Multi-biomarker approach using the blue mussel (Mytilus edulis L.) to assess the quality of marine environments: season and habitat-related impacts.

Marine Environmental Research xxx (2013) 1e15

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Multi-biomarker approach using the blue mussel (Mytilus edulis L.) to assess the quality of marine environments: Season and habitat-related impacts M. Brenner a, *, K. Broeg a, S. Frickenhaus a, b, B.H. Buck a, b, A. Koehler a, c a b c

Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven, Germany University of Applied Sciences Bremerhaven, Bremerhaven, Germany Jacobs University Bremen, Bremen, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Received 14 August 2013 Received in revised form 6 December 2013 Accepted 7 December 2013

Using a comprehensive approach, intertidal, near- and offshore sites in the German Bight were analysed for their environmental quality by assessing the health of blue mussels (Mytilus edulis). During a ten month sampling period mussels were studied with a set of biomarkers comprising lysosomal membrane stability and accumulation of lipofuscin, supplemented by biomarkers indicating nutritional status such as neutral lipids and glycogen in the cells of the digestive gland. Data were analysed in relation to sex, gonadal status, condition index and for the presence of parasites, to determine the overall health status of mussels at the respective sites. Mussels from all sites showed clear signs of stress, indicating an inferior environmental quality throughout the southern German Bight. Further, habitat characteristics such as inundation time and growing on- or off-bottom, as well as seasonal factors, can clearly influence the response of biomarkers in mussels exposed to similar levels of chemical environmental stress. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Mytilus edulis Biomarker Lysosomal stability Parasites Habitat condition Multivariate analysis

1. Introduction The blue mussel is an active suspension feeder, filtering phytoplankton and suspended particles from the water column. Due to this feeding mode, in addition to their normal food, mussels ingest bacteria, algae, toxins, parasite larvae as well as chemical pollutants from the marine environment. Blue mussels predominantly inhabit shores and estuarine environments. These habitats are highly complex due to their natural variability in temperature, salinity, and duration of exposure to air and food supply. Due to anthropogenic activities, shorelines are also particularly exposed to high concentrations of chemical pollutants, near surface agents and estuarine runoffs which can pose a threat to the health of mussels (Cajaraville et al., 2000; Barsiene_ et al., 2006; Marigómez et al., 2013). As sessile filter feeding organisms, mussels directly reflect the level of contamination in a habitat (e.g. Moore, 1985; Viarengo, 1985; Dondero et al., 2006; Brenner et al., 2012).

* Corresponding author. Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI), Am Handelshafen 12, 27570 Bremerhaven, Germany. Tel.: þ49 471 4831 1034; fax: þ49 471 4831 1149. E-mail address: [email protected] (M. Brenner).

In general, the presence of toxic chemical compounds in an environment can be detected by chemical analysis of the water and sediment samples. However, this approach provides only limited information on concentrations of pollutants in organisms and their tissues and no information on the effects of these toxic chemicals on biological systems. Thus, the health of sentinel organisms is commonly used to assess the quality of a particular marine environment. Since mussels are widely distributed, abundant, sedentary, and easy to obtain, they are one of the best investigated marine organisms worldwide (Rainbow and Phillips, 1993; Rainbow, 1995; Szefer et al., 2006) Today, blue mussels are the bioindicator of choice in several national and international environmental monitoring programs e.g. MED POL (UNEP Mediterranean Biomonitoring Programme) or BEEP (EU Biological Effects of Environmental Pollution Programme) and they are widely used in marine pollution monitoring (Goldberg, 1975; Cajaraville et al., 1990; Livingstone et al., 1990; Smolders et al., 2003; ICES, 2006; Marigómez et al., 2006). A specific suite of biomarkers can be used to assess the impacts of stress on the deployed bioindicator organism (Viarengo et al., 2007). Some of these biomarkers act at the molecular and cellular levels, thus providing the earliest warning signals of toxic chemicals on tissues and organisms (Shugart et al., 1990, 1992). Stress sensitive

0141-1136/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marenvres.2013.12.009

