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Article

Species-Specific Element Accumulation in Mollusc Shells: A Framework for Trace Element-Based Marine Environmental Biomonitoring

by
Sergey V. Kapranov
1,
Larisa L. Kapranova
1,
Elena V. Gureeva
1,
Vitaliy I. Ryabushko
1,
Juliya D. Dikareva
1 and
Sophia Barinova
2,*
1
A.O. Kovalevsky Institute of Biology of the Southern Seas of RAS, 2 Nakhimov Ave., Sevastopol 299011, Russia
2
Institute of Evolution, University of Haifa, 199 Abba Khoushi Ave., Mount Carmel, Haifa 498838, Israel
*
Author to whom correspondence should be addressed.
Water 2025, 17(16), 2407; https://doi.org/10.3390/w17162407
Submission received: 9 July 2025 / Revised: 11 August 2025 / Accepted: 13 August 2025 / Published: 14 August 2025

Abstract

Mollusc shells serve as valuable biogeochemical archives of natural or anthropogenic processes occurring in the aquatic environment throughout the life of the molluscs. One such process is trace element pollution, which can be assessed by analyzing the elemental composition of mollusc shells. However, different mollusc species accumulate elements in their shells from the aquatic environment at varying concentrations, and specific patterns of this accumulation remain largely unknown. In the present study, we measured the concentrations of 33 elements in the shells of five commercially important Black Sea molluscs, all collected from the same site, using inductively coupled plasma mass spectrometry. The species were ranked according to the number of elements with the highest concentrations in their shells as follows: Crassostrea gigas (9) = Rapana venosa (9) = Anadara kagoshimensis (9) > Flexopecten glaber ponticus (4) > Mytilus galloprovincialis (2). Cluster analysis of Pearson’s coefficients of correlation of elemental concentrations in the molluscan shells revealed significant separation of C. gigas, F. glaber ponticus, and M. galloprovincialis. Multivariate ordination analyses allowed the accurate classification of >92.3% of shell samples using as few as four elements (Fe, As, Sr, and I). Linear discriminant analysis revealed the probability of separation of all species based on the concentrations of these elements in their shells being not lower than 79%. The applied multivariate approach based on the analysis of four base elements in shells can help not only in the taxonomic identification of molluscs, but also, upon appropriate calibration, in monitoring medium-term dynamics of trace elements in the aquatic environment.

1. Introduction

A distinctive characteristic of many molluscs is the presence of shells, which vary in mineral composition among different species. These shells are the focus of research in food chemistry, biotechnology, materials science, and biomineralization studies [1,2,3,4,5,6,7]. Extracts from mollusc shells have antibacterial [8,9], antitumor, anti-inflammatory and immunomodulatory properties [10]. Anadara spp., mussel and oyster shells are used in medicine to increase testosterone levels [11] and for bone tissue engineering [12,13].
The weight of shells of bivalves and gastropods, depending on the species, can be up to 90% of the total weight, while in the shellfish food industry, shells are considered an unwanted by-product [1,14]. Over the past decades, many studies have been published on valorization of mollusc shells with a focus on their utilization and potential use in various economically profitable industries, including agriculture [15,16,17,18,19,20] and aquatic environmental science [1,4,6,14,21,22,23]. Being capable of bioaccumulating elements, mollusc shells enable recording medium- and long-term changes in the environment (typically, on the weekly to decadal scale [24]), can be used as alternative biomaterials for heavy metal sorption and wastewater treatment, and also provide the possibility of determining the mollusc provenance [14,25,26,27,28]. The use of shells as indicators for monitoring metal pollution in seawater has a number of advantages over soft tissues in this role; for example, ease and longevity of storage as well as sensitivity to long-term exposure to metals in the environment [29,30,31,32,33]. In addition, the fluctuations in the values of the elemental concentrations in shells are significantly lower than in soft tissues, and the levels of metals are conserved after the death of the organism [33,34].
Element character type and accumulated element concentrations in the carbonate shells of marine invertebrates are the integrated archives of environmental conditions, including chemical composition of seawater [35,36]. Methods of sclerochronology, which describes the gradual growth of the shell over time, allow estimating the concentrations of elements in a certain period of time in the past [37]. It should be taken into account that the element concentrations can differ in different layers of the shell. For example, the outer surface of the shell can adsorb metals from the environment, and the concentrations of metals in this layer may be higher than in soft tissues and the inner and central layers of the shell [37,38,39]. The metal adsorption was found to be mainly determined by the integrity of the shell’s outer surface rather than by its chemical and mineral composition [37].
The main sources of trace elements in mollusc shells are water-dissolved phases and the dietary intake [40,41]. At the same time, their bioavailability depends on a combination of environmental (salinity, pH and sediment type) and chemical factors (binding strength and shell composition) [42]. The mineralogy and chemistry of shells are related to both environmental conditions and the physiology of organisms, as even closely related species within the same habitat may exhibit different levels of accumulation [42,43]. For instance, due to the loose crystal structure of aragonite, calcium in this mineral is preferentially substituted for larger cations such as Sr, while in calcite, smaller cations such as Mg are energetically favored [42]. As a result of the influence of diverse factors on the chemical composition of molluscan shells and soft tissues, elemental analysis coupled with multivariate techniques can be successfully used for the trace element fingerprinting to identify the provenance (area of collection) [27,44,45,46,47,48,49,50,51], date of collection [52,53], class or order of the mollusc [26,54,55,56], its age [50] or specific organ [57]. At the same time, no attempts have been made to single out a set of a minimum possible number of elements that would unambiguously characterize different groups, as concentrations of many elements obtained in the analysis are not meaningful as independent variables, being significantly correlated to the others or presenting indistinguishable distributions in all groups.
High concentrations of various metals in sediments and marine organisms pose a significant environmental concern, particularly regarding the Black Sea, which is one of the most polluted bodies of water in the world, especially in terms of toxic metals [58,59]. Terrestrial pollutants primarily enter the Black Sea ecosystem via rivers as a result of industrial, shipping, agricultural, domestic and other activities on its coasts [60,61]. They pose a serious threat to human health due to their toxicity, bioaccumulation, biomagnification and long-term persistence in the food chain [61]. Gastropods are capable of accumulating almost all heavy metals to a considerable extent, and the low migratory activity of bivalves allows using them as bioindicators for areas with high anthropogenic impact [62,63,64].
To our knowledge, there are no reports on comparative analysis of concentrations of elements, including toxic heavy metals, in shells of Black Sea molluscs, and there are no studies on the element screening for the trace element fingerprinting in application to molluscs. For this study, we selected five molluscan species most commonly found in coastal areas of the northern Black Sea and of actual or potential commercial importance as seafood: the bivalves Anadara kagoshimensis, Crassostrea gigas, Flexopecten glaber ponticus, Mytilus galloprovincialis and the gastropod Rapana venosa. The purposes of this study are as follows: (1) to characterize the molluscs in terms of their ability to accumulate trace elements in their shells that can be used as bioindicators of element pollution in the marine environment; and (2) to develop a multivariate approach to differentiating between mollusc species based on the element signatures in their shells as a methodological basis for various classifications and aquatic environment biomonitoring studies in the future.

