# Otolith fingerprints as natural tags to identify juvenile fish life in ports

M. Bouchoucha, Christophe Pécheyran, J.L. Gonzalez, P. Lenfant, Audrey M. Darnaude

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M. Bouchoucha, Christophe Pécheyran, J.L. Gonzalez, P. Lenfant, Audrey M. Darnaude. Otolith fingerprints as natural tags to identify juvenile fish life in ports. Estuarine, Coastal and Shelf Science, 2018, 212, pp.210 - 218. 10.1016/j.ecss.2018.07.008. hal-01859799

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# Otolith fingerprints as natural tags to identify juvenile fish life in ports

Bouchoucha Marc<fnref n="1" />, <fnref n="4" />, <fnref n="5" />, <fnref n="6" />, *, Pécheyran C. <fnref n="2" />, Gonzalez Jean-Louis <fnref n="3" />, Lenfant P. <fnref n="4" />, <fnref n="5" />, Darnaude A.M. <fnref n="6" />

## Abstract :

The construction of ports has caused substantial habitat destruction in coastal areas previously used as nursery grounds by many fish species, with consequences to fish stocks. These artificial coastal areas might provide alternative nursery habitats for several species for juvenile fish abundances and growth in ports, although their contribution to adult stocks had never been estimated. The variability of otolith composition in the juveniles of two Diplodus species was investigated in three contrasting port areas and two adjacent coastal juvenile habitats of the Bay of Toulon (northwestern Mediterranean) in order to determine the possible use of otolith fingerprints as natural tags for the identification of juvenile fishes in ports. The global accuracy of discrimination between ports and coastal areas was very high (94%) irrespective of species, suggesting that otolith fingerprints can be used with confidence to retrospectively identify past residency in the ports of this bay. However, Ba was systematically the most discriminating element, since its concentrations in otoliths were generally higher outside ports than in inside them, probably due to river runoff. Moreover, otolith signatures varied greatly by species and between sampling sites. Furthermore, although Cu and Pb concentrations in water were at least 2.3–34-fold higher inside ports than outside, this was not consistently reflected in fish otoliths, confirming that spatial differences in otolith concentrations depend on the species and do not directly reflect differences in environmental contamination levels. Therefore, it seems unlikely that otolith microchemistry could provide a universal fingerprint capable of discriminating ports from other coastal areas. Nevertheless, the contribution of ports to adult fish populations can be determined well by establishing a library of otolith fingerprints for all juvenile habitats.

**Keywords** : Coastal areas, Nursery habitats, Fish, LA-ICPMS, Contamination

<footnote n="1">¹ Lab Environnement Ressources Provence Azur Corse, Centre Ifremer de Méditerranée ZP de Brégaillon, CS 20330, 83507, La Seyne-sur-Mer, France</footnote>

<footnote n="2">² Univ. Pau et des Pays de l'Adour, CNRS, LCABIE-IPREM UMR 5254, 64053, Pau, France</footnote>

<footnote n="3">³ Lab. Biogéochimie des Contaminants Métalliques, Centre Ifremer de Méditerranée ZP de Brégaillon, CS 20330, 83507, La Seyne-sur-Mer, France</footnote>

<footnote n="4">⁴ Univ. Perpignan Via Domitia, CEntre de Formation et de Recherche sur les Environnements Méditerranéens, UMR 5110, 58 Avenue Paul Alduy, F-66860, Perpignan, France</footnote>

<footnote n="5">⁵ CNRS, CEntre de Formation et de Recherche sur les Environnements Méditerranéens, UMR 5110, 58 Avenue Paul Alduy, F-66860, Perpignan, France</footnote>

<footnote n="6">⁶ CNRS, UMR MARBEC 9190. CC093, Université de Montpellier, Place Eugène Bataillon, 34095, Montpellier, France</footnote>

<footnote>* Corresponding author : Marc Bouchoucha, email address : marc.bouchoucha@ifremer.fr</footnote>
1 Introduction:

2 Shallow coastal areas are used as nursery habitats by many fish species as they are highly productive
3 environments (Harmelin-Vivien et al., 1995; Macpherson et al., 1997; Beck et al., 2001). However,
4 they are increasingly threatened by urbanization (Lotze et al., 2006; Airoldi and Beck, 2007; Halpern
5 et al., 2008), in particular by port constructions (Rodríguez-Rodríguez et al., 2015). Several recent
6 studies have suggested that coastal man-made infrastructures, particularly those found inside ports,
7 might provide suitable alternative nursery habitats for certain fish species (e.g. Pizzolon et al., 2008;
8 Bouchoucha et al., 2016; Mercader et al., 2017). However, these conclusions were solely based on fish
9 abundance (Bouchoucha et al., 2016; Mercader et al., 2017) or post-settlement growth and condition
10 (Bouchoucha et al., 2018) in ports when, in theory, fish nursery grounds are defined according to three
11 conditions: they support high abundances of juveniles, they sustain faster somatic growth rates and
12 they also enhance fish survival so their populations contribute more to the final adult stock (Beck et
13 al., 2001). Therefore, the correct estimation of the nursery role played by ports implies estimating the
14 relative contribution of port juvenile habitats to adult stocks.