Please cite this article in press as: Brenner, M., et al., Multi-biomarker approach using the blue mussel (Mytilus edulis L.) to assess the quality of marine environments: Season and habitat-related impacts, Marine Environmental Research (2013), http://dx.doi.org/10.1016/ j.marenvres.2013.12.009

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M. Brenner et al. / Marine Environmental Research xxx (2013) 1e15

biomarkers can be used to assess the health of an ecosystem as a whole in which the organisms live (Cajaraville et al., 1998) or for the analysis of individual organisms that live in a specific environment or at specifically contaminated sites. Well-established examples of biomarkers of stress are the tests for lysosomal membrane stability, the lysosomal lipofuscin content and the neutral lipid accumulation in lysosomes (Viarengo and Nott, 1993; Marigómez and BaybayVillacorta, 2003; Aarab et al., 2011). Lysosomes are cell organelles containing various hydrolytic enzymes for the degradation of macromolecules and are surrounded by a semi permeable membrane (e.g. Moore, 1976; Ferreira and Dolder, 2003). They are responsible for the recycling of aged cell organelles, metabolic waste products, and isolate harmful chemical substances, once they have entered the cells (Einsporn and Köhler, 2008a,b). Lysosomes in molluscan digestive cells accumulate metals, organic contaminants as well as nano- and microplastic particles that cannot be degraded. These substances may provoke significant alterations in the lysosomes (Moore et al., 1980a, 1980b; Nott et al., 1985; Viarengo et al., 1985; Sarasquete et al., 1992; Cajaraville et al., 1995; Moore et al., 2004; Koehler et al., 2008; von Moos et al., 2012). In general, contaminants from the environment cause an increase in the size and number of lysosomes (Marigómez et al., 1989; Regoli et al., 1998; Koehler et al., 2002). When storage capacities of the lysosomes are overloaded lysosomal membrane may become unstable and leaky. Pollutants and hydrolytic-lysosomal enzymes can re-enter the cytoplasm with serious risk of cell death (Koehler et al., 2002). Impairment of lysosomal functions and hence, reduced food assimilation in the digestive tubules can result in severe alterations in the nutritional status of cells and adversely affect the whole organism. For this reason, lysosomal changes and especially lysosomal membrane destabilisation are widely accepted as general stress biomarkers indicative of poor health (Moore et al., 2004). Another cellular indicator of nutritional status and thus, indirectly of health, is the presence and amount of glycogen in the cells of the digestive gland. Glycogen is the molecule that functions as secondary long-term energy storage in animal and fungi cells. It is found in the form of granules in the cytosol of many cell types, and plays an important role in the glucose cycle. Glycogen forms an energy reserve that can be quickly mobilized to meet a sudden need for glucose. In bivalves glycogen is the primary energy reserve (Patterson et al., 1999). Environmental stress and anthropogenic pollution increases stress on the mussel, thus leading to an increased metabolism and reduced glycogen storage. As changes in the glycogen level correlates with the level of chemical stress, this parameter is also widely used as a general biomarker for environmental monitoring (Ansaldo et al., 2006). In addition, the accumulation of lipofuscin is used to describe the health status of blue mussels. Lipofuscin, also known as an age pigment, and is widely regarded as an end product of protein and lipid peroxidation due to oxidative stress (Au et al., 1999; Au, 2004; Terman and Brunk, 2004). An increased accumulation of lipofuscin in the lysosomes of the digestive gland of mussels or in the liver of fish has been shown to be associated with oxiradical damage caused by anthropogenic pollutants e.g. metals (Krishnakumar et al., 1994, 1997; Au et al., 1999; Au, 2004). The excess of neutral lipids, either intra-lysosomal or in the cytoplasm, can be an indicator of lipidosis induced by toxic chemicals, especially due to exposure to xenobiotics such as polycyclic aromatic hydrocarbons (Lowe et al., 1981). Further, accumulations of lipid droplets in the cytoplasm, consisting of predominantly neutral lipids, are known to be involved in the induction of apoptosis (Boren and Brindle, 2012). In addition to the histochemical biomarker approach chosen the infestation with macro parasites was investigated. Macro parasites