2. Materials and Methods

2.1. Sampling Area and Molluscs

Molluscs were collected from the same location at a shellfish farm established in the vicinity of Karantinnaya Bay in the coastal area of southwestern Crimea (northern Black Sea, 44°36′56.4″ N; 33°30′13.6″ E, Figure 1). They were sampled from the same biotope to eliminate the influence of spatial inhomogeneity of local element concentrations in the environment.
Four bivalves and one gastropod species were used in the study: the ark clam Anadara kagoshimensis (Tokunaga, 1906), shell length L = 30.5 ± 1.0 mm, n = 10; the smooth scallop Flexopecten glaber ponticus (Bucquoy, Dautzenberg & Dollfus, 1889), L = 38.8 ± 5.1 mm, n = 6; the Pacific oyster Crassostrea gigas (Thunberg, 1793), L = 90.7 ± 10.3 mm, n = 7; the Mediterranean mussel Mytilus galloprovincialis (Lamarck, 1819), L = 54.1 ± 2.9 mm, n = 6; and the gastropod—veined rapa whelk Rapana venosa (Valenciennes, 1846), also previously referred to as R. thomasiana Crosse, 1861, shell height 84.7 ± 4.8 mm, n = 11.
The bivalves were collected in October 2022 at a water temperature of 21.4 °C and a salinity of 18.39‰. The oysters, clams and scallops were collected from cages (the nylon collectors suspended in the water column at a depth of 2–3 m). The mussels were collected from the fouling on the submerged ropes of the shellfish farm. The molluscs were 2 years old (with their age counted from the time their larvae settled on the collector). The rapa whelks were sampled at a depth of 18 m on the seafloor under farm collectors by scuba diving (at a temperature of 19 °C).

2.2. Sample Preparation for the Analysis

Molluscs were transported to the laboratory in plastic containers filled with seawater within 1–2 h after being collected. The animals were first cleaned of epiphytes and epifauna and washed with seawater. Then, each bivalve individual was opened with a knife, and the gastropod shells were mechanically broken. The soft tissues were thoroughly removed using a plastic scalpel, and the shells were rinsed with deionized water and dried at 105 °C to constant weight.
The dried shells of each specimen were ground into powder in a porcelain mortar. A 100 mg amount of the resulting shell powder was transferred to the PTFE acid-digestion tubes, which had been pre-soaked in 10% nitric acid, rinsed with deionized water, and dried. For the digestion, analytical-grade nitric acid was purified by sub-boiling distillation in an acid purification system DST-1000 (Savillex, Eden Prairie, MN, USA). A 2 mL volume of the distilled nitric acid was added into each tube, and the tubes were closed with PTFE screw caps and left to stand overnight. Then, they were autoclaved at 120 °C for 2 h. The digested samples were subsequently diluted with deionized water to achieve a dilution factor of 1000 mL g−1 dry weight (d.w.).

2.3. ICP-MS Analysis

Elemental analysis was conducted using a single-quadrupole ICP-MS instrument PlasmaQuant MS Elite (Analytik Jena, Jena, Germany). The instrument underwent the preliminary internal procedures of resolution and trim and mass calibration. The instrument setup parameters used in the analysis were reported elsewhere [51]. A total of 5 scans per replicate and 5 replicates per sample were performed for each analytical sample.
Certified multielement standard IV-ICPMS-71A (Inorganic Ventures, Christiansburg, VA, USA) and a certified iodide standard GSO7620-99 (Lenreaktiv, Saint Petersburg, Russia) were diluted with deionized water to achieve analyte concentrations of 0.1, 1, 5, 10, 50, 100, and 1000 μg L−1 in the standard solutions, which were used for plotting the calibration curves for most of the elements under study. For the Hg analysis, a certified mercury nitrate standard (Supelco, Bellefonte, PA, USA) was used separately. To quantify ultra-trace elements (Be, Ga, Cs and Hg), concentrations of the standard solutions were 0.005, 0.01, 0.02, 0.05, and 0.1 μg L−1. The R2 coefficients for the resultant linear fits were above 0.999.
The concentrations of elements, with the exception of Fe, in all samples were found within the concentration limits of the calibration curves. For the quantification of iron, the samples were subjected to additional dilution by a factor of 10–20. The limits of detection were below 0.3 μg L−1 (Se). No internal standard was used. To compensate for the signal drift over time, the apparent concentrations in the 50 μg L−1 standard solution were measured after every fifth sample, and a time-dependent piecewise linear function of the standard solution signal was utilized for the correction of concentrations.
To mitigate the calcium matrix effects, the optimal dilution procedure was applied. An acid-digested shell sample was progressively diluted, with the dilution factor being increased from 250 to 1500 mL g−1 in steps of 250 mL g−1. The dilution factor which produced no further reduction in the apparent concentrations of the analytes (on a dry weight basis) was taken as the optimal one. In the experimental trials, it was estimated to be approximately 1000 mL g−1.

2.4. Quality Assurance/Quality Control

The quality assurance (quality control) procedures included quantifying trace elements in a certified reference material, conducting duplicate sampling, and employing the standard additions validation.
The entire procedure for the sample preparation and the ICP-MS analysis was validated using certified reference material ERM-CE278k (Institute for Reference Materials and Measurements, Geel, Belgium) (n = 5). The recovery rates for element concentrations ranged from 87% (Ni) to 116% (Cr) as compared to the certified values.
To enhance the accuracy and reliability of measurements, each shell sample was prepared and analyzed in duplicate, with the arithmetic mean used as the expected value.
Standard additions validation was run to confirm the validity of the dilution process and the elimination of matrix effects. For this purpose, concentrations of all analytes, except iron, in a diluted sample with a typical Ca concentration of 300–400 mg L−1 were increased by 0.20, 0.40, 1.00, 5.0, 10.0, 50, and 100 μg L−1 by adding appropriate amounts of diluted certified standards. For iron, the added concentrations were 200, 400, 600, 800, and 1000 μg L−1. The measured concentrations, comparable to the initial one (no added concentration), were plotted and subjected to linear regression in GraphPad Prism 8.0.1. The initial concentrations fell within the 95% confidence intervals for the respective X-intercepts, confirming the accuracy of the dilution procedure and the effective removal of matrix effects.

2.5. Statistical Analysis

The statistical comparison of element concentrations in shells of different molluscs was conducted using PAST 4.14 [65]. Levene’s test was used to check if there was no heterogeneity in dispersions of the samples. If dispersion heterogeneities were absent, the significance of differences among samples was tested using classical ANOVA, supplemented with Tukey’s post hoc test; otherwise, Welch’s ANOVA and the Games–Howell pairwise test were applied. Boxplots and Pearson’s and Spearman’s correlation coefficients were calculated and visualized also in PAST 4.14. The significance threshold in all analyses was set at p = 0.05.
Principal component analysis (PCA), canonical analysis of principal coordinates (CAP), and hierarchical cluster analysis were performed in PRIMER 6.1.16 and PERMANOVA+ 1.0.6, using Euclidean distance as a measure of similarity. In CAP, the group factor was species identity, and 999 permutations were used for the hypothesis testing. In cluster analysis, the unweighted pair group method with arithmetic mean (UPGMA) was employed as the clustering algorithm, and the SIMPROF test was used to verify the separation of clusters at the 5% significance level. In the resulting dendrograms, black solid lines indicate significant separation, while red dotted lines indicate the lack of significant separation of clusters.
Linear discriminant analysis (LDA) was performed in PAST 4.14. Without cross-validation, it classifies the data by assigning each point to the group that results in minimal Mahalanobis distance to the group mean. With cross-validation (leave-one-out allocation, or jackknifing), each point consecutively is excluded from the linear discriminant classifier construction and then is assigned to a particular group using this classifier.

3. Results

3.1. Element Concentrations in Mollusc Shells

The concentrations of elements in shells of the molluscs under study varied significantly depending on the species (Table 1 and Figure S1 (Supplementary Materials)). The concentrations averaged over all species decreased in the following order: Fe > Na > Mg > Br > F > Sr > P > Al > K > Si > Cu > Ni > I > Mn > Zn > Ba > As > B > Li > Ti > Cr > Se > Co > Cd > Ge > Pb > Mo > V > Rb > Hg > Ga > Cs > Be.
Relatively high concentrations of Fe, Ni, Ga and Ge were found in oyster shells; Al, Ti, Mn, Co, Mo, Cs in oyster and ark clam shells; and F and Mg in oyster and scallop shells. Increased concentrations of B, V, Cr, I and Ba were found in ark clam shells, and B and Ba in mussel shells. Rapa whelk shells were distinguished by high concentrations of Li, Si, As, Br, Sr and Hg. The concentrations of the remaining elements were comparable or statistically indistinguishable in shells of the molluscs in question.
The number of elements that had maximal concentrations among the mollusc species was summed up for each species and yielded the following sequence: Crassostrea gigas (9) = Rapana venosa (9) = Anadara kagoshimensis (9) > Flexopecten glaber ponticus (4) > Mytilus galloprovincialis (2). The same sequence was obtained in the cluster analysis of median concentrations with Euclidean distance as resemblance measure and unweighted pair group averaging as clustering method (Figure 2). The dendrogram represents a hierarchy of species based on distances between them; however, according to the SIMPROF test, the distances were not great enough to discriminate between the species at the 5% significance level.

3.2. Correlations

Graphical matrices of Pearson’s r and Spearman’s rs correlation coefficients for the log-transformed element concentrations are presented in Figure 3 and Figure 4 (the corresponding numerical versions are given in Tables S1–S10). The predominantly positive correlations indicate that pathways of accumulation of most elements are similar. It is worth noting the relatively large number of negative correlations for elements in the mussel M. galloprovincialis, which could be a sign of competitive deposition of these elements (mostly Li, Sr, I and metals from Fe to Zn) from inside the mussel.
The correlation coefficients for each species, stretched out in a row, were used to construct resemblance matrices with Euclidean distance as the similarity measure. Cluster analysis coupled with the SIMPROF test shows that Spearman’s correlation coefficients did not lead to distances great enough to discriminate between species (Figure 5a). At the same time, using Pearson’s correlation coefficients, it was possible to identify M. galloprovincialis and F. glaber ponticus (Figure 5b), and use of untransformed concentrations in constructing Pearson’s correlations allowed additionally distinguishing C. gigas (Figure 5c). Notably, these were the two most similar species and the most differing mollusc in the median concentration dendrogram (Figure 2). Shells of A. kagoshimensis and R. venosa were not different enough according to the SIMPROF test applied to the element correlation coefficients.

3.3. Multivariate Ordinations

The most suitable multivariate ordination methods for distinguishing specific mollusc groups according to their trace element composition—linear discriminant analysis (LDA) and canonical analysis of principal coordinates (CAP)—were used in the present study to find statistically significant differences among shells of the different Black Sea molluscs based on their elemental composition.
Log-transformed Z-standardized concentrations of all elements under consideration gave 74.4% and 97.4% correct classification in LDA and CAP, respectively. It is essential to determine the minimum set of element concentration variables that produces the highest accuracy in classifying observations correctly. We considered only those smallest combinations of elements that gave >95% correctly classified observations in LDA without cross-validation. The minimum number of such elements was three; they consisted mostly of one alkaline-earth metal (Mg or Sr), one element of the iron group (Fe, Co or Ni), and arsenic (Table 2). Some of them contained I or Cs in place of the alkaline-earth elements. The maximum of correct classifications was 100% in LDA for the combinations: (Fe, As, I), (Ni, As, I) or (Ni, As, Cs) and 97.4% in CAP for (Mg, Co, As). The combination of four elements (Fe, As, Sr, I) in LDA exhibited 100% of correct classifications in both non-jackknifed and jackknifed versions of the analysis, and only 92.3% in the jackknifed CAP (three M. galloprovincialis samples misclassified as F. glaber ponticus observations). The use of a larger number of elements diminished the percentage of correct classifications.
The biplots for the two ordination techniques with the log-transformed standardized concentrations of Fe, As, Sr and I in the mollusc shells are shown in Figure 6. The overall arrangement of observations and alignment of loading vectors in LDA and CAP were congruous. In LDA, the 95% confidence interval ellipses overlapped for all the molluscs except C. gigas. Below the 90% confidence level, ellipses did not overlap for all the molluscs except M. galloprovincialis and F. glaber ponticus, with the strongly prolate confidence ellipse of F. glaber ponticus absorbing a point belonging to M. galloprovincialis. This is in a relatively good agreement with the CAP results, in which M. galloprovincialis scores were misclassified as those belonging to F. glaber ponticus. No confidence ellipse overlapping occurred below the 79% level (Figure 6a).

4. Discussion

4.1. Comparative Analysis of Element Concentrations in Mollusc Shells

Concentrations of elements in the shells of molluscs from the same genus but different habitats vary, likely reflecting the geochemical characteristics of local biotopes (Table 3). For example, the concentrations of elements in shells of A. granosa collected at various sites along the coast of Malaysia were significantly lower [66] or higher [67] than those from the present study (Table 3).
In the work [70], the concentrations of Li, P, K, Fe, Cu, Ba and Pb in the shells of Mytilus edulis and M. trossulus collected in the eastern and western parts of Northern Eurasia in the White Sea and Pacific Ocean Basins were lower than our results (Table 3). At the same time, in M. trossulus collected off the coast of the Baltic Sea, the concentrations of Pb, Cu, Sr, Mn and Mg were higher or at the same level for Cd and Ba [42] than in M. galloprovincialis from our work. When studying the influence of biotope on the concentration of elements in Rapana venosa shells from the Romanian sector of the Black Sea [73], it was found that the element concentrations in shells of the gastropods living on sandy seafloor were higher than those in shells collected from sandy and rocky seafloor, and the concentrations were lower than in the present study. Thus, element concentrations can vary greatly in shells from different habitats, being largely influenced by environmental conditions during biomineralization. In this case, biological species-specific factors, such as sex, growth rate, metabolism and feeding habits, must be taken into account [42].
Flexopecten glaber ponticus and M. galloprovincialis are responsible for the isolated clusters in Figure 5, demonstrating similarities in correlation coefficients due to the lower trace element levels in their shells. The low concentrations gave rise to correlations with low similarity to those in molluscs with high element concentrations. This is also clear from Figure 6, in which the data for F. glaber ponticus and M. galloprovincialis cannot be separated from each other with high confidence due to the generally low element levels in these molluscs. In contrast, the data for Anadara kagoshimensis, Crassostrea gigas and R. venosa in LDA and CAP are well separated. Thus, the analysis of correlation similarities and the discriminant analyses were, in a sense, inversely related and appeared to be inherently connected to each other. The low trace element levels in the mussel and scallop shells may be due to the relatively smooth surface of these molluscs, whereas the rough and ribbed surfaces of A. kagoshimensis, C. gigas and R. venosa not only give rise to the increased element (particularly, calcium) flux from the mantle but also are conducive to adsorption of elements from the environment. Another possible reason for the interspecific differences in the trace element concentrations is different calcium carbonate polymorph modifications (mainly, aragonite and calcite) in shells of different taxa that tend to host ions of differing size [76].
Among the elements under consideration, five exhibited distinctly high concentrations in all shells, with their averages ranked in descending order as follows: Na ≈ Fe > Mg > F > Sr. Since these elements (except for Na) substitute Ca and are energetically favored in the CaCO3 crystal lattice or form an insoluble compound with Ca (CaF2), their high concentrations are typical of calcareous skeletons of marine invertebrates [42,70,77].
Biogenic CaCO3 in mollusc shells can be of six modifications, of which the most common are two polymorphs, calcite and aragonite, and in some taxa, they coexist in the shells [78,79]. Aragonite and calcite have the same chemical formula and similar diffraction patterns, but differ significantly in their crystal structure [80]. The species selected for this study were the aragonite-shelled clam A. kagoshimensis, the bimineral mussel M. galloprovincialis, scallop F. glaber ponticus, gastropod R. venosa, and the calcite-shelled oyster C. gigas [71,81]. Aragonite, due to its spatial structure, preferentially includes larger cations, such as Sr, while smaller cations, such as Mg, are more energetically favored in calcite. Furthermore, the higher the Mg content, the higher the calcite solubility [42,82]. According to our data, the Mg concentration is indeed significantly higher in the calcite shells of C. gigas and in shells of R. venosa and F. glaber ponticus, which contain a large fraction of calcite, while a higher concentration of Sr is noted in the aragonite shells of A. kagoshimensis (Table 1). Moreover, only in the aragonite shells of A. kagoshimensis, the Sr concentration was three times higher than the Mg concentration. The Sr concentration in the bimineral shells of M. galloprovincialis was twice as low as that of Mg, which indicates a larger calcite fraction but does not align with the data of [42], in which the concentrations of these two elements in M. trossulus differed insignificantly. Notably, the highest concentration of Sr, although slightly lower than the Mg concentration, was found in the bimineral shells of R. venosa, which is probably due to the pronounced species ability to accumulate this element. According to the data of [83], a high Sr concentration was also noted in five examined organs of this gastropod (soft tissue, gonad, muscular leg, hepatopancreas and operculum).
The accumulation of high concentrations of As and Sr in the rapa whelk shells can probably be attributed to several reasons. R. venosa is a benthic mollusc, whose shell can capture and adsorb settling particles rich in specific trace elements, particularly those that are not bound to complexing materials dissolved in seawater and are not readily remobilized from sediments (environmental control of concentration). Likely for this reason, the shells of R. venosa accumulate typical anionic elements (Si, As, Se, Br, etc.) that are not prone to complexation. Furthermore, this mollusc is an active predator that feeds mainly on mussels and oysters [84] and can accumulate essential elements, such as iron, from a Fe-rich diet (primarily biological control of concentration). Rapana can assimilate trace elements from food and surrounding water to significant levels in tissues [85]. The bioavailability of various metals in sediments allows marine biota to remobilize and biomagnify them through the food chain [34]. Rapana often live in coastal areas where there are high concentrations of these elements in water and in sediments due to anthropogenic activity. Some elements may be more bioavailable under specific conditions and be accumulated in organisms that actively filter water or absorb substances from sediments.
High fluorine levels were found in the shells of F. glaber ponticus, C. gigas and M. galloprovincialis; a similar observation for the oyster C. gigas was made quite a long time ago [86]. At present, it has been shown [87,88] that shells of bivalve molluscs are accumulators of fluoride ions and can be considered effective adsorbents for removing excess fluoride from water sources.
One of the macroelements in shells of all the species was Fe, whose high levels in shells were confirmed by the data of other authors [69,70]. According to our results (Table 1), the highest iron concentrations were found in shells of the oyster (12,017 mg kg−1) and rapa whelk (3336 mg kg−1). Fe in oyster shells can be complexed in large amounts with proteins and porphyrin family pigments present in the shell [89,90], being another instance of biological control in the element accumulation. The relatively high Fe concentration in Rapana shells may be due to the environmental control through the mollusc habitat: unlike bivalves settled in submerged farm cages, Rapana live on the muddy seafloor, which serves as a source of high Fe content [91]. In the mollusc periostracum, Fe may act as a protective buffer against degradation under acidic conditions [91], and high Fe concentrations in shells may also be related to environmental pollution.
Also, considerable concentrations of essential elements, such as Se, Zn and I, were found in shells of the Black Sea molluscs (Table 1), being consistent with the results of other studies [68,92]. The concentration of Zn in oyster shells is higher than in mussel shells and is 34 times as high as in fish [93]. Therefore, oyster and mussel shells are good sources of Zn, representing an alternative to meat as a dietary source of this mineral [94]. We also found a high concentration of iodine in the shells of the clam and oyster. In shells of the mussel M. galloprovincialis collected on the South African Atlantic coast, the iodine concentration was comparable to our data, while in shells of the oyster C. gigas, it was lower by a factor of 3 [95].

4.2. Element Concentrations in Molluscs as Signatures of Their Provenance, Harvest Time, or Taxonomic Affiliation

The chemical composition of the shell is highly dependent on external environmental variables, and since shells can be effective accumulators of elements, their ability to record long-term environmental changes can be used to trace the provenance of molluscs [27,47,48,49,50]. As a result, using multivariate trace element analysis, it becomes possible to identify the geographic origin of various seafood products, including shellfish, with high accuracy [96,97,98]. For instance, in [98], 10 key indicator elements were used to construct discriminant models and establish, with an accuracy of 80–100%, the place of origin of the commercially important mollusc Ruditapes philippinarum supplied to the Japanese market from China and the Republic of Korea. Similarly, in a study on the mussel Perna canaliculus, a valuable seafood product for export in New Zealand, an elemental signature methodology was developed, in which the amounts of trace elements incorporated in the shell varied as a function of location and were used to identify the location of source broodstock populations [45].
The application of microelement fingerprinting to bivalve shells, utilizing five-element measurements [47], enabled the identification of sampling sites for Cerastoderma edule molluscs even at a distance of less than 1 km within a single estuarine system. In [27], seven elemental ratios (Zn:Ca, Mn:Ca, B:Ca, Sr:Ca, Mg:Ca, Ba:Ca and Cu:Ca) were found to provide a high degree of reliability (>90%) in identifying the region and collection site of mussel juveniles when modeled using discriminant function analysis. In [46], the Mn:Ca ratio alone was applicable to correctly classify 81.9% of the locations of the recruited juvenile hard clam Mercenaria mercenaria. Thus, even a small set of elements can be used to accurately indicate the provenance of molluscs. For example, only three elemental ratios (Pb:Ca, Ba:Ca, and Mn:Ca) were sufficient to distinguish between sampling sites for two mussel species (M. californianus and M. galloprovincialis) collected along the Californian coast [44].
The authors of [52] classified sites and times of harvest of the king scallop Pecten maximus according to the random forest model using elemental composition of clean and unclean shells (n = 10) and found, respectively, 94.3% and 100% of total correct classifications of two harvest dates that were just 42 days apart. The most important elements in the site and date discrimination were B, Cr, Mn, Se, Mo, Ba and Pb. A similar procedure applied to trace element fingerprints of the mussel M. edulis showed 80 and 90% of total correct classifications of unclean and clean shells (n = 10), respectively [53]. The authors highlight the need for a continuously updated reference library to ensure confidence in the predictions made [48].
There are quite a few studies on trace element fingerprinting in molluscan taxa identification. Although the accuracy of this method applied to shells of living molluscs is considerably inferior to that of molecular genetic identification, it can still prove useful in discriminating between fossil taxa, and more importantly, it can provide methodological groundwork for other applications of multivariate analyses, e.g., environment pollution bioindication. In [56], rare-earth element profiles of shells of five mollusc species were used to classify the molluscs by means of multivariate ordination techniques. The total misclassification error in CAP was estimated at 15%, with the largest specific errors being associated with F. glaber ponticus and C. gigas shells (33 and 29%, respectively). Yap and coworkers [67] found that there were significant correlations between element concentrations in different soft tissues of molluscs, but no correlations were observed between the elements in soft tissues and shells, indicating fundamentally different principles of element accumulation in these animal body parts. The interspecific differences were attributed to different feeding habits (biological control) and living habitats (environmental control). In the work [26] on trace elements in soft tissues of bivalves from the coast of Brazil, two molluscan species were identified as preferentially accumulating distinct sets of elements: Lucina pectinata (with significantly higher concentrations of Cd, Cu, Pb and Zn) and Trachycardium muricatum (with higher concentrations of Cr, Fe, Mn, Ni and Se). Notably, all four of the elements most concentrated in L. pectinata are chalcophiles, while most of the elements characteristic of T. muricatum are siderophiles. A congruous study on elemental fingerprinting in Korean coastline bivalves [55] revealed that the most significant interspecific differences were observed for Cd, Mn, Ni, Zn, and Fe, with particular sets of elements being preferentially accumulated in specific molluscs.
In the present study, siderophile elements (Fe, Co, or Ni) are also included in the sets of elements that characterize the interspecific differences in the shells. In contrast, no characteristic chalcophile metals are present in these sets, likely because their excess is tightly and selectively bound in soft tissues to metallothioneins, which are not known to occur in molluscan shells. The necessary and sufficient set of elements to correctly classify 100% of the data points in LDA comprised Li, Fe, As, and I. This was the only set of four elements that offered completely correct classification of species. It provided >90% of confidence in the separation of R. venosa, A. kagoshimensis, C. gigas, and the pair of F. glaber ponticus and M. galloprovincialis. The data of all five molluscs were fully separated with 79% confidence, according to the parametric ellipsoidal model. It is reasonable to believe that the combination of these elements not only indicates species-specific element accumulation in shells but also reflects various environmental conditions, including the levels of these four elements in the environment, and possibly other contaminants as well. To date, there is no evidence supporting this hypothesis, as there has been very limited research on trace elements in molluscan shells. However, indirect support for this claim can be inferred from the significant interaction of the species and location group factors in analysis of variance (ANOVA) applied to trace element concentrations in molluscan tissues [99,100]. Experiments on raising molluscs under controlled concentrations of different contaminants will shed light on this issue.
The fact that the lowest confidence in the separation of LDA scores was obtained for M. galloprovincialis and F. glaber ponticus shells may stem from their relative smoothness, which leads to lower extrapallial fluxes of elements and lower sorption capacity. As a result, low element concentrations lead to poor discrimination between species. Another distinctive biological factor, the bimineral structure of these shells, does not seem to have a bearing on the poor separation of their scores in LDA and CAP.
The small sample sizes (n = 6–11 per species) and the single-site sampling used in this study might appear to restrict generalizability of the results obtained. However, the limited number of sampled individuals is not expected to drastically affect the accuracy of species classification or the mollusc separation confidence, given the anticipated minimal deviation of the data score distributions (Figure 6) from their general populations. Conversely, the spatial variability of environmental conditions may greatly influence the separation of data scores for collections of the same molluscs from sites located as close as only a few kilometers apart [47]. Validation through sampling the molluscs at additional sites, where feasible, is planned for the future studies.

5. Conclusions

This study presents the first characterization of the macro- and trace element composition in shells of several molluscs (four bivalves and one gastropod) that are common in the Black Sea and have actual or potential commercial value. Different molluscs have been found to preferentially accumulate specific elements in their shells. The elements with the highest median concentrations were Ti, V, Cr, Mn, Rb, Mo, I, Cs and Ba in Anadara kagoshimensis; F, Na, Al, Fe, Co, Ni, Zn, Ga and Ge in Crassostrea gigas, Mg, P, Cd and Pb in Flexopecten glaber ponticus, Li and B in Mytilus galloprovincialis; and Be, Si, K, Cu, As, Se, Br, Sr and Hg in Rapana venosa.
Cluster analysis applied to coefficients of Spearman’s correlations between element concentrations showed no significant separation of the mollusc clusters, while cluster analysis conducted using Pearson’s correlation coefficients demonstrated significant separation of M. galloprovincialis, F. glaber ponticus, and C. gigas. Minimum sets of three elements have been identified that prove to be effective in the correct species classification in the linear discriminant analysis (LDA) and canonical analysis of principal coordinates (CAP). They typically include arsenic, one element of the iron group (Fe, Co, or Ni), and one alkaline earth metal (Mg or Sr). A unique set of four elements (Li, Fe, As, and I) was found to correctly classify 100% and 92.3% of observations in the cross-validated LDA and CAP, respectively. The interspecific data separation confidence in LDA has proven to be above 79%, with the lowest separation being between M. galloprovincialis and F. glaber ponticus, and the separation confidence for the other molluscs exceeded 90%.
This study emphasizes that molluscs are bioaccumulators of a number of trace elements, suggesting that high biomasses of farmed and wild molluscs can make a significant contribution to the biogeochemical cycle of trace elements. The considerable element accumulation selectivity indicates a high degree of biological control in the element deposition in shells. It is likely that environmental control is also involved in the element accumulation, which suggests the possibility of using mollusc shells as bioindicators of medium- and long-term environmental pollution. Upon proper calibration, this bioindication is not only relevant to elements associated with basis variables in the multivariate analyses but can also be extended to other pollutants interacting with these elements through synergistic or antagonistic relations. This calibration involves (1) establishing temporal and/or spatial baseline (minimal) levels of the determinant elements in the environment (suspended matter and seawater) and in shells of the molluscs; and (2) monitoring element concentrations in the environment on considerably shorter time scales than in the shells and noting effects of their averages in the statistical analyses, including discriminant analysis. The strong deviation in the basis variables from linearity will indicate the presence of a pollutant (or pollutants) that interferes with the accumulation of the determinant elements.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17162407/s1, Figure S1: Boxplots of element contents in shells of the Black Sea molluscs Flexopecten glaber ponticus, Mytilus galloprovincialis, Crassostrea gigas, Rapana venosa and Anadara kagoshimensis: (a) Li, (b) Be, (c) B, (d) F, (e) Na, (f) Mg, (g) Al, (h) Si, (i) P, (j) K, (k) Ti, (l) V, (m) Cr, (n) Mn, (o) Fe, (p) Co, (q) Ni, (r) Cu, (s) Zn, (t) Ga, (u) Ge, (v) As, (w) Se, (x) Br, (y) Rb, (z) Sr, (aa) Mo, (ab) Cd, (ac) I, (ad) Cs, (ae) Ba, (af) Hg and (ag) Pb. Table S1: Pearson’s correlation coefficient matrix (lower\upper triangle: r\p) for the log-transformed element concentrations in shells of Anadara kagoshimensis. Table S2: Pearson’s correlation coefficient matrix (lower\upper triangle: r\p) for the log-transformed element concentrations in shells of Crassostrea gigas. Table S3: Pearson’s correlation coefficient matrix (lower\upper triangle: r\p) for the log-transformed element concentrations in shells of Flexopecten glaber ponticus. Table S4: Pearson’s correlation coefficient matrix (lower\upper triangle: r\p) for the log-transformed element concentrations in shells of Mytilus galloprovincialis. Table S5: Pearson’s correlation coefficient matrix (lower\upper triangle: r\p) for the log-transformed element concentrations in shells of Rapana venosa. Table S6: Spearman’s correlation coefficient matrix (lower\upper triangle: rs\p) for the log-transformed element concentrations in shells of Anadara kagoshimensis. Table S7: Spearman’s correlation coefficient matrix (lower\upper triangle: rs\p) for the log-transformed element concentrations in shells of Crassostrea gigas. Table S8: Spearman’s correlation coefficient matrix (lower\upper triangle: rs\p) for the log-transformed element concentrations in shells of Flexopecten glaber ponticus. Table S9: Spearman’s correlation coefficient matrix (lower\upper triangle: rs\p) for the log-transformed element concentrations in shells of Mytilus galloprovincialis. Table S10: Spearman’s correlation coefficient matrix (lower\upper triangle: rs\p) for the log-transformed element concentrations in shells of Rapana venosa.

Author Contributions

Conceptualization, L.L.K., J.D.D. and S.V.K.; methodology, S.V.K.; validation, S.V.K.; formal analysis, S.V.K., S.B. and L.L.K.; investigation, S.V.K. and S.B.; data curation, S.V.K. and L.L.K.; writing—original draft preparation, L.L.K., J.D.D. and E.V.G.; writing—review and editing, E.V.G., S.V.K. and V.I.R.; visualization, S.V.K.; supervision, V.I.R.; funding acquisition, S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ministry of Science and Higher Education of the Russian Federation within the state assignment 124022400152-1 (“Comprehensive study of the mechanisms of functioning of marine biotechnological complexes for obtaining biologically active substances from aquatic organisms”).

Data Availability Statement

The data used in this study are available from the corresponding author on reasonable request.

Acknowledgments

The authors acknowledge the service of the “Spectrometry and Chromatography” core facility (IBSS RAS) in the ICP-MS analysis of elements.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ICP-MSInductively coupled plasma mass spectrometry
LDALinear discriminant analysis
CAPCanonical analysis of principal coordinates

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Figure 1. Map of the shellfish farm (quadrangle 1-2-3-4) and the sampled site (point 3).
Figure 1. Map of the shellfish farm (quadrangle 1-2-3-4) and the sampled site (point 3).
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Figure 2. Cluster analysis of median element concentrations in mollusc shells.
Figure 2. Cluster analysis of median element concentrations in mollusc shells.
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Figure 3. Graphical matrices of Pearson’s correlation coefficients for the log-transformed element concentrations in shells of (a) Anadara kagoshimensis, (b) Crassostrea gigas, (c) Flexopecten glaber ponticus, (d) Mytilus galloprovincialis and (e) Rapana venosa. Significant correlations are shaded.
Figure 3. Graphical matrices of Pearson’s correlation coefficients for the log-transformed element concentrations in shells of (a) Anadara kagoshimensis, (b) Crassostrea gigas, (c) Flexopecten glaber ponticus, (d) Mytilus galloprovincialis and (e) Rapana venosa. Significant correlations are shaded.
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Figure 4. Graphical matrices of Spearman’s correlation coefficients rs for the log-transformed element concentrations in shells of (a) Anadara kagoshimensis, (b) Crassostrea gigas, (c) Flexopecten glaber ponticus, (d) Mytilus galloprovincialis and (e) Rapana venosa. Significant correlations are shaded.
Figure 4. Graphical matrices of Spearman’s correlation coefficients rs for the log-transformed element concentrations in shells of (a) Anadara kagoshimensis, (b) Crassostrea gigas, (c) Flexopecten glaber ponticus, (d) Mytilus galloprovincialis and (e) Rapana venosa. Significant correlations are shaded.
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Figure 5. Cluster analysis and SIMPROF test of coefficients of (a) Spearman’s and (b) Pearson’s correlations between log-transformed element concentrations and (c) Pearson’s correlations between untransformed element concentrations in mollusc shells.
Figure 5. Cluster analysis and SIMPROF test of coefficients of (a) Spearman’s and (b) Pearson’s correlations between log-transformed element concentrations and (c) Pearson’s correlations between untransformed element concentrations in mollusc shells.
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Figure 6. (a) LDA and (b) CAP biplots for four elements (Fe, As, Sr, I) in the Black Sea mollusc shells. In (a), ellipses denote the 79% parametric confidence intervals for the mollusc groups. In (b), the vectors denote projections of Pearson’s correlations with the axes.
Figure 6. (a) LDA and (b) CAP biplots for four elements (Fe, As, Sr, I) in the Black Sea mollusc shells. In (a), ellipses denote the 79% parametric confidence intervals for the mollusc groups. In (b), the vectors denote projections of Pearson’s correlations with the axes.
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Table 1. Element contents in shells of Black Sea molluscs: median ± median absolute deviation (no emphasis: in mg·kg−1 d.w., bold emphasis: in μg·kg−1 d.w.). Different superscript letters denote significant differences (a > b > c > d).
Table 1. Element contents in shells of Black Sea molluscs: median ± median absolute deviation (no emphasis: in mg·kg−1 d.w., bold emphasis: in μg·kg−1 d.w.). Different superscript letters denote significant differences (a > b > c > d).
Anadara
kagoshimensis
Crassostrea gigasFlexopecten
glaber ponticus
Mytilus
galloprovincialis
Rapana
venosa
Li0.69 ± 0.31 b0.54 ± 0.10 b0.39 ± 0.16 b14.0 ± 8.7 ab8.72 ± 2.68 a
Be 6.31 ± 1.65 a 6.86 ± 2.40 a 7.42 ± 2.44 a 5.63 ± 1.51 a 9.58 ± 4.18 a
B6.10 ± 1.10 a4.22 ± 0.17 ab3.83 ± 0.54 ab7.39 ± 0.47 a1.89 ± 1.53 b
F *113 ± 40 bc1842 ± 645 abc1147 ± 115 a891 ± 164 ab171 ± 13 c
Na3201 ± 159 a4186 ± 523 a2725 ± 125 b2630 ± 391 ab4039 ± 463 a
Mg338 ± 62 c2330 ± 137 a2403 ± 119 a1181 ± 198 bc1539 ± 218 b
Al333 ± 68 a391 ± 187 a103 ± 47 ab32.4 ± 3.4 b165 ± 105 a
Si113 ± 29 bc21.1 ± 4.1 c56.2 ± 29.6 bc34.3 ± 23.0 bc521 ± 50 a
P233 ± 27 ab84.9 ± 16 c277 ± 32 ab223 ± 18 bc167 ± 85 bc
K75.2 ± 4.7 a43.8 ± 27.4 a52.0 ± 17.1 a44.9 ± 23.0 a81.0 ± 42.7 a
Ti4.13 ± 0.86 a4.10 ± 0.80 a2.17 ± 0.31 ab1.53 ± 0.14 b3.16 ± 0.80 ab
V1.87 ± 0.29 a0.24 ± 0.16 b0.36 ± 0.05 b0.30 ± 0.03 b0.52 ± 0.41 b
Cr6.77 ± 3.27 a0.16 ± 0.12 c1.38 ± 0.08 b1.09 ± 0.10 bc0.71 ± 0.24 bc
Mn29.0 ± 11.6 a22.8 ± 6.1 ab20.5 ± 2.4 a8.47 ± 1.27 b14.3 ± 3.7 ab
Fe1471 ± 121 d12017 ± 1743 a2244 ± 126 c2441 ± 49 bcd3336 ± 170 b
Co2.22 ± 0.08 a2.26 ± 0.23 a1.28 ± 0.14 bc1.16 ± 0.03 bc1.65 ± 0.37 ab
Ni31.5 ± 5.0 c90.8 ± 11.7 a39.0 ± 1.1 bc42.4 ± 0.74 bc47.8 ± 1.8 b
Cu70.9 ± 28.6 c161 ± 62 ab65.7 ± 16.5 bc86.4 ± 11.4 bc209 ± 25 a
Zn5.76 ± 1.88 a9.07 ± 4.10 a3.66 ± 2.08 a2.06 ± 0.81 a7.05 ± 2.29 a
Ga 62.3 ± 10.6 b 136 ± 39.4 a 41.5 ± 13.4 b 23.5 ± 7.1 ab 47.6 ± 24.3 b
Ge1.16 ± 0.19 b3.23 ± 0.65 a1.20 ± 0.02 b1.13 ± 0.11 b1.18 ± 0.06 b
As1.59 ± 0.19 b2.30 ± 0.25 b1.16 ± 0.13 c1.87 ± 0.35 bc15.5 ± 5.3 a
Se1.34 ± 0.74 ab1.53 ± 0.49 b2.36 ± 0.22 a2.07 ± 0.03 ab3.56 ± 2.85 ab
Br168 ± 31 b123 ± 30 bc72.8 ± 11.8 c364 ± 138 bc4769 ± 2152 a
Rb0.53 ± 0.13 a0.45 ± 0.10 a0.29 ± 0.15 a0.10 ± 0.03 a0.23 ± 0.06 a
Sr973 ± 80 ab484 ± 79 d788 ± 49 c571 ± 26 bcd1376 ± 183 a
Mo0.84 ± 0.22 ab0.79 ± 0.28 ab0.50 ± 0.17 ab0.234 ± 0.012 b0.57 ± 0.07 a
Cd0.18 ± 0.04 a0.43 ± 0.19 a1.22 ± 0.75 a0.070 ± 0.014 a0.14 ± 0.11 a
I49.0 ± 7.9 a31.7 ± 1.1 b7.35 ± 1.72 c5.96 ± 0.22 c22.7 ± 7.5 b
Cs 72.1 ± 27.7 a 36.3 ± 13.6 ab 20.1 ± 6.7 b 6.07 ± 2.49 b 10.7 ± 6.5 b
Ba17.6 ± 3.6 a5.62 ± 1.75 b6.61 ± 1.52 b16.4 ± 2.4 a3.99 ± 0.97 b
Hg 17.9 ± 9.4 c 109 ± 44 ab 35.5 ± 11.6 bc 12.2 ± 4.1 bc 201 ± 7 a
Pb0.81 ± 0.15 a0.73 ± 0.14 a1.37 ± 1.07 a0.71 ± 0.08 a0.63 ± 0.35 a
Note: * Measured semi-quantitatively.
Table 2. Percentage of total correct classification of shell samples in LDA and CAP based on the minimum number of determinant elements.
Table 2. Percentage of total correct classification of shell samples in LDA and CAP based on the minimum number of determinant elements.
LDA CAP
No Cross-Validation Jackknifed Jackknifed
Mg, Fe, As97.494.992.3
Mg, Co, As97.494.997.4
Mg, Ni, As97.494.992.3
Fe, As, Sr97.497.487.2
Fe, As, I10094.987.2
Ni, As, I10089.787.2
Ni, As, Sr97.494.989.7
Ni, As, Cs10087.289.7
Fe, As, Sr, I10010092.3
Table 3. Concentrations of elements (mg·kg−1 d.w.) in shells of molluscs of different genera: mean ± standard deviation. Note: (t.) = (this study).
Table 3. Concentrations of elements (mg·kg−1 d.w.) in shells of molluscs of different genera: mean ± standard deviation. Note: (t.) = (this study).
Anadara Crassostrea Aequipecten, Flexopecten Mytilus Rapana venosa
NaA. granosa 887 ± 81 [66]
A. kagoshimensis 3234 ± 288 (t.)
C. brasiliana and C. mangle 3830 ± 1990 [68]
C. gigas 4210 ± 878 (t.)
F. glaber ponticus 2581 ± 330 (t.)M. galloprovincialis 4333 ± 418 [69]
M. trossulus 2010 ± 310 [42]
M. edulis 3450 ± 191 [70]
M. galloprovincialis 4002 ± 3626 (t.)
2420 ± 1575 [71]
4034 ± 1109 (t.)
MgA. granosa 53.2 ± 2.6 [66]
A. kagoshimensis 668 ± 879 (t.)
C. brasiliana and C. mangle 2785 ± 1643 [68]
C. gigas 2471 ± 486 (t.)
A. opercularis 92.8 ± 1.3 [72]
F. glaber ponticus 2581 ± 330 (t.)
M. galloprovincialis 937 ± 153 [69]
M. trossulus 1060 ± 170 [42]
M. edulis 1020 ± 65 [70]
M. galloprovincialis 1359 ± 471 (t.)
950 ± 85 [71]
490 ± 64 [73]
1517 ± 369 (t.)
AlA. granosa 451 ± 12 [66]
A. kagoshimensis 321 ± 114 (t.)
C. gigas 2471 ± 486 (t.)F. glaber ponticus 149 ± 136 (t.)M. galloprovincialis 34 ± 14 [69]
M. edulis 34.5 ± 9.1 [70]
M. galloprovincialis 42 ± 18 (t.)
735 ± 960 [71]
5.32 ± 2.65 [73]
213 ± 142 (t.)
PA. granosa 467 ± 2.4 [66]
A. kagoshimensis 245 ± 73 (t.)
C. gigas 111 ± 60 (t.)F. glaber ponticus 298 ± 88 (t.)M. edulis 154 ± 12 [70]
M. galloprovincialis 220 ± 181 (t.)
125 ± 174 [71]
278 ± 415 (t.)
KA. kagoshimensis 83 ± 20 (t.)C. gigas 61 ± 56 (t.)A. opercularis 1349 ± 449 [72]
F. glaber ponticus 70 ± 64 (t.)
M. edulis 31.8 ± 11.3 [70]
M. galloprovincialis 51 ± 32 (t.)
315± 428 [71]
98 ± 47 (t.)
CrA. kagoshimensis 7.82 ± 4.61 (t.)C. brasiliana and C. mangle 0.72 ± 0.82 [68]
C. gigas 0.23 ± 0.28 (t.)
F. glaber ponticus 1.75 ± 0.66 (t.)M. galloprovincialis 1.5 ± 0.3 [69]
M. galloprovincialis 1.69 ± 1.46 (t.)
0.30 ± 0.22 [73]
0.78 ± 0.51 (t.)
MnA. granosa 0.255 ± 0.001 [66]
A. kagoshimensis 35.1 ± 18.2 (t.)
C. brasiliana and C. mangle 28 ± 21 [68]
C. gigas 24.3 ± 12.0 (t.)
A. opercularis 75.5 ± 88.7 [72]
F. glaber ponticus 19.2 ± 5.3 (t.)
M. galloprovincialis 1.8 ± 0.3 [69]
8.2 ± 1.7 [74]
M. trossulus 54.3 ± 15.1 [42]
M. edulis 154 ± 71 [71]
M. galloprovincialis 8.18 ± 1.57 (t.)
2.96 ± 0.84 [73]
16.2 ± 10.9 (t.)
FeA. granosa 1400 ± 100 [66]
A. granosa 312 ± 188 [67]
A. kagoshimensis 1564 ± 325 (t.)
C. brasiliana and C. mangle 29 ± 24 [68]
C. gigas 13,157 ± 4088 (t.)
A. opercularis 1595 ± 2746 [72]
F. glaber ponticus 2285 ± 1852 (t.)
M. galloprovincialis 123 ± 29 [69]
M. edulis 337 ± 100 [70]
M. galloprovincialis 1962 ± 1028 (t.)
9.3 ± 1.2 [73]
3294 ± 665 (t.)
CoA. kagoshimensis 2.19 ± 0.15 (t.)C. brasiliana and C. mangle 0.082 ± 0.065 [68]
C. gigas 2.73 ± 0.97 (t.)
A. opercularis 0.50 ± 0.63 [72]
F. glaber ponticus 1.29 ± 0.15 (t.)
M. galloprovincialis 35.6 ± 7.3 [74]
M. galloprovincialis 0.1 ± 0.03 [69]
M. galloprovincialis 0.99 ± 0.39 (t.)
1.83 ± 0.63 (t.)
NiA. granosa 0.027 ± 0.001 [66]
A. granosa 31.8 ± 17.6 [67]
A. kagoshimensis 31.2 ± 5.0 (t.)
C. gigas 90.8 ± 11.8 (t.)A. opercularis 1.77 ± 1.07 [72]
F. glaber ponticus 41.3 ± 4.2 (t.)
M. galloprovincialis 1.4 ± 0.3 [74]
M. galloprovincialis 0.4 ± 0.19 [69]
M. galloprovincialis 34.5 ± 18.6 (t.)
1.36 ± 1.01 [73]
50.0 ± 10.2 (t.)
CuA. granosa 0.93 ± 0.01 [66]
A. granosa 4.95 ± 0.78 [67]
A. kagoshimensis 75.0 ± 36.9 (t.)
C. gigas 170 ± 73 (t.)A. opercularis 6.8 [72]
F. glaber ponticus 89.4 ± 52.4 (t.)
M. galloprovincialis 2.9 ± 0.7 [74]
M. trossulus 14.1 ± 10.2 [42]
M. edulis 0.96 ± 0.23 [70]
M. galloprovincialis 77.1 ± 20.0 (t.)
5.88 [75]
190 ± 77 (t.)
ZnA. granosa 9.22 ± 4.82 [67]
A. kagoshimensis 8.27 ± 8.85 (t.)
C. brasiliana and C. mangle 4.0 ± 4.1 [68]
C. gigas 16.1 ± 21.1 (t.)
A. opercularis 41 [72]
F. glaber ponticus 4.61 ± 3.28 (t.)
M. galloprovincialis 38.9 ± 7.5 [74]
M. galloprovincialis 3.33 ± 0.43 [69]
M. edulis 17.3 ± 2.13 [70]
22.99 [75]
M. galloprovincialis 15.7 ± 30.0 (t.)
16.82 [75]
1.04 ± 0.36 [73]
6.81 ± 2.65 (t.)
AsA. kagoshimensis 1.58 ± 0.23 (t.)C. brasiliana and C. mangle 0.2 ± 0.12 [68]
C. gigas 2.28 ± 0.56 (t.)
F. glaber ponticus 1.15 ± 0.15 (t.)M. galloprovincialis 3.1 ± 0.8 [74]
M. galloprovincialis 3.72 ± 4.54 (t.)
24.1 ± 18.8 (t.)
BrA. kagoshimensis 176 ± 51 (t.)C. brasiliana and C. mangle 8 ± 6.6 [68]
C. gigas 120 ± 39 (t.)
F. glaber ponticus 79.0 ± 25.4 (t.)M. galloprovincialis 89.3 ± 22.5 [69]
M. galloprovincialis 392 ± 214 (t.)
5539 ± 4809 (t.)
SrA. kagoshimensis 951 ± 114 (t.)C. gigas 518 ± 106 (t.)F. glaber ponticus 794 ± 56 (t.)M. trossulus 1170 ± 100 [42]
M. galloprovincialis 1220 ± 264 [69]
M. edulis 1600 ± 37 [70]
M. galloprovincialis 475 ± 251 (t.)
1620 ± 3048 [71]
1393 ± 442 (t.)
MoA. kagoshimensis 1.01 ± 0.70 (t.)C. gigas 0.85 ± 0.42 (t.)F. glaber ponticus 0.96 ± 1.06 (t.)M. galloprovincialis 0.29 ± 0.12 (t.)1.26 ± 0.22 [73]
0.56 ± 0.15 (t.)
CdA. granosa 8.65 ± 3.15 [67]
A. kagoshimensis 0.19 ± 0.11 (t.)
C. gigas 0.89 ± 0.86 (t.)F. glaber ponticus 6.7 ± 14.1 (t.)M. trossulus 0.09 ± 0.11 [42]
M. galloprovincialis 0.35 ± 0.58 (t.)
0.94 [75]
0.33 ± 0.34 (t.)
IA. granosa 0.01 ± 0.0001 [66]
A. kagoshimensis 55.6 ± 17.3 (t.)
C. gigas 35.1 ± 5.4 (t.)F. glaber ponticus 9.21 ± 4.84 (t.)M. galloprovincialis 8.0 ± 4.4 [69]
M. galloprovincialis 5.91 ± 0.45 (t.)
26.0 ± 15.7 (t.)
BaA. kagoshimensis 17.8 ± 4.8 (t.)C. brasiliana and C. mangle 5.0 ± 5.6 [68]
C. gigas 5.64 ± 2.16 (t.)
A. opercularis 4.0 ± 3.8 [72]
F. glaber ponticus 7.08 ± 2.20 (t.)
M. trossulus 17.1 ± 6.69 [42]
M. edulis 12.2 ± 1.01 [70]
M. galloprovincialis 15.3 ± 5.3 (t.)
4.96 ± 2.67 (t.)
HgA. kagoshimensis 0.020 ± 0.011 (t.)C. gigas 0.120 ± 0.055 (t.)F. glaber ponticus 0.036 ± 0.021 (t.)M. galloprovincialis 0.040 ± 0.002 [74]
M. galloprovincialis 0.031 ± 0.039 (t.)
0.195 ± 0.108 (t.)
PbScapharca sp. 3.11 [75]
A. kagoshimensis 0.78 ± 0.21 (t.)
C. brasiliana and C. mangle 0.9 ± 0.56 [68]
C. gigas 1.02 ± 0.54 (t.)
A. opercularis 1.68 ± 1.63 [72]
F. glaber ponticus 1.65 ± 1.48 (t.)
M. galloprovincialis 1.0 ± 0.2 [74]
M. trossulus 1.14 ± 1.41 [42]
M. edulis 0.11 ± 0.01 [70]
M. galloprovincialis 0.89 ± 0.34 (t.)
0.74 ± 0.48 (t.)
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MDPI and ACS Style

Kapranov, S.V.; Kapranova, L.L.; Gureeva, E.V.; Ryabushko, V.I.; Dikareva, J.D.; Barinova, S. Species-Specific Element Accumulation in Mollusc Shells: A Framework for Trace Element-Based Marine Environmental Biomonitoring. Water 2025, 17, 2407. https://doi.org/10.3390/w17162407

AMA Style

Kapranov SV, Kapranova LL, Gureeva EV, Ryabushko VI, Dikareva JD, Barinova S. Species-Specific Element Accumulation in Mollusc Shells: A Framework for Trace Element-Based Marine Environmental Biomonitoring. Water. 2025; 17(16):2407. https://doi.org/10.3390/w17162407

Chicago/Turabian Style

Kapranov, Sergey V., Larisa L. Kapranova, Elena V. Gureeva, Vitaliy I. Ryabushko, Juliya D. Dikareva, and Sophia Barinova. 2025. "Species-Specific Element Accumulation in Mollusc Shells: A Framework for Trace Element-Based Marine Environmental Biomonitoring" Water 17, no. 16: 2407. https://doi.org/10.3390/w17162407

APA Style

Kapranov, S. V., Kapranova, L. L., Gureeva, E. V., Ryabushko, V. I., Dikareva, J. D., & Barinova, S. (2025). Species-Specific Element Accumulation in Mollusc Shells: A Framework for Trace Element-Based Marine Environmental Biomonitoring. Water, 17(16), 2407. https://doi.org/10.3390/w17162407

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