15 Among the tools available for this purpose, otolith microchemistry is increasingly used (Campana et
16 al., 2000; Elsdon et al., 2008; Dierking et al., 2012; Fortunato et al., 2017). Otoliths are paired
17 calcified structures located in the inner ear of teleost fishes. They have been widely used in marine
18 ecology and fishery research to describe population age structure, assess connectivity between fish
19 stocks and study individual migration patterns (Campana, 1999; Dierking et al., 2012). Otoliths are
20 particularly well suited for these applications because of three key properties. Firstly, they grow
21 continuously throughout daily centrifugal accretions of calcium carbonate (CaCO3) aragonitic crystals
22 on protein layers (Campana and Neilson, 1985). Secondly, as otoliths are acellular and metabolically
23 inert, they are neither reworked nor resorbed, even during times of starvation (Campana and Neilson,
24 1985). Chemical fingerprints are thus retained permanently within the microstructure (Panfili, 2002).
25 Thirdly, the incorporation of some chemical elements in the otolith is somehow influenced by the
26 ambient water (Elsdon and Gillanders, 2003; Sturrock et al., 2012). The chemical composition of the
27 inner region within adult fish otoliths can thus be used as a natural tag to retrospectively assign their
28 juvenile origin (e.g. Gillanders and Kingsford, 2000; Vasconcelos et al., 2008; Tournois et al., 2017).

29 To achieve this, the habitats must differ sufficiently in their environmental conditions, whether
30 because of varied anthropogenic influences or due to natural variations (Barnes and Gillanders, 2013;
31 Sturrock et al., 2015). As the recipients of industrial and domestic wastes, ports are generally subject
32 to high chemical contaminations (Darbra et al., 2004) and seem particularly suited for studies using
elemental fingerprints in otoliths as natural tags. However, the incorporation of elements in otoliths is more complex than their mere concentration in the ambient water as it is affected by their bioavailability, the physiological state of individual fish (Sturrock et al., 2015) and the synergetic/concomitant effects of temperature, salinity and water chemistry (Elsdon and Gillanders, 2004). While some elements (e.g., Sr, Ba) appear to directly reflect ambient water concentrations, others (e.g. Cu, Zn) are physiologically regulated and less likely to reflect surrounding environmental conditions (Campana, 1999; Sturrock et al., 2012; Izzo et al., 2018). Moreover, some elements (e.g. Cu, Pb) can affect fish condition and metabolic rates leading to variations in their incorporation in otoliths (Geffen et al., 1998; Hamer and Jenkins, 2007).

In this context, the specific aim of this study was to investigate differences in otolith composition among fish juveniles captured after several months of residency in varied habitats located inside and outside ports. Using two *Diplodus* species with different recruitment periods and five contrasting sites (three inside ports and two outside them, in adjacent juvenile habitats) located in the Bay of Toulon (Northwestern Mediterranean), we tested whether otolith elemental fingerprints could be used with confidence to retrospectively identify previous fish residency in ports. This would allow the reliable assessment of the contribution of ports to adult fish populations and thus better evaluation of the potential consequences of port infrastructures for fish stock dynamics. The hypothesis tested here was that the otoliths of fish captured inside ports would contain consistently higher concentrations of port related trace elements (such as Pb, Cu, Zn, etc.) than those of fish from the other coastal areas.

## 52 Materials and Methods

### 53 Model species

Two *Diplodus* species, *D. sargus sargus* (Linnaeus, 1758), hereafter *D. sargus*, and *D. vulgaris* (Geoffroy Saint-Hilaire, 1817) were chosen for this study. Both are very common in the Mediterranean (Coll et al., 2004; Morales-Nin et al., 2005; Lloret et al., 2008) and their juveniles are found in high abundances inside ports (Clynick, 2006; Bouchoucha et al., 2016). However there is temporal segregation between settlement periods of the two species (García-Rubies and Macpherson, 1995; Harmelin-Vivien et al., 1995; Vigliola et al., 1998; Cheminee et al., 2011; Ventura et al., 2014; Bouchoucha et al., 2016): *D. sargus* generally settles in one pulse, in May-June, and usually leaves its nursery grounds in September while *D. vulgaris* generally settles in two pulses, in November-December and in January-February, the first pulse being predominant, and leaves its nursery grounds in June-July. However, in the both species, spatial variability in settlement period may occur (Vigliola et al., 1998). Moreover, migrations between nursery areas is very limited for both species (Macpherson et al., 1997). Therefore, investigating variations in otolith chemical composition in these two species should allow reaching conclusions regarding the temporal stability of the otolith elemental fingerprints if any.
68 Study area, fish sampling and environmental data

69 This study was carried out in the Bay of Toulon (Northwestern Mediterranean, Fig. 1) which is divided into the Large Bay (42.2 km²) and the Small Bay (9.8 km²), separated by a breakwater (1,200 m) built in the nineteenth century (Fig.1). The Small Bay harbors one of France's largest industrial ports, the biggest naval port of the Mediterranean, and several marinas. Both the historic and present activities pursued in this part of the bay result in the heavy multi-contamination of its sedimentary compartment (Tessier et al., 2011; Pougnet et al., 2014; Dang et al., 2015) and water column (Jean et al., 2012; Dang et al., 2015). For example, sediment concentrations of Pb and Cu within the naval port of Toulon are among the highest ever measured in a marine area (Tessier et al., 2011). Conversely, the Large Bay is only slightly impacted by human activities and offers shallow rocky areas whose characteristics correspond to suitable nursery areas for many juvenile fishes (Harmelin-Vivien et al., 1995; Cheminee et al., 2011). Contamination levels in this part of the bay are low (Tessier et al., 2011). Therefore, the Bay of Toulon is particularly suited for investigating differences in fish otolith composition between juvenile habitats located within and outside ports.