living in blue mussels are numerous in intertidal and nearshore areas. Buck et al. (2005) and Brenner et al. (2009, 2012) have shown that offshore-grown mussels are essentially free of macro parasites. Infestation rates increased the closer the sites were to shore, where in particular intertidal mussels showed the highest numbers of parasites. The objectives of this study were (i) to use a multi-biomarker approach to describe the health of investigated mussel in an integrative way including parasitic infestation, (ii) to assess the seasonal variability of biomarker responses and (iii) to evaluate the influence of different habitat structures (nearshore intertidal, nearshore submerged and offshore submerged) on biomarker variability. The results of the multi-biomarker analysis are displayed including sex and gonadal status of each mussel by multivariate analyses such as principal component (PCA) and linear discriminant analysis (LDA) to reduce data variability and to visualize dominant factors. 2. Material and methods For this study, five locations were selected along the coast of the German Bight (Fig. 1). Three of the locations were natural colonies of mussels (near Neuharlingersiel, NH; Bordumer Sand, BS; the Lister Strand of the Island of Sylt, SY) and two were locations where artificial substrates were provided for the mussels to colonize (of the cargo bridge Niedersachsenbrücke at the Jade Bay, JD; and an area in the outer Weser estuary approx. 17 nautical miles northwest of the city of Bremerhaven called Roter Sand, RS). The NH, BS, SY, JD, and RS sites are characterized by upper intertidal, upper intertidal, lower intertidal, nearshore, and offshore conditions (Fig. 1). At the sampling areas JD and RS a Japanese patent called “Artificial Seaweed” (Ó Fukui North America) was used as an artificial substrate. The substrate consists of a 10 mm nylon rope as its back bone to which 10 cm long polypropylene-leaves are attached perpendicularly at both sites. Each centimetre of back bone holds approx. 20 leaves to enlarge the substrate’s surface. At the Niedersachsenbrücke, the artificial substrates were attached to harnesses hanging from the cargo bridge. Each harness consisted of a 20 mm polypropylene rope with an iron plate (5 kg) at its distal end, weighing down the substrates into the water column even at strong current velocities. To insure that mussels were submerged during the whole experiment, samples of artificial substrates were fixed to the ropes from 1 to approximately 3 m below mean low water (mLW) level. The deployed substrate samples were about 15 cm in length and fixed every 20 cm to the rope. At the offshore location RS, three modified buoyancies (3.6 m in height; 1.1 m in diameter) were deployed to test the mussels. All buoyancies were equipped with an anchor stone of 1.5 tons and a 30 m steel chain in between. All buoys were equipped with three steel rings, welded to each buoy by three 25 cm long cross-beams. Rings were placed at water surface level, 1.5 m and 3 m in depth. A 10 mm nylon rope was tied up vertically between the three steel rings. Similarly to the set-up at the Niedersachsenbrücke, substrate samples were attached at 20 cm intervals to the ropes using plastic binders. The set-up at Jade Bay and Roter Sand was conducted in April 2006, before the expected spawning time of mussels in early May (Pulfrich, 1997). Harnesses at JD and buoys at RS were inspected regularly between May 2006 and November 2007. In 2007, four consecutive sampling cycles in March, May, August, and November were conducted to analyse effects of the habitat as well as seasonal impacts on the selected health parameters. Each sampling cycle was completed within 10 days and all parameters were analysed for each site and sample cycle. Intertidal areas (NH, BS and SY) were sampled at low water, whereas RS had to be sampled at slack water



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Fig. 1. Map of the German Bight showing the sample sites. Three intertidal sampling locations at Neuharlingersiel (NH, upper intertidal, Position 53 420 1000 N; 007 430 5000 E), Bordumer Sand (BS, upper intertidal, Position 53 300 0000 N; 008 060 0000 E) and Lister Strand at the island of Sylt (SY, lower intertidal, Position 55 010 3200 N; 008 260 4300 E) and two sampling areas with mussels hanging suspended in the water column at the Niedersachsenbrücke (nearshore) near Wilhelmshaven in the Jade (JD, Position 53 350 0500 N; 008 090 1400 E) Bay and offshore at the entrance of the Weser estuary near the lighthouse Roter Sand (RS, Position 53 510 0000 N; 008 040 2000 E) were sampled.

with a team of scuba divers. The JD site is accessible without any tidal constraints all year round. Per season and site, mussels between 30 and 50 cm were collected for further investigations. Fifteen mussels per treatment were used to analyse parasitic infestations. All other parameters were investigated using another 12 individuals of the same size. These 12 individuals per treatment group were used to assess all other parameters. Data describing temperature and salinity in near- and offshore areas of the German Bight were obtained by taking monthly water samples between March and the end of October at sampling sites JD and RS. Temperature and salinity were not measured on a regular basis at the intertidal areas, since there sampling was restricted to low water conditions. 2.1. Fulton’s condition index Most commonly, the condition indices (CI) uses a weight relationship between shell and soft body to describe mussel fitness. However, blue mussels develop different shells in terms of stability and weight according to the predominant habitat conditions in which they live such as currents, tide and wave movement, as well as their growing mode i.e. whether on the bottom or hanging in the water column. Using common CIs, light shells of submerged or hanging mussels will result automatically in high CIs, whereas the heavy shells produced by intertidal mussels will lead to rather low CIs. As blue mussels show in a clear linear growth, in contrast to clams and scallops for example, a length-weight-relationship developed originally for fishery research (Fulton, 1904; Heincke, 1908) was chosen to compare the condition of investigated mussels. Per treatment group 12 individuals were used to calculate the Fulton’s Condition Index (CIF). For a direct comparison of CIF with

the other assessed health parameters only wet tissue weight could be used (see below).

CIF ¼

Wet meat weight ½g Length ½cm3

100

(1)

2.2. Parasites To ensure that all mussels were of a comparable age range, 15 mussels per site and sampling event were selected according to a shell length between 30 and 50 mm. These represent specimens of similar physiology, also used in standardized bioassays (Ernst et al., 1991). Raw mussel were frozen and stored at 20 C. After defrosting at room temperature (approx. 20e30 min) mussels were analysed immediately. Length and width of each selected mussel were measured according to Seed (1968) to the nearest 0.1 mm using a vernier calliper. Mussels were opened, briefly drained on absorbent paper, and subsequently total wet weight was determined. The soft body was then removed and both shell and soft body were weighed (0.01 g) separately. The soft body was then placed on the bottom of a glass compressorium and the mantle, gills, food, adductor muscle and other tissues were dissected carefully and dispersed. The digestive gland was pulled apart and squeezed together with the other tissues using the cover glass of the compressorium. The preparations were examined under a stereo magnifying glass (10e50 magnification) with transmitting light for the presence of parasites. Parasite species were identified according to descriptions from the literature (e.g. Dethlefsen, 1970, 1972; Lauckner, 1983; Watermann et al., 1998) and infested organs listed. As freezing of the samples does not affect size of a trematod’s metacercaria (Lepitzki et al., 1994), identification of trematodes was


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also reliable using the frozen samples. In a final step all shells of the analysed mussels were inspected for the presence of shell-boring polychaetes using the stereo magnifying glass. Identified and counted parasites were assessed regarding their prevalence (P) as the percentage of infested mussels per site and regarding their intensity (I) as mean number of parasites per mussel and side. Results are represented as means from four conducted sampling cycles per site (n ¼ 60) over the 10 months sampling period. 2.3. Assessing lysosomal membrane stability (LMS) Mussels between 30 and 50 mm in length were collected from the sampling sites and immediately transferred dry in cool boxes to the lab. The length and width of 12 mussels were measured to the nearest 0.1 mm using a vernier calliper. The mussels were opened; drained and total wet weight was determined (0.01 g). The soft body was then dissected, weighed (0.01 g), put in cryo-vials each filled with 1 ml fish gelatine and then shock-frozen in liquid nitrogen. Subsequently, shells were weighed (0.01 g). Frozen soft body samples were fixed on pre-cooled aluminium chucks for cryostat-sectioning. Tissue sections of 10 mm were obtained using a cryotome (Microm, HM 500) with chamber temperature of 25 C. Sections were stored for a maximum of 24 h at 20 C until processed for histochemistry. The lysosomal membrane stability test was performed according to Moore et al. (2004). Serial cryostat sections were incubated at 37 C in a 0.1 M citrate buffer, pH 4.5, containing 3% NaCl to destabilize the membrane for increasing time intervals (2e50 min). After this acid labilisation, sections were incubated for 15 min at 37 C in a medium containing the substrate Naphthol AS-BI Nacetyl b-D-glucosamide (Sigma) dissolved in 2-methoxy ethanol and low-viscosity polypeptide (Polypep, Sigma) dissolved in 0.1 M citrate buffer, pH 4.5 with 3% NaCl. The lysosomal hydrolase Nacetyl b-D-hexosamidase catalyses the release of the Naphthol ASBI group which undergoes a post-coupling reaction with the diazonium salt Fast Violet B (Sigma) dissolved in 0.1 M phosphate buffer (pH 7.4) leading to an insoluble bright violet reaction product. Following the colour reaction (10 min), samples were rinsed in running tap water, fixed in Baker’s Formalin, and dried overnight in the dark at room temperature. Subsequently, slights were mounted in Kaiser’s glycerine-gelatine. 2.4. Sectioning and staining for glycogen, lipofuscin and neutral lipid assessment Tissue sections from 12 mussels were used for the assessment of glycogen, lipofuscin and neutral lipids. Sections were obtained using the above described microtome. To determine glycogen quantities duplicate sections were stained using the Perjod-AcidSchiff (PAS) method (modified after Culling, 1974). For this purpose, sections were fixed overnight in 10% formalin before being placed for two hours in an aldehyde blocking solution (2% sodium chlorite in 6% acetic acid). Slides were washed in running tap water for 10 min, rinsed in distilled water for 2 min, placed in 1% periodic acid for 10 min and rinsed again in distilled water for 5 min. The sections were counterstained in Schiff’s reagent for 8 min thereafter, bleached in sulphurous acid for 2 min and washed again in distilled water for 5 min. After a few minutes of drying slides were mounted in Euparal. The accumulation of neutral lipids in cells of the digestive gland was determined using the Oil-Red-O method modified after Lillie and Ashburn (1943). Duplicated sections were fixated in Baker’s Formalin for 15 min, dipped three times in distilled water before washed in 60% Triethylphosphate. Sections were then stained for

15 min in Oil-Red-O solution (1% Oil Red O, 60% Triethylphosphate, pre-cooked for 5 min and filtered 1 time hot and 1 time cold). Stained sections were rinsed again for 30 s in 60% Triethylphosphate before being washed with distilled water. Sections were dried for a short period of time and mounted using Kaiser’s glycerine-gelatine. Lipofuscin accumulation in the lysosomes was determined using the Schmorl reaction modified after Pearse (1985). Duplicate cryostat sections (10 mm) of the digestive gland were fixed for 15 min in 4% Baker’s formalin then rinsed in distilled water before stained in a 1% hydrous ferric chloride/potassium ferricyanide (1:1) solution. Tissues sections were stained for 20 s, washed in 1% acetic acid for 2 min, rinsed under tap water for 10 min and finally rinsed 3 times in distilled water. After a short drying period, sections were mounted in Histomount. The optimal staining duration was preassessed by a time series controlled with the light microscope to avoid staining of tissue background. 2.5. Determination of sex and gonadal status To assess the sex and gonadal status of the investigated mussels, cryo-sections of 12 mussels were obtained as described above and stained in Gill’s Hematoxylin and counterstained using an Eosine Phloxin solution. Tissue sections were then fixed in Baker’s Formalin for 5 min, rinsed in distilled water, stained for 15 s in Gills hematoxylin, washed under tap water for 20 min before counterstained for 30 s in EosinePhloxin solution. Subsequently, sections were dipped 5 times in 80% ethanol before they were dried briefly and mounted in Euparal. 2.6. Microscopic image analysis To assess the lysosomal membrane stability, the maximum reaction product for N-acetyl b-D-hexosaminidase was determined by automatic measurement of the number and percentage of darkly stained lysosomes in the digestive tubules using computer assisted image analysis (Zeiss, KS300) combined with a light microscope (Zeiss, Axioskop) at 400 fold magnification. For contrast enhancement a green filter was applied. The time period needed to destabilise the membranes is represented by the maximum staining intensity in the lysosomes represented by a “peak”. In most cases two peaks of maximum staining intensity are visible. These peaks represent different membrane properties of two sub-populations of lysosomes (Moore et al., 2004). Usually the two peaks correspond to each other and early first and second peaks indicate low stability of the lysosomal membranes and vice versa. Low membrane stabilities provide evidence for pre-damage caused by stress during the end-of-life stages of the investigated organism. According to Moore et al. (2004) only the first peak is used to determine the labilisation period and functions as a baseline or reference value. Tissue sections stained with the Schmorl’s and Perjod-AcidSchiff (PAS) method were quantitatively and objectively assessed for lipofuscin content also using computer assisted image analysis. The image analysis consisted of the above-described microscope and camera and the software KS300 (Version 3.0, ZEISS). Staining intensity was measured using the same macro applied for LMS assessment. For both lipofuscin and glycogen assessment three black and white images were taken from each duplicate section of the digestive gland tissue (6 measurements per individual) at 400 fold magnification. Similarly to the LMS analysis a green filter for contrast enhancement was applied for PAS quantification. Neutral lipids were assessed semi-quantitatively using the same microscope (Axioscope, ZEISS) with camera (MRc, ZEISS) coupled to a computer equipped with the software AxioVision (Version 4.6.3.0, ZEISS). Images were taken at 400 fold magnification and classified


M. Brenner et al. / Marine Environmental Research xxx (2013) 1e15 Table 1 Data of tidal status high water (HW), low water (LW), salinity [g/L] and temperature [ C] measured monthly at the areas RS and JD during sampling season between March and October. RS

Tide

Salinity [g/L]

Temperature [ C]

Mar Apr May Jun Jul Aug Oct JD Mar Apr May Jun Jul Aug Oct

HW LW HW LW LW LW HW

28.6 31.5 32.1 23.1 30.2 29.4 31.6

7.4 12.2 16.6 18.5 21.1 19.7 13.1

HW HW HW HW HW HW HW

29.3 29.1 31.1 31.4 30.7 30.5 29.3

7.3 10.3 13.5 18.4 20.4 19.5 10.6

to 8 different categories of accumulation (0 ¼ no staining reaction, 7 ¼ maximum staining reaction). Sex and gonadal status were determined using the above mentioned light microscope and camera. Gonadal status was described according to Guillou et al. (1990) and differentiated into 3 categories: (1) recovering (non-discriminable), (2) premature (discriminable) and (3) mature gonads (discriminable). Numbers of individuals assigned to a specific category were count and their percentage displayed.

2.7. Statistical analysis Means and standard deviations (mean SD) of data of the macro parasite intensities and condition indices were calculated by using Microsoft Office Excel 2007. Box plots displaying LMS were calculated using Statistica 9.0 software. Differences of data on histopathology between sites and season were tested for significances with the software JMP (Version 7.0, SAS Institute Inc.). Data were

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analysed using a KruskaleWallis ANOVA on ranks with a Dunn’s test as post-hoc test. The significance level was set at p < 0.05. However, if higher significance levels (p < 0.01 and 0.001) were achieved, levels were presented separately. The linear discriminant analysis (LDA) and the principal component analysis (PCA) were conducted using R (Version 2.15.0, The R Foundation for Statistical Computing). PCA was applied for the interpretation of data variability (Reid and Spencer, 2009). It is widely used to rotate and project data into subspace of variates of reduced dimensionality. Reducing the data to dominant components or factors is achieved by suppressing parts of the total variance in the data and results in a more interpretable output for exploratory purposes (Praveena et al., 2012). In addition, linear discriminant analysis (LDA) was used to evaluate the influence of the quality of a particular habitat and its distance to shore on the grouping of data into classes. This analysis computes a linear projection for one or more predictors and yields a new set of transformed data for grouping data according to classes (Wang and Mizaikoff, 2008) without dimensional reduction. 3. Results 3.1. Environmental conditions Salinity and temperature were measured at JD and RS. Values for both sampling sites were comparable over the 10 month sampling period (Table 1). Salinity varied moderately between 28 and 32 ppt at RS (LW and HW) and between 29 and 31 ppt at JD (only HW). At both sites, temperature ranged between 7 C in winter and 21 C in summer, whereas the nearshore area JD remained slightly cooler throughout the year compared to the offshore area RS. More detailed results concerning the environmental conditions are presented in Brenner et al. (2012). 3.2. Condition index Low CIF were found throughout the year with only moderate differences at the intertidal areas (CIF 1.95 0.35 to 2.84 0.41)

Fig. 2. Fulton’s condition indices [CI] of blue mussels from five different sampling sites NH [black], SY [dark grey], BS [grey], JD [light grey] and RS [white] in the German Bight (n ¼ 12 per site and season).


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Table 2 Prevalence (P) [%] and intensity (I) [n/ind.] of 4 main parasites species (M. intestinalis, R. roscovita, H. elongata and P. ciliata) found in blue mussel as well as main target organs or tissues for parasite infestation [%] according to five sampling sites (n ¼ 60 per site) in the German Bight. M. intestinalis P [%] Infested Mussels NH SY BS JD RS Infested organs Digestive gland Gills/Palps Foot Muscle Shell

29.0 89.8 38.2 20.4 0.0 100.0 0.0 0.0 0.0 0.0

R. roscovita

H. eleongata

P. ciliata

I [n/ind.]

P [%]

I [n/ind.]

P [%]

I [n/ind.]

100.0 100.0 40.5 0.0 0.0

121.7 202.8 169.5 284.8 4.3 12.3 0.0 0.0

76.3 32.7 5.6 0.0 0.0

16.2 37.6 1.3 3.4 0.2 0.9 0.0 0.0

0.5 4.0 2.9 0.3 0.0

1.0 3.2 2.3 0.6

59.0 35.0 3.0 3.0 0.0

and at the nearshore area JD (CIF 1.77 0.29 to 2.82 0.31) (Fig. 2). A notable increase in the CIF-values was registered only between spring (CIF 2.89 0.40) and summer (CIF 4.36 1.05) at the offshore site RS (Fig. 2). Summer values at RS were significantly higher than all other CI measured (Fig. 2).

6.0 1.0 78.0 15.0 0.0

P [%] 7.9 57.1 20.2 2.0 0.0

I [n/ind.] 0.1 0.3 3.6 6.1 0.3 0.7 0.02 0.0

0.0 0.0 0.0 0.0 100.0

3.3. Parasite assessment More than 99% of the parasites found in the tissues and organs of Mytilus edulis belonged to four different native species (Krakau et al., 2006): juveniles and adults of the copepod Mytilicola

Fig. 3. Images of hematoxylin-eosin stained mussel gonads tissue samples used to determine sex and gonadal status. Examples show post spawning/recovering (a/b), growing/ premature male (c) and female (d), and mature male (e) and female (f) gonads.



intestinalis in the gut, two trematod species Renicula roscovita and Himastla elongata occurring as metacercarias in the gills, mouth palps and tubuli of the digestive gland or in the foot and other muscles, respectively and the polychaet Polidora ciliata living in self-drilled ducts of the shell of mussels (Table 2). With the deployed sampling method (using a glass compressorium under a stereo magnifying glass) only juvenile or adult M. intestinalis were found in the digestive gland. The most common parasites showed a high prevalence of up to 100% at the three intertidal areas, while at the JD site (nearshore) the parasites found were M. intestinalis and P. ciliata only, with prevalence of 20 and 2%, respectively. No parasites were found offshore in the mussels from the RS site (Table 2). Comparable to the prevalence, also the intensities of parasites were much higher at the intertidal areas than at the nearshore site JD. Highest values were observed at SY for M. intestinalis, R. roscovita and P. ciliata occurring with 4.0 3.2, 169.5 284.8 and 3.6 6.1 individuals per mussel, respectively. At SY also mass infestations of R. roscovita with values above 1000 metacercarias per mussel occurred. In contrast, at JD the intensities found for the two parasites M. intestinalis and P. ciliata showed fairly low values of 20.4 and 0.02 individuals per mussel (Table 2). Adult M. intestinalis inhabit only the hind gut of the digestive gland, whereas R. roscovita occurred in the tubuli of the digestive gland (59%) and in the gills or pulps (35%) of the mussel. The trematod species H. elongata was found predominantly in the foot of the mussel (78%) and in other muscular tissues (15%) (Table 2). The most infested organ by macro parasites was the digestive gland, where M. intestinalis and R. roscovita were found, mouth palps and gills were infested by R. roscovita and the foot was infested by mainly H. elongata and to a certain extent also R. roscovita (Table 2). All organs and tissues of the investigated samples from all five different sample sites were free of Marteilia refrigens throughout the sampling period.

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ANOVA on ranks with a Dunn’s test as a post-hoc test). Data in Fig. 4 aej were sorted according to site and display the seasonal differences. Intertidal sites (Fig. 4 a/b, c/d, or e/f) did not show significant differences over the sampling period for both peaks. In contrast, at the sampling sites JD and RS the spring and summer values of peak 1differed significantly (p < 0.05) (Fig. 4 g/h and 4 i/j). Values for peak 2; however, did not differ during the same period at JD and RS. Both submerged sampling sites displayed comparable trends for peak 1 and 2 showing the lowest labilisation values in spring followed by an increase in summer. Autumn and winter samples at both sites stayed mostly stable on intermediate levels. The two intertidal sites NH and SY showed contrasting trends with higher values for peak 1 and 2 in spring followed by a decline of values in summer for both peaks. BS was comparable for both of the submerged sites, however, only for values for peak 1. Further, results were also tested for site specific differences. Here, only one significant difference was detected in the summer between peak 1 of NH and JD (p < 0.01). 3.6. Glycogen quantification The results of the quantification of glycogen in cells of the digestive gland are displayed in Fig. 5 aed. Throughout the year, mussels from all sampling sites showed high glycogen concentrations. Highest values, except for autumn at SY (Fig. 5 d) were always found in mussels from the submerged sampling sites. At these sites values were higher for RS in winter and autumn whereas JD showed the highest values in spring and summer. Values from the three intertidal sites were more variable. Significant differences were detected between SY vs. JD, SY vs. RS and SY vs. NH in spring (Fig. 5 b) and between SY vs. NH (p < 0.05) and SY vs. BS in autumn (p < 0.05) (KruskaleWallis ANOVA on ranks with a Dunn’s test as a post-hoc test) (Fig. 5 d). 3.7. Lipofuscin assessment

3.4. Sex determination and gonadal status Determining sex using the hematoxylin-eosin method was limited to individuals showing mature or at least growing or premature gonads (see examples shown in Fig. 3). The sex of individuals recovering from spawning or having early development stages of gonads was not clearly distinguishable. In winter 100% of near- and offshore mussels from JD and RS were sexually distinguishable, whereas mussels from the intertidal areas still had high proportions of recovering gonads, varying between 67% at NH,17% at SYand 8% at BS (Table 3). Submerged mussels differ in the proportion of growing and mature individuals. At JD 100% of the mussel gonads were still growing whereas at RS 17% were already mature (Table 3). In spring all mussels NH, SY, JD and RS were sexually distinguishