef sites were selected on the three main geomorphological habitats (from the coast to the ocean: fringing reef, channel reef, barrier reef) in the South part of Bora-Bora’s lagoon (Bertucci et al. 2020b; Lecchini et al. 2021a). Two sites were chosen on the barrier reef: BR1 (16°32′47.904″ S, 151°47′9.312″ W) and BR2 (16°31′46.956″ S, 151°47′19.823″ W) (between 1 and 2 m depth). One site was located on the fringing reef (FR1—between 1 and 3 m depth) (16°32′11.543″ S, 151°43′30.575 W) and one in the channel (C1—between 1 and 5 m depth) (16°30′7.416 S, 151°46′5.448″ W) (Fig. 1).

Fig. 1.

Fig. 1

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a Map of the Pacific Ocean showing the location of French Polynesia (red square) and Bora-Bora (red dot), and b map of Bora-Bora showing the 4 study sites. FR1 on the fringing reef, BR1 and BR2 on the barrier reef, and C1 in the channel. The study was conducted during a period of COVID-19-related social restrictions from mid-February to mid-May 2021. There were no international tourists on Bora-Bora and boat traffic was low in the lagoon due to reduced local tourism activities. The maps were drawn by the authors using PhotoFiltre 7 software (version 7.1.2—www.photofiltre.com). Dark grey represents land areas, light grey represents reef areas

The substrate composition of each site was described by setting up three transect lines (25 m long). The substrate type (living coral, dead coral, sand, and macro-algae) were reported every meter using the point intercept transect method (Loya 1978). FR1 had the highest living coral cover (mean ± SD: 67 ± 7%). The highest percentage of dead coral was observed on C1 (61 ± 21%). The four sites also differed in terms of sand cover, with 27 ± 10% of sand on BR1 and less than 7% on the other sites. Lastly, macro-algae were observed on the barrier reef sites only (BR1: 7 ± 4% and BR2: 8 ± 1%) (Table 1).

Table 1.

Proportion (%) of substrate cover (living coral, dead coral, sand, and macro-algae) of the four reef sites inside the lagoon of Bora-Bora

% Living coral % Dead coral % Sand % Macro-algae
FR1 67 ± 7 31 ± 7 2 ± 2 0
BR1 31 ± 2 35 ± 16 27 ± 10 7 ± 4
BR2 32 ± 3 56 ± 16 4 ± 3 8 ± 1
C1 32 ± 3 61 ± 21 7 ± 4 0
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FR1: the fringing reef, BR1 and BR2: the barrier reef, and C1: channel. Values are mean ± SD

Acoustic recordings

Autonomous SNAP acoustic recorders (Loggerhead Instruments; Sarasota, FL, USA; https://www.loggerhead.com/snap) equipped with HTI-96-Min hydrophones (sensitivity of 169.9 dB and 170.1 dB re 1 V for a sound pressure of 1 µPa; flat frequency response from 2 Hz to 30 kHz) were used to record the soundscapes at each study site. Acoustic recorders were positioned at 2 m depth and always at the same location for each temporal replicate to reduce variability. Three temporal replicates were realized per site (February, March, and April) over the two days following the new moon to standardize recording conditions (Galzin 1987; Lecchini and Galzin 2005). Recordings were conducted during 24 h with a duty cycle of 1 min of recording every 10 min at a sampling rate of 44.1 kHz (16-bit resolution).

The variability of the speed of sound propagation due to sea-water temperature (T) and salinity (S) was neglected due to their limited variations during the warm season on Bora-Bora (T = 28 ± 1 °C, S = 36.1 ± 0.2; mean ± SD, from February to April, data from SNO Corail—http://www.criobe.pf/). In the case of bad weather conditions (wind speed > 20 knots or wave period > 10 s) during the initially scheduled 24 h of recording, the acoustic recorders were left in place for an additional day to avoid meteorological bias.

Soundscapes analyses were performed using PAM Guide (Merchant et al. 2015) in R version 4.1.1 (R Core Team 2021). Recordings were subsampled at 20 kHz. Soundscapes were divided in two frequency bands: a high-frequency band, between 2 and 10 kHz, and a low-frequency band, between 50 Hz and 2 kHz (Raick et al. 2021). The high-frequency band is known to be dominated by invertebrate sonic activities (Hildebrand 2009; Coquereau et al. 2016; Raick et al. 2021), and the low-frequency band is known to be dominated by fish sounds (Lobel et al. 2010; Tavolga et al. 2012; Raick et al. 2021, 2023).

A different size of the Fast Fourier Transform (FFT) was used for each frequency band: FFT = 64 points for the high-frequency band, and FFT = 256 points for the low-frequency band (Raick et al. 2021). For both bands, a Hamming window with an overlap of 50% was used. This filter is defined by good frequency resolution, reduced spectral leakage, and acceptable noise performance (Bojkovic et al. 2017). Both frequency bands were studied during the day-time (05:30 a.m.–05:25 p.m) and night-time (05:30 p.m.–05:25 a.m.) in order to separate the sounds produced by diurnal and nocturnal communities (Galzin 1987; Bertucci et al. 2015, 2020b; Raick et al. 2021).

The median (50th percentile) Power Spectral Density (PSD, in dB re 1 µPa2 Hz−1) of each temporal replicate (one per month) was calculated for both frequency bands and for both time periods (day and night). PAM Guide was used to generate PSD value. Graphics were produced with Python version 3.8.3 (Van Rossum and Drake 1995). On PSD plots, the peak frequency (Fpeak, in kHz) and the corresponding power spectral density amplitude (PSDpeak, in dB re 1 µPa2 Hz−1) were displayed for each site and temporal replicate.

Fish records

All fish, except Bleniidae, Carapidae, Gobiidae, and Triperygiidae that were too cryptic to be observed (Siu et al. 2017), were recorded to the species level by visual surveys along the three transects (25 m long and 4 m wide, i.e., 100 m2 per transect) on each site during the morning (08:00 a.m.–11:00 a.m.) over the two days following the new moon (Lecchini and Galzin 2005; Nakamura et al. 2009). These fish surveys were performed before the start of the audio recordings. As sonic benthic invertebrates are mainly cryptic, they were not counted. Three parameters were extracted from the surveys on each site to describe fish communities (Table 2): fish density (number of individuals per 100 m2, D), species richness (total number of species per 100 m2, SR), and the Shannon–Wiener index of fish diversity (H-index). Based on the list of vocal fish species of French Polynesia (Parmentier et al. 2021), three additional parameters were calculated: density of vocal fish species (Dvocal), species richness of vocal fish species (SRvocal), and H-index of vocal fish species (Hvocal).

Table 2.

Names and abbreviations of vocal fish species considered to calculate the three parameters

Fish name Abbreviation Fish name Abbreviation
Abudefduf septemfasciatus Asep Dascyllus flavicaudus Df
Abudefduf sexfasciatus Asex Diodon histrix Dh
Acanthurus blochii Ab Epinephelus merra Em
Acanthurus guttatus Ag Forcipiger longirostris Fl
Acanthurus lineatus Al Gomphosus varius Gv
Acanthurus nigricans Ans Heniochus chrysostomus Hc
Acanthurus nigricauda Ana Lutjanus fulvus Lf
Acanthurus triostegus At Mulloidichtys flavolineatus Mf
Agrilinus sordidus Asor Mulloidichtys vanicolensis Mv
Balistapus undulatus Bu Myripristis adusta Ma
Caranx melampygus Cme Myripristis pralina Mp
Centropyge bispinosa Cbis Neoniphon sammara Ns
Centropyge flavissima Cf Ostracion cubicus Oc
Cephalopholis argus Car Ostracion meleagris Om
Chaeodon lunula Clu Parupeneus barberinus Pb
Chaetodon auriga Cau Parupeneus multifasciatus Pmu
Chaetodon bennetti Cben Pomacentrus pavo Pp
Chaetodon citrinellus Cc Rhinecanthus aculeatus Ra
Chaetodon ephippium Ce Sargocentron microstoma Sm
Chaetodon ornatissimus Co Sargocentron spiniferum Ssi
Chaetodon pelewensis Cp Scarus altipinnis Sa
Chaetodon reticulatus Cr Scarus oviceps So
Chaetodon trifasciatus Cti Scarus psittacus Sp
Chaetodon ulietensis Cul Scarus schlegeli Sch
Chaetodon unimaculatus Cun Scarus sordidus Sco
Chaetodon vagabundus Cva Stegastes nigricans Sn
Chromis iomelas Cio Sufflamen bursa Sbu
Chromis viridis Cvi Thalassoma amblycephalum Ta
Chrysiptera glauca Cgl Thalassoma hardwicke Th
Chrysiptera leucopoma Cle Thalassoma lutescens Tl
Coris aygula Cay Thalassoma purpureum Tp
Ctenochaetus binotatus Cbin Zanclus cornutus Zc
Ctenochaetus striatus Cst Zebrasoma scopas Zs
Dascyllus aruanus Da Zebrasoma veliferum Zv
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Statistical analysis

All statistical analyses were conducted using R version 4.1.1 (R Core Team 2021) at a significance level of α = 0.05. Acoustic and fish parameters did not meet normality and homoscedasticity assumptions (Shapiro–Wilk’s tests, W = 0.87–0.98, all P < 0.001). Non-parametric tests were therefore used. Moreover, for both acoustic parameters (Fpeak and PSDpeak), no significant differences between the three sampling months were found (Kruskal–Wallis tests, χ2 < 2.81, P > 0.24—Table 3). February, March, and April were hence considered as replicates in the following statistical analyses. Every one-minute recording containing the sound of a motorboat were removed from the dataset after listening to audio files using VLC Media player (version 3.0.16). Similar statistical results were obtained in both the complete dataset and the dataset with near boats passages removed (Mann–Whitney U test, V = 38 and V = 58, P = 0.38 and P = 0.75 respectively). Thereafter, the analysis focused on the complete data set.

Table 3.

Comparisons of the three temporal replicates (months) for each site (FR1: fringing reef, BR1 and BR2: barrier reef, and C1: channel reef) for the peak frequency (Fpeak) and the power spectral density (PSDpeak) with Kruskal–Wallis tests

Kruskal–Wallis tests Variable χ2 Df P
FR1 Power spectral density 1.37 2 0.50
Peak frequency 2.1 2 0.34
BR1 Power spectral density 0.14 2 0.93
Peak frequency 1.4 2 0.49
BR2 Power spectral density 2.24 2 0.32
Peak frequency 2.81 2 0.24
C1 Power spectral density 1.97 2 0.37
Peak frequency 0.91 2 0.63
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Df degrees of freedom

To analyze the acoustic parameters (Fpeak and PSDpeak) either between the four sites or between day- and night-time, Kruskal–Wallis tests were used, followed by Dunn’s post-hoc tests. To compare fish community parameters (D, SR, H-index, Dvocal, SRvocal, and Hvocal-index) between the four sites, Kruskal–Wallis tests were used, followed by Dunn’s post-hoc tests. A canonical correspondence analysis (CCA) was conducted to test the influence of benthic cover on vocal fish community composition (Ter Braak 1987; Di Iorio et al. 2021; Raick et al. 2023). The CCA was used to find the best dispersion of fish species and to relate them to combinations of environmental variables (i.e., benthic cover features) (Ter Braak 1987). A “forward stepwise variable selection” model-building process (which gradually adds significant variables based on the Akaike information criterion) was used to determine which variables are most relevant for the model (Chambers and Hastie 2017). The relevant variables were thus added to the ordination plot to study their relationships to fish community composition (function ordi-ellipse, vegan package with a 95% confidence interval). The CCA was conducted only on vocal fish species parameters (Dvocal, SRvocal, and Hvocal-index) since they were strongly auto-correlated with all fish species parameters (D, SR, and H-index) (Spearman’s correlation, respectively: ρ = [0.94, 0.80, 0.80], P = [0.051, 0.33, 0.33]).

Lastly, to analyze a possible link between marine biodiversity and acoustic diversity, Spearman correlation tests (with Holm’s correction) were conducted between the acoustic parameters (Fpeak and PSDpeak), the substrate composition (% of living coral, dead coral, sand, and macro-algae), and vocal fish species parameters (Dvocal, SRvocal, and Hvocal-index) during the day-time in the low-requency band. For night-time in the low-frequency band and during day- or night-time in the high-frequency band, correlations were only tested between acoustic parameters and the substrate composition because of the lack of invertebrate counts and the difficulty to conduct night surveys and to link observations with vocal fish species parameters).

Results

Biophony in the high-frequency band (2–10 kHz)

During day-time (Fig. 2a), three of the four studied sites (BR1, BR2, and C1) had a similar median Fpeak, between 5.1 and 6.2 kHz, while FR1 had a significantly lower Fpeak centred at 3.7 kHz (Kruskal–Wallis test, χ23 = 13.3, P = 0.003—Online Resource 1; see Dunn post-hoc tests’ results in Online Resource 2). During night-time, Fpeak displayed a similar pattern than during day-time (Fig. 2b). BR1, BR2, and C1 had an Fpeak between 5.3 and 6.3 kHz, while FR1 had a significantly lower Fpeak, centred at 3.7 kHz (Kruskal–Wallis test, χ23 = 10.1, P = 0.01).

Fig. 2.

Fig. 2

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Acoustic parameters (Fpeak and PSDpeak) for the high-frequency band (2–10 kHz) for each site. Fpeak (a, b) and PSDpeak (c, d). Panels a and c are for day-time. Panels b and d are for night-time. Values are means of the three temporal replicates ± standard deviation. Different letters indicate significant differences at P < 0.05, provided by Dunn’s post hoc tests. Sites with similar letters are not significantly different. Sites with different letters are significantly different

During day-time, the median PSD level (PSDpeak) of FR1 was significantly higher than that of BR1 and BR2 (Fig. 2c, Kruskal–Wallis test, χ23 = 21.6, P < 0.001—Online Resource 1; see Dunn post-hoc tests’ results in Online Resource 2). BR2 had the lowest PSDpeak (74.7 ± 0.6 dB re 1 µPa2 Hz−1). C1 had a significantly higher PSDpeak than BR2 (83.5 ± 0.9 dB re 1 µPa2 Hz−1). During night-time (Fig. 2d), FR1 had the highest PSDpeak (89.4 ± 0.1 dB re 1 µPa2 Hz−1), and BR2 the lowest (75.9 ± 1.0 dB re 1 µPa2 Hz−1) (Kruskal–Wallis test, χ23 = 18.9, P < 0.001).

The four sites showed no significant differences between day-time and night-time for Fpeak or PSDpeak (Online Resource 1—Kruskal–Wallis test, χ212 < 92, P ≥ 0.24). Lastly, when looking together PSDpeak and Fpeak values during day-time and night-time, FR1 and BR2 were the most different sites. FR1 had the highest PSDpeak and the lowest Fpeak, while BR2 had the lowest PSDpeak and the highest Fpeak. The two barrier sites (BR1 and BR2) were the most similar habitats (Fig. 2).

Biophony in the low-frequency band (50–2000 Hz)

During day-time (Fig. 3a), BR1, BR2, and C1 had a similar Fpeak, between 230 and 260 Hz, while FR1 had a significantly higher Fpeak centred at 350 Hz (Kruskal–Wallis test, χ23 = 19.5, P < 0.001—Online Resource 1; see Dunn post-hoc tests’ results in Online Resource 2). During night-time (Fig. 3b), BR1 and BR2 had a similar Fpeak (259 ± 1 Hz and 261 ± 24 Hz respectively), while C1 had the lowest Fpeak (168 ± 5.0 Hz), and FR1 the highest (314 ± 37 Hz) (Kruskal–Wallis test, χ23 = 18.2, P < 0.001).

Fig. 3.

Fig. 3

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Acoustic parameters (Fpeak and PSDpeak) for the low-frequency band (50–2000 Hz) for each site. Fpeak (a, b) and PSDpeak (b, d). Panels a and c are for day-time. Panels b and d are for night-time. Values are means of the three temporal replicates ± standard deviation. Different letters indicate significant differences at P < 0.05, provided by Dunn’s post hoc tests. Sites with similar letters are not significantly different. Sites with different letters are significantly different

During day-time (Fig. 3c), the PSDpeak of FR1 was significantly higher than in BR2, which had the lowest PSDpeak (101.6 ± 0.6 dB re 1 µPa2 Hz−1, and 95.9 ± 0.4 dB re 1 µPa2 Hz-1 respectively—Kruskal–Wallis test, χ23 = 14.8, P < 0.001). C1 and BR1 had a similar PSDpeak centred at 99.5 dB re 1 µPa2 Hz−1 (100 ± 0.8 dB re 1 µPa2 Hz−1, and 98.6 ± 1.3 dB re 1 µPa2 Hz−1 respectively). During night-time (Fig. 3d), PSDpeak had inter-site patterns similar to day-time ones (Kruskal–Wallis test, χ23 = 20.1, P < 0.001). FR1 PSDpeak was significantly higher than BR2 (101.5 ± 0.1 dB re 1 µPa2 Hz−1, and 96.3 ± 0.9 dB re 1 µPa2 Hz−1 respectively), which had the lowest PSDpeak (Fig. 3b). Similarly, C1 and BR1 had a similar PSDpeak centred at 99 dB (99.5 ± 1.0 dB re 1 µPa2 Hz−1, and 98.2 ± 0.4 dB re 1 µPa2 Hz−1 respectively).

The four sites showed no significant differences between day-time and night-time for Fpeak or PSDpeak (Online Resource 1—Kruskal–Wallis test, χ212 < 95, P > 0.19). Lastly, when looking together PSDpeak and Fpeak values during day-time and night-time, FR1 had the highest values of PSDpeak and Fpeak during the day- and night-time. C1 had the lowest Fpeak while BR2 had the lowest PSDpeak. The two barrier sites (BR1 and BR2) were the most similar habitats (Fig. 3).

Vocal fish communities & link between acoustic and ecological parameters

The fish communities varied significantly among the four sites in terms of H-index for all fish (Kruskal–Wallis test, χ23 = 14.5, P = 0.002) and H-index for vocal fish species (Kruskal–Wallis test, χ23 = 9.6, P = 0.02) (Table 4, Online Resource 1). Thus, FR1 had the lowest H-index and Hvocal-index. The highest H-index was found on BR1 and the highest Hvocal-index was found on BR2. In contrast, the species richness (SR) and density (D) of all species and of vocal fish species did not significantly vary among the four sites (Kruskal–Wallis tests, χ23 = 0.78–6.62, all P > 0.05—Table 3, Online Resource 1).

Table 4.

Species richness (SR and SRvocal), density (D and Dvocal), and H-index (H and Hvocal) of all fish and of vocal fish species of the four study sites

All fish Vocal fish species
SR D H SRvocal Dvocal Hvocal
FR1 28 ± 19 288 ± 223 2.02 ± 0.29 26 ± 17 281 ± 213 1.98 ± 0.27
BR1 29 ± 7 258 ± 132 2.62 ± 0.22 23 ± 6 239 ± 124 2.32 ± 0.15
BR2 39 ± 20 320 ± 219 2.53 ± 0.27 31 ± 14 281 ± 179 2.34 ± 0.23
C1 22 ± 10 226 ± 133 2.35 ± 0.18 20 ± 10 214 ± 121 2.20 ± 0.20
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Values are means of the three temporal replicates ± standard deviation

The CCA indicated that the vocal fish community composition was mainly influenced by the percentages of macro-algae, living coral, and sand (Fig. 4). Among the 64 vocal species observed, 31 were specific to one site, but nine of the ten most abundant vocal species were present in all four sites (but with some different abundances according to the sites). Thus, RF1 was mainly characterized by a high percentage of living coral (fish species specific to this site: Chaetodon bennetti, Chaetodon ephippium, Myripristis pralinia, and Thalassoma lutescens). BR1 and BR2 were mainly characterized by the highest proportion of sand and macro-algae (fish species specific to these two sites: Ctenochaetus binotatus, Mulloidichtys vanicolensis, Neoniphon samara, and Sufflamen bursa). Those three sites had positive CCA1 scores (i.e., were located on the right side of the axis). C1 had negative CCA1 scores (i.e., were located on the left side of the axis), with few site-specific species (Centropyge bispinosa, Chaetodon pelewensis, Chromis iomelas, and Dascyllus flavicaudus).

Fig. 4.

Fig. 4

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Canonical Correspondence Analysis (CCA) ordination plots of the vocal fish community composition. The analysis is based on Bray–Curtis dissimilarities of relative abundance of the 64 vocal fish species present at the four sites (C1 in red, BR1 in orange, BR2 in green and FR1 in pink, and three temporal replicates per site: colour points). Black arrows show the influence of benthic cover features. Ellipses are 95% confidence intervals for each site. For the correspondence between the abbreviations and the list of species, see Table 2

Lastly, the possible link between marine biodiversity and soundscapes was analyzed. Spearman correlation tests showed that only the percentage of living coral (LC) was positively correlated with the peak frequency (Fpeak) during day-time and in the low-frequency band (ρ = 0.91; P = 0.001—Table 5). No significant correlation between any acoustic and any environmental parameters were detected at night in the low-frequency band and during the day- or night-time in the high-frequency band (see Online Resource 3).

Table 5.

Matrix of Spearman correlation tests between acoustic and ecological data during the day-time in the low-frequency band

Fpeak PSDpeak SRvocal Dvocal Hvocal
LC

r = 0.57

P = 0.002

r = 0.31

P = 0.21

r = 0.05

P = 0.96

r = 0.20

P = 0.99

r = –0.58

P = 0.1
DC

r = –0.51

P = 0.41

r = –0.52

P = 0.09

r = 0.02

P = 0.88

r = –0.16

P = 0.76

r = 0.20

P = 0.44
S

r = –0.18

P = 0.68

r = 0

P = 0.98

r = 0.05

P = 0.73

r = 0.05

P = 0.71

r = 0.45

P = 0.28
Ma

r = –0.18

P = 0.79

r = –0.34

P = 0.3

r = 0.43

P = 0.29

r = 0.48

P = 0.35

r = 0.55

P = 0.09
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r corresponds to the correlation value, and P to the P-value. Only the percentage of living coral (LC) was significantly and positively correlated with the peak frequency (Fpeak). Significant values are in bold

DC dead coral, S sand, Ma macro-algae

Discussion

In coral reefs, several studies have found a spatial variation in reef sounds, suggesting that habitat-associated soundscapes can provide essential information about the quality of a habitat (Kennedy et al. 2010; Staaterman et al. 2013; Bertucci et al. 2015) and its animal communities (Nedelec et al. 2015; Bertucci et al. 2020b; Raick et al. 2023). On Bora-Bora, we showed that three main morphological units (barrier reef, fringing reef and channel) differed in terms of two acoustic features: the peak frequency (Fpeak) and the corresponding power spectral density (PSDpeak). In particular, we showed that a higher living coral cover was significantly positively correlated with Fpeak in the low-frequency band during day-time. Several studies (Nedelec et al. 2015; Raick et al. 2023) provided evidence that variations in the soundscape features of coral reefs in French Polynesia could be linked with habitat and their corresponding vocal communities. Other studies showed that a reef with a higher living coral cover is often associated to higher sound pressure levels (Tricas and Boyle 2014; Bertucci et al. 2016) since healthy coral communities can shelter more species than damaged coral reefs. Therefore, changes in species assemblage associated with different states of habitat degradation or anthropogenic pressures could be detected by means of acoustics, i.e., through altered acoustic activity of organisms, acoustic diversity or changes in some spectral characteristics. Significant differences in the high-frequency parameters are likely due to different invertebrate communities and/or in their abundance (Hildebrand 2009; Raick et al. 2021). Yet, there is still an important lack of knowledge about the sounds produced by marine invertebrate species compared to fish, despite that their signals dominate underwater soundscape of many coastal habitats (Staaterman 2016). Their low interference with anthropogenic noise would however make them very good candidates for detecting environmental changes (Staaterman et al. 2014; Staaterman 2016). To date, snapping shrimps (Johnson et al. 1947; Knowlton and Moulton 1963; Chitre et al. 2012), sea urchins (Radford et al. 2008), lobsters (Meyer-Rochow and Penrose 1976; Patek 2001; Buscaino et al. 2011) and crabs (Salmon 1967; Salmon and Hyatt 1983), for example, are already known to contribute to the soundscapes of many temperate, subtropical and tropical coastal habitats. Similarly, despite fish communities and soundscapes in the low-frequency range were significantly different, biodiversity indices based on visual surveys did not differ significantly (with the exception of the H-index in the fringing reef). This suggests that the acoustic environment is influenced by the activity of some vocal species and not by the totality of species present. A prerequisite for future studies on the ecology of marine organisms and underwater soundscapes is therefore the specific identification of soniferous species and the characterisation of the sounds generated by these animals. The nocturnal fish biophony needs to be better understood. Indeed, diurnal vocalizations, mainly attributed to Pomacentridae, can be more accurately characterised thanks to visual confirmations (Tricas and Boyle 2014; Raick et al. 2021, 2023). At night, visual observations are more difficult and the use of light would alter fish behaviours. Moreover, complementary non-invasive methods, such as environmental DNA could be used in future surveys to provide more precise evaluation of biodiversity.

The variations in acoustic characteristics were not significant between the three temporal replicates (monthly from February to April 2021), making PAM efficient to study local biophony through time. Our study provides information about ambient reef sounds that are habitat-specific in the absence of a strong human activity (due to COVID-19 sanitary restrictions). This will therefore constitute a baseline for future monitoring studies on the effects of the presence or absence of tourists on coral reefs. It is now well accepted that anthropogenic noise is an emerging pollutant and threat for subaquatic environments. An increasing number of studies show that higher noise levels in marine environments linked to human activities are impacting animals and their ecosystems in complex ways through acute and chronic stresses (Duarte et al. 2021; Ferrier-Pagès et al. 2021). Many aspects such as effects on population dynamics, and on cumulative impacts with other stressors are still insufficiently understood.

Nevertheless, while acoustic recorders can now be deployed for long periods, in places that are not easily accessible, making sounds an almost continuous proxy of biodiversity, advances in sound detection and soundscape description are needed for long-term acoustic monitoring to keep pace with current environmental changes and associated biodiversity loss. Two recent studies (Pieretti and Danovaro 2020; Dimoff et al. 2021) suggested that the extensive use of acoustic monitoring is hampered by the lack of algorithms enabling the discrimination of different sound sources (e.g., geophysical, anthropogenic, and biological). In this perspective, the use of artificial intelligence to develop automatic learning and classification models based on sound libraries is very promising and would be greatly needed in order to speed up data processing (Ross et al. 2018; Bergler et al. 2022). Hopefully, such improvement will strengthen the relevance of acoustic tools in national and international regulatory frameworks.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1: (DOCX 16 KB) (15.5KB, docx)
Supplementary file 2: (DOCX 20 KB) (20.1KB, docx)
Supplementary file 3: (DOCX 18 KB) (17.9KB, docx)

Acknowledgements

We would like to thank the staff of Polynésienne des Eaux, Ia Vai Ma Noa Bora-Bora and the Mairie of Bora Bora for their help. We thank Lucia Di Iorio for discussions on the “forward stepwise variable selection”.

Author contribution

LM, XR, and DL designed the monitoring protocol. LM, EG, TM, VS, EB, and DL collected the data. LM, XR and FB analysed the data and prepared the figures. LM, XR and FB wrote the manuscript. EP and DL reviewed and edited the different version of the manuscript. All authors reviewed the final version of the manuscript.

Funding

This work has received several grants: Fondation de France (2019-08602), Ministère de l’Economie verte et du domaine—Délégation à la recherche de Polynésie française (contrat N3622 MED-EPHE), Office Français de la Biodiversité (AFB/2019/385—OFB.20.0888), Polynésienne des Eaux and Agence Nationale de la Recherche (ANR-19-CE34-0006-Manini and ANR-19-CE14-0010-SENSO).

Data availability

All data are fully available upon kind request to the corresponding author (FB).

Code availability

Not applicable.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article.

Ethics approval

The research required no permits. This article does not contain any experiments with human participants or animals performed by any of the authors.

Consent to participate

Not applicable.

Consent for publication

All listed authors agreed on the publication of the present research and accepted responsibility for the work presented here.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Lana Minier and Xavier Raick have contributed equal as first authors

Frédéric Bertucci and David Lecchini have contributed equal as last authors

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Associated Data

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Supplementary Materials

Supplementary file 1: (DOCX 16 KB) (15.5KB, docx)
Supplementary file 2: (DOCX 20 KB) (20.1KB, docx)
Supplementary file 3: (DOCX 18 KB) (17.9KB, docx)

Data Availability Statement

All data are fully available upon kind request to the corresponding author (FB).

Not applicable.

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