82 In this work, five contrasting sampling sites were selected in the Bay of Toulon: three representative of the different types of port present in the Bay of Toulon and two representative of the other types of coastal habitats available for the juveniles of local rocky fishes. The first sampling site (STM) was located in the Saint-Mandrier marina. This marina, representative of the marinas found in the Small Bay of Toulon, harbors 800 boats and pleasure craft and its average depth varies between 2.5 and 4 m. The second site (TLN) was located about 500 m from the port of Toulon, which is the biggest naval port in the Mediterranean (30 warships and nuclear attack submarines) and a major ferry terminal (around 1,000 rotations and 1.2 million passengers a year). The area around the TLN sampling site is one of the most contaminated of the Bay of Toulon (Tessier et al., 2011; Wafo et al., 2016). The third site (IFR) was positioned further in the Small Bay (fig. 1), within a port harboring a part of the French oceanographic fleet. The fourth sampling site (DLE) was located on the seaward side of the breakwater that separates the Small from the Large Bay (Fig. 1), in an area considered as slightly impacted by port pollution (Tessier et al., 2011; Wafo et al., 2016). As the seaward sides of peripheral breakwaters are generally considered favorable juvenile fish habitats (Ruitton et al., 2000; Guidetti, 2004; Clynick, 2006; Pizzolon et al., 2008; Dufour et al., 2009; Pastor et al., 2013), the DLE site was considered in this study as representative of the non-polluted artificial juvenile habitats available for rocky fishes within the Bay of Toulon. Physical habitat characteristics (substrate, depth, etc.) are equivalent in the STM, TLN, IFR and DLE sampling sites. The last sampling site (MAG) was located in a natural cove (Anse Magaud) whose characteristics match those of the successful benthic settlement of *Diplodus* species, i.e. shallow water habitats (between 0 and 2 m) protected from prevailing winds and characterized by gentle slopes covered with sand, pebbles and boulders
103 (Harmelin-Vivien et al., 1995; Cheminee et al., 2011). Therefore, this site is considered as representative of the natural rocky fish nursery habitats available in the Bay of Toulon.

105 Sampling took place in September 2014 for *D. sargus* and late June / early July 2015 for *D. vulgaris*, i.e. during the month preceding the departure of the juveniles of each species from nursery their areas to join adult populations. Between 9 and 14 juveniles per species were sampled from each site using hand-nets. Juveniles were discriminated following the size and morphometric criteria given by Vigliola and Harmelin-Vivien (2001).

110 Trace element concentrations in the water column were assessed at each sampling site using the diffusive gradient in thin film (DGT) device which allows gathering integrative values of the most labile dissolved concentrations of metal species in water (Davison and Zhang, 1994). By accumulating ionic forms and weakly dissociable complexes (hereafter named the DGT-labile fraction) on a Chelex 100 resin, proportionally to their water concentration and exposure time, "Standard" DGTs "concentrate" the most highly "labile" dissolved cations, as defined operationally (hydrated ions, mineral complexes, and "weak" or "rapidly dissociable" organic complexes). This DGT- labile fraction is generally considered more representative of potentially bioavailable fraction than the total metal concentration (Zhang and Davison, 2001; Odzak et al., 2002; Twiss and Moffett, 2002). In our study, three DGTs were immersed at 1.5m depth for 15 days each month, from January to July 2015, at each of the five sampling sites. Three DGTs were selected as controls and not immersed in the seawater. During this period, temperature and salinity data were also recorded monthly at each site using a CTD probe (YSI Pro30).

123 Otolith analyses

124 Fish juveniles were transported on ice to the laboratory, measured (Total length TL, in mm), weighed (total mass, in mg) and stored at -20°C until otolith extraction. In accordance with Campana et al. (2000), all the materials used for otolith extraction and handling were decontaminated in 4% ultrapure nitric acid baths, triple rinsed with ultrapure water (18.2 MΩ) and dried on a Class 100 clean bench. *Diplodus* sagital otoliths were extracted using plastic forceps under binocular lens, rinsed with ultrapure water and cleaned of adhering tissues. They were then sonicated for 5 min in ultrapure water, triple rinsed again and dried on the same laminar clean bench.

131 Right otoliths from all fish were embedded separately in epoxy resin (Araldite 2020, Escil) and cut in the transverse plane using a saw with a diamond coated blade (Buehler IsoMet 1000 precision saw). Individual otolith sections were then polished using 1200, 2400 and 4000 silicate papers to expose the core (average thickness ≈ 300 µm). A final sonication was carried out for surface decontamination before storage in dust-free conditions until analysis.

136 In addition to <sup>43</sup>Ca, otolith elemental compositions of nine elements (<sup>24</sup>Mg, <sup>51</sup>V, <sup>55</sup>Mn, <sup>63</sup>Cu, <sup>66</sup>Zn, <sup>88</sup>Sr, <sup>118</sup>Sn, <sup>138</sup>Ba and <sup>208</sup>Pb) were assessed using a 257 nm high repetition rate femtosecond (fs) Laser
138 Ablation system (Lambda 3, Nexeya, France) coupled with a High Resolution Inductively Coupled

139 Plasma Mass Spectrometer (HR-ICPMS) fitted with a jet interface (Element XR, Thermo Bremen)

140 (fsLA-HRICPMS). The laser in this system delivers 360 fs pulses at a wave-length s of 257 nm and
141 can be operated at high repetition rates (up to 100 kHz). By combining the high repetition rate (500

142 Hz) of the laser with a fast back and forth movement (1 mm.s⁻¹) of its beam (diameter 20 µm, energy
143 45 µJ.pulse⁻¹), an elongated virtual beam shape (width 150 µm) was simulated at a speed of 5 µm.s⁻¹.

144 For each otolith analyzed, a linear raster of 150 µm length consisting of approx. 30 sequential
145 measures, was taken along the longest radius, starting at 200 µm after the settlement mark (Fig. 2).

146 This portion of the otolith corresponds approximately to the third month of fish juvenile life. This was
147 estimated through a microstrucural analysis and a daily increment counts performed on the left otolith
148 of each fish (Vigliola, 1997). For each otolith, the laser was used to preclean the otolith surface of
149 interest prior to ablation. To do this, the laser was operated at a lower repetition rate (100 Hz) and a
150 faster stage movement (400 µm.s⁻¹ instead of 5 µm.s⁻¹) in order to prevent excessive in-depth removal
151 (resulting ablation depth = 2-5 µm).

152 The ablation cell was flushed with Helium (700 ml.min⁻¹) to carry the particles to the ICPMS, and
153 argon (300 ml.min⁻¹) was mixed with the helium stream using a Y-piece to adjust the optimal particle
154 atomization conditions in the plasma. The plasma was also fed with nitrogen (10 ml.min⁻¹) to enhance
155 signal sensitivity.

156 As light elements are prone to interference from polyatomic and double charged isotopes, the mass
157 spectrometer was used in medium resolution mode (R = 4000) to optimize interference removal during
158 analysis. Calcium was used as an internal standard for each ablation to correct for instrumental error
159 (in terms of ablation yield and detection). The quantification of elements in the otoliths was achieved
160 by external calibration using three NIST glass standards (610, 612 and 614). Two otolith Certified
161 Reference Materials (NIES22 and FEBS 1 were also pelletized and used to control the quality of the
162 analysis of selected elements in the fish otoliths. The average limits of detection (LOD) over a three
163 month-session based on the threefold standard deviation of blank gas were 2.0, 0.8, 0.8, 4.2, 13.2, 1.6,
164 8.1, 0.3 and 0.6 µg.g⁻¹ in the otoliths for ²⁴Mg, ⁵¹V, ⁵⁵Mn, ⁶³Cu, ⁶⁶Zn, ⁸⁸Sr, ¹¹⁸Sn, ¹³⁸Ba and ²⁰⁸Pb,
165 respectively.

166 Water analyses

167 Following the deployment of the DGTs, the Chelex resin of the probes was peeled off in the laboratory
168 and trace elements were extracted in 1.8 ml of 1 M ultrapure nitric acid (24 – 48 h). Concentrations in
169 the acid extracts were analyzed for four trace elements (Pb, Mn, Zn and Cu) using ICP-MS to
170 determine the mass of metal (M) accumulated in the Chelex 100 resin. These elements were chosen
171 because they can be measured using DGT and are representative of port and human activities in the
172 Bay of Toulon (Tessier et al., 2011).
173 For each trace element, the average labile concentrations in water (CDGT) were calculated using the
174 following equation (Zhang et al., 1995):

$$
175 \qquad \mathrm{CDGT} = (\mathrm{M} \cdot \Delta\mathrm{g}) / (\mathrm{D}\mathrm{m} \cdot \mathrm{t} \cdot \mathrm{A})
$$

176 where M is the mass of the trace element accumulated in the resin, Δg is the diffusive layer thickness,
177 Dm is the diffusion coefficient of the trace element provided by DGT Research
178 (www.dgtresearch.com), t is the immersion of the DGT probe, and A is the exposure area.

179 Statistical analyses

180 Otolith concentrations for all elements were transformed to their ratio to Calcium (element:Ca ratio)
181 and checked for normality and homoscedasticity prior to statistical analyses. Since none of the
182 assumptions were satisfied, even after transformation, Mann-Whitney tests and Kruskall-Wallis tests
183 followed by Steel-Dwass-Chritchlow-Fligner post-hoc tests were used. For each species spatial
184 variation in elemental ratios were analyzed according to habitat type (inside or outside a port) and the
185 sampling site, respectively. Spatial variation in the elemental composition of water was also tested
186 using Kruskall-Wallis tests followed by Steel-Dwass-Chritchlow-Fligner post-hoc tests.

187 In accordance with Mercier et al. (2011), accuracy in habitat type and sampling site identification
188 using otolith elemental fingerprints were investigated for each species using the Random Forest (RF)
189 algorithm (Breiman, 2001). RF is a machine-learning classification method which requires no a priori
190 assumptions regarding data distribution and can extract signals from complex datasets. Therefore it is
191 very effective for discriminating local coastal habitats based on otolith elemental fingerprints (e.g.
192 Mercier et al., 2012; Regnier et al., 2017; Tournois et al., 2017) even in the presence of skewness
193 (Mercier et al., 2011; Jones et al., 2017). Details of the method are provided in Mercier et al. (2011)
194 and Tournois et al. (2013). For each of the individual measures performed on the otoliths, the RF
195 predicts habitat origin by running it through 5000 trees of a classifier (built from known signatures
196 from all habitats) and then using “majority rules” to reach a consensus between all the trees (Tournois
197 et al., 2017). In our case, to identify the list of elements needed to reach maximum accuracy in habitat
198 discrimination, a portion of the dataset (75%) was randomly selected to build the RF classifier and the
199 remaining portion (25%) was used to calculate the global classification accuracy and the percentage of
200 correct re-assignments for each location (cross validation procedure). This cross-validation procedure
201 was applied to all the possible combinations of two to nine of the nine elements measured in the
202 otoliths (Ca was used as an internal standard).

203 Once the optimal RF classifier had been identified, values for True Skill Statistics per habitat (TSS,
204 Allouche et al., 2006) were calculated to evaluate the successful discrimination for each location.
205 Contrary to habitat accuracy, TSS accounts for true negative prediction, namely for fish whose
206 absence from a given site was correctly predicted. TSS ranges from -1 to 1, where 1 indicates 100%
207 correct prediction of presence or absence in a given habitat (Tournois et al., 2017). Finally, the
contribution of each element to the global success of discrimination was evaluated using the mean decrease in the Gini index (Archer and Kimes, 2008): the higher it is, the more discriminative the element (Tournois et al., 2013). All data processing and statistical analyses were performed using the R software (R Core Team, 2014) and PRIMER 6 software with the PERMANOVA add-on (Clarke and Warwick, 2001).

## 213 Results

Water concentrations in Cu and Pb differed significantly (p < 0.0001) according to the sampling sites (Table 1), with consistently lower values at MAG and higher ones in ports. However, water concentrations in Cu were maximal at STM (2 971 ± 997 ng.l⁻¹), while for Pb, the highest values were recorded at TLN (328 ± 107 ng.l⁻¹). No significant differences (p ≥ 0.06) in Mn and Zn concentrations in water were observed among the sites (Table 1).

Spatial differences in otolith elemental concentrations were not consistent for all elements and between species (Fig.3). Ba, Mn and Sr values were always significantly higher (p < 0.001) outside ports while for Mg and Sn otolith concentrations were consistently higher inside them (p < 0.018). For Cu, the values in ports were significantly higher for *D. sargus* (p < 0.001) but lower for *D. vulgaris* (p < 0.0001). For Pb, no significant variation (p > 0.063) was observed between the two habitat types (inside or outside ports), irrespective of species. Finally, for Zn, variations were not significant between habitat types for *D. sargus* (p = 1) whereas, for *D. vulgaris* the values were higher outside ports (p < 0.001). This pattern was inversed for V.

For both species, discrimination accuracy using RF was always maximal when including eight elements out of nine, V and Sr apparently contributing more noise than signal to the discrimination for *D. sargus* and *D. vulgaris*, respectively (Fig. 4).

The global discrimination accuracy between ports and adjacent juvenile habitats reached 94% with TSS > 0.87 irrespective of species (Fig. 4, Table 2). However, the list of elements included in the RF classifier differed between the two species, the three most useful elements being Ba, Mn and Sn for *D. sargus* and Ba, Zn and Mn for *D. vulgaris* (Fig. 4).

When considering the five sampling sites separately, using the optimal RF classifier for each species allowed reaching high overall accuracy for both *D. sargus* (90%) and *D. vulgaris* (89%). Yet again, the most useful elements differed between species, the three most discriminating elements being Ba, Sn and Mn for *D. sargus* and Ba, Sn and Cu for *D. vulgaris* (Fig. 4). For both species, misclassification errors mostly concerned the STM sampling site (TSS < 0.84) which was to some extent confused with the MAG, DLE and IFR sites due to similar values for Mn, Sr, Cu, Mg, Sn and Zn (Table 2, Fig. 3).

## 241 Discussion
As expected from similar studies on other coastal habitats in the Mediterranean (e.g. Di Franco et al., 2011; Fortunato et al., 2017) and elsewhere (e.g. Hamer et al., 2005; Correia et al., 2011), our study showed differences in otolith elemental composition between the five sites sampled. This is not particularly surprising as the sampling sites in this work are highly contrasted in terms of contaminant concentrations (Fig. 3), which are known to influence fish otolith composition (Campana, 1999; Sturrock et al., 2012).

In our study area, otolith signatures inside ports differed markedly from those outside them (especially for Ba and Mn), which should allow accurate the identification of port origin in local adult *D. sargus* and *D. vulgaris*. For both species, the global classification accuracy obtained between habitat types (inside ports vs. outside) was even higher (94%) than those found in other coastal systems (Gillanders, 2005; Vasconcelos et al., 2008; Tanner et al., 2011; Tournois et al., 2013). However, considering all five sampling sites separately also led to very high global classification accuracies (≥ 89%) and correct re-assignment rates for all of them (≥76%). Therefore, although port habitats could be confidently identified here for both species, the variability of elemental fingerprints was high among port sites and among adjacent juvenile habitats. This variability has already been pointed out in natural coastal areas and is largely exploited in studies of the dispersal/connectivity of fully marine fishes (Di Franco et al., 2011).

However, this work also showed that otolith elemental concentrations in ports do not consistently reflect their high concentrations in the environment. The relationship between environmental exposure and otolith final concentrations was even inversed for some trace elements. For example, in *D. sargus*, otoliths from MAG were 1.4-fold more contaminated in Cu than those from STM (Fig. 4) whereas Cu concentration was found to be 34-fold higher in the water of MAG (Fig. 3). Similarly, in *D. vulgaris*, otolith concentrations in Pb were significantly higher in DLE than in TLN (1.3-fold, p = 0.017) whereas this element was 2.6-fold more concentrated in the water at TLN than at DLE (Fig. 3, Fig. 4). Therefore, although the three ports studied were more contaminated than adjacent juvenile habitats, in particular for Pb and Cu (Fig. 3), we failed to find at least one trace element for which otolith concentrations would be consistently higher in ports. Up to now few studies have focused on the link between the accumulation of elements in fish otoliths and their bioavailable concentrations in the aquatic environment (e.g. Geffen et al., 1998; Ranaldi and Gagnon, 2009; Daverat et al., 2012), yet those that have done so generally suggested that for many trace elements stored in fish otoliths, in particular Cu and Pb (Friedrich and Halden, 2010, 2011; Søndergaard et al., 2015), otolith concentrations largely reflect concentrations in water. This led some authors to conclude that trace element analyses in otoliths could be considered a valuable method for assessing time-resolved trace element exposure due to anthropogenic pollution (Søndergaard et al., 2015; Selleslagh et al., 2016). Several hypotheses can be proposed to explain the inconsistency between our results and these conclusions. Firstly, due to differences in metabolism and otolith formation, trace element
accumulation in otoliths differs among species (Geffen et al., 1998; Hamer and Jenkins, 2007; Vasconcelos et al., 2007). Therefore, it is possible that otolith trace element incorporation is lower in *D. sargus* and *D. vulgaris* juveniles than in the sand goby *Pomatoschistus minutus* (Geffen et al., 1998), black bream *Acanthopagrus butcheri* (Ranaldi and Gagnon, 2008), flounder *Platichthus flesus* (Selleslagh et al., 2016) and common sculpin *Myoxocephalus scorpius* (Søndergaard et al., 2015), which were all suggested to be adequate for monitoring pollution from point sources. This hypothesis is supported by the fact that lower otolith accumulation rates have already been pointed out for certain trace elements in *Diplodus vulgaris*, when compared to other species like sole and flounder (Vasconcelos et al., 2007). Secondly, the accumulation of trace elements in fish otoliths not only depends on several factors, including their concentration in the environment and their bioavailability, but also on the physiological state of the fish (affecting the exchange rate between the external and internal environments), and on the mechanisms they use for detoxifying different metals or controlling their growth rate (affecting the rate of accumulation of otolith material). Although juvenile growth for both species is equivalent inside and outside the ports of this area (Bouchoucha et al., 2018), the consequences of juvenile life in ports on their individual physiological state still remain unclear. The influence of anthropogenic stressors on fish metabolism may explain differences in otolith trace element incorporation between the two types of habitat. However, further research is needed to investigate the respective influences of element concentrations in water and spatial variations in fish metabolism on their accumulation in the otoliths of juvenile *Diplodus*. Nonetheless, we have to reject our initial hypothesis: the otoliths of the juvenile *Diplodus* captured inside ports, even at the most polluted sites, did not contain higher concentrations of port-related trace elements (such as Pb, Cu, Zn, etc.) than those from the juveniles captured in less polluted coastal areas. Therefore, otolith concentrations in these elements cannot be used to retrospectively identify the past residency of adult fish in ports, at least for the two species studied.

For both species, Ba was the most discriminating element among our sampling sites and between the two types of habitat, as its concentration in the otoliths, with one exception, was consistently higher outside ports than inside them (Fig. 4). This result is surprising as Ba is routinely used to distinguish residence in brackish habitats (reviewed by Elsdon et al., 2008), as its incorporation in otoliths falls when salinity increases (Bath et al., 2000; Elsdon and Gillanders, 2004). Nonetheless, the three port sites investigated here had lower salinities than the two others (Table 1). However, Ba concentrations in otoliths can be modulated by endogenous factors (e.g. fish condition - Izzo et al., 2015), and generally reflect ambient environmental concentrations (Campana, 1999; Elsdon et al., 2008). The DLE and MAG sites are located in the Large Bay of Toulon, close to the two outlets of the Eygoutier river (Fig. 1). As Ba is closely bound to fluvial sediments (Coffey et al., 1997), ambient Ba concentrations may be higher in DLE and MAG than in the three other sampling sites, explaining differences in juvenile fish otolith compositions. Therefore, it is likely that the pattern observed here
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with regard to otolith Ba concentrations is particular to the Bay of Toulon and cannot be extrapolated to other ports and coastal areas.

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Finally, the trace elements responsible for the discrimination among the five sampling sites differed according to species, which might suggest that otolith fingerprints should be determined for each species independently and cannot be extrapolated from one species to another. The differences in otolith environmental records between our two species may be due to endogenous processes like physiological regulation (Sturrock et al., 2015) as well as to exogenous ones. Indeed, in the Northwestern Mediterranean, *D. vulgaris* generally hatches in two pulses, in October-November and in December-January, whereas *D. sargus* generally hatches only in May-June (Vigliola et al., 1998).
Since we analyzed otolith portions corresponding approximately to the third month of life for both species, the environmental signals recorded by the two species do not overlap: for *D. vulgaris*, the otolith fingerprints correspond to winter/spring conditions while for *D. sargus*, they correspond to summer ones. Temporal variability in the elemental fingerprints must therefore be evaluated before concluding on the differences in environmental recording capacities among species.

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Acknowledgements

This work was conducted within the framework of the RESPONSE Project funded by the 'Agence de l'Eau Rhône Mediterranée et Corse' and the Ifremer. We are grateful to G. Barbotin for her assistance for the otolith analyses. Thanks are also due to F. Chavanon and A. Vion for their help during the laboratory work.

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Figure 1: Map of the Bay of Toulon (Northwestern Mediterranean) showing the location of the five sites sampled for this study. The sites located inside ports are represented by black circles while those located outside them are represented by white circles.

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<figure><img src="image_2.png" /></figure>

Figure 2: Cross section through a sagittal otolith of juvenile *D. sargus*. The 150 x 150µm ablation area, represented by the grey square (a), is positioned 200µm after the settlement mark.
<figure><img src="image_3.png" /></figure>

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ACCEPTED MANUSCRIPT
Figure 3: Mean otolith element:Ca ratios (± SE) measured for Ba, Mn, Sr, Cu, Pb, V, Mg, Sn and Zn in the juveniles of *D. sargus* and *D. vulgaris* from the two habitat types (In, inside ports, Out, outside ports) and the five sampling sites (M, MAG; D, DLE; S, STM; T, TLN; I, IFR). For each species, letters indicate significant differences (p < 0.05) between habitat types or sampling sites (see M&M for details).
<figure><img src="image_4.png" /></figure>

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

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

D. sargus

D. vulgaris

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

D. sargus

D. vulgaris

<figure><img src="image_10.png" /></figure>

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D. sargus

D. vulgaris
Figure 4: Effect of elemental combination size and composition on the discrimination accuracy obtained using the RF method: (a) between the two habitat types for *D. sargus*, (b) among the five sampling sites for *D. sargus*, (c) between the two habitat types for *D. vulgaris* and (d) among the five sampling sites for *D. vulgaris* (d). The grey area on the graph represents the range between the minimal and maximal accuracies reached for each combination size while the corresponding average accuracy (±SD) is represented by the black square. The table gives the list of elements that achieved maximal discrimination with the corresponding mean decreases in the Gini index (see text for details).
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(a)

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(b)

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(c)

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(d)

Combination size
<table><caption>Table 1: Concentrations in trace elements in the water column for the five sampling sites measured with DGT. Letters indicate significant differences (p<0.05) between sites if any</caption><thead><tr><th rowspan="2">site</th><th colspan="2">Temp. (°C)</th><th colspan="2">Salinity (psu)</th><th colspan="2">Cu (ng.l<sup>-1</sup>)</th><th colspan="2">Mn (µg.l<sup>-1</sup>)</th><th colspan="2">Pb (ng.l<sup>-1</sup>)</th><th colspan="2">Zn (µg.l<sup>-1</sup>)</th></tr><tr><th>Mean ± sd</th><th>Min-Max</th><th>Mean ± sd</th><th>Min-Max</th><th>Mean ± sd</th><th>Min-Max</th><th>Mean ± sd</th><th>Min-Max</th><th>Mean ± sd</th><th>Min-Max</th><th>Mean ± sd</th><th>Min-Max</th></tr></thead><tbody><tr><td>TLN</td><td>17.5 ± 4.3</td><td>12.8 - 24.8</td><td>37.1 ± 1.0</td><td>35.1 - 38.8</td><td>1268 ± 572 <sup>(a,b)</sup></td><td>842 - 2203</td><td>0.83 ± 0.49 <sup>(a)</sup></td><td>0.43 - 1.62</td><td>328 ± 107 <sup>(a)</sup></td><td>206 - 540</td><td>3.05 ± 1.67 <sup>(a)</sup></td><td>1.52 - 6.03</td></tr><tr><td>IFR</td><td>17.6 ± 4.8</td><td>12.0 - 25.7</td><td>37.2 ± 1.9</td><td>32.2 - 38.7</td><td>879 ± 261 <sup>(b)</sup></td><td>589 - 1205</td><td>1.28 ± 0.59 <sup>(a)</sup></td><td>0.76 - 2.22</td><td>203 ± 85 <sup>(a,b)</sup></td><td>132 - 337</td><td>3.44 ± 1.42 <sup>(a)</sup></td><td>1.72 - 5.36</td></tr><tr><td>STM</td><td>17.7 ± 4.9</td><td>12.0 - 25.4</td><td>37.8 ± 1.3</td><td>34.4 - 39.5</td><td>2972 ± 997 <sup>(a)</sup></td><td>1593 - 4375</td><td>0.67 ± 0.20 <sup>(a)</sup></td><td>0.48 - 0.90</td><td>222 ± 47 <sup>(a)</sup></td><td>153 - 261</td><td>3.27 ± 0.78 <sup>(a)</sup></td><td>2.32 - 4.30</td></tr><tr><td>DLE</td><td>17.1 ± 3.9</td><td>12.6 - 23.6</td><td>38.0 ± 0.8</td><td>35.8 - 39.0</td><td>257 ± 166 <sup>(c)</sup></td><td>142 - 498</td><td>1.52 ± 1.18 <sup>(a)</sup></td><td>0.57 - 3.12</td><td>86 ± 44 <sup>(b,c)</sup></td><td>52 - 145</td><td>1.47 ± 1.26 <sup>(a)</sup></td><td>0.50 - 3.17</td></tr><tr><td>MAG</td><td>17.4 ± 4.1</td><td>13.2 - 25.5</td><td>38.0 ± 0.5</td><td>37.2 - 39.1</td><td>87 ± 38 <sup>(c)</sup></td><td>39 - 145</td><td>0.50 ± 0.16 <sup>(a)</sup></td><td>0.40 - 0.78</td><td>47 ± 10 <sup>(c)</sup></td><td>36 - 61</td><td>1.31 ± 0.96 <sup>(a)</sup></td><td>0.26 - 2.79</td></tr></tbody></table>
Table 2: Classification accuracy and True Skill Statistics (TSS) per habitat type or per sampling site reached for *D. sargus* and *D. vulgaris* with RF using the optimal elemental combination.
<table><thead><tr><th></th><th>Inside ports</th><th>TLN</th><th>IFR</th><th>STM</th><th>Outside ports</th><th>DLE</th><th>MAG</th></tr></thead><tbody><tr><td colspan="8"><strong>a) D. sargus</strong></td></tr><tr><td><strong>Inside ports</strong></td><td><strong>95.3</strong></td><td></td><td></td><td></td><td><strong>6.7</strong></td><td></td><td></td></tr><tr><td>TLN</td><td></td><td>88.1</td><td>2.6</td><td>4.4</td><td></td><td>0.6</td><td>0.8</td></tr><tr><td>IFR</td><td></td><td>6.7</td><td>88.7</td><td>6.3</td><td></td><td>1.1</td><td>0.9</td></tr><tr><td>STM</td><td></td><td>1.1</td><td>2.4</td><td>76.1</td><td></td><td>0.8</td><td>0.7</td></tr><tr><td><strong>Outside Ports</strong></td><td><strong>4.7</strong></td><td></td><td></td><td></td><td><strong>93.3</strong></td><td></td><td></td></tr><tr><td>DLE</td><td></td><td>1.4</td><td>3.2</td><td>6.1</td><td></td><td>97.2</td><td>0.3</td></tr><tr><td>MAG</td><td></td><td>2.7</td><td>3</td><td>7.2</td><td></td><td>0.3</td><td>97.3</td></tr><tr><td><strong>TSS</strong></td><td><strong>0.89</strong></td><td>0.86</td><td>0.85</td><td>0.75</td><td><strong>0.89</strong></td><td>0.95</td><td>0.94</td></tr><tr><td colspan="8"><strong>b) D. vulgaris</strong></td></tr><tr><td><strong>Inside ports</strong></td><td><strong>95.3</strong></td><td></td><td></td><td></td><td><strong>8.6</strong></td><td></td><td></td></tr><tr><td>TLN</td><td></td><td>91.7</td><td>5.7</td><td>0.5</td><td></td><td>1.0</td><td>1.3</td></tr><tr><td>IFR</td><td></td><td>4.3</td><td>88.8</td><td>3.0</td><td></td><td>1.5</td><td>1.4</td></tr><tr><td>STM</td><td></td><td>0.4</td><td>1.7</td><td>85.9</td><td></td><td>4.6</td><td>1.4</td></tr><tr><td><strong>Outside ports</strong></td><td><strong>4.7</strong></td><td></td><td></td><td></td><td><strong>91.4</strong></td><td></td><td></td></tr><tr><td>DLE</td><td></td><td>1.7</td><td>2.1</td><td>4.3</td><td></td><td>86.3</td><td>2.8</td></tr><tr><td>MAG</td><td></td><td>1.8</td><td>1.8</td><td>6.4</td><td></td><td>6.5</td><td>93.1</td></tr><tr><td><strong>TSS</strong></td><td><strong>0.87</strong></td><td>0.90</td><td>0.86</td><td>0.84</td><td><strong>0.87</strong></td><td>0.84</td><td>0.89</td></tr></tbody></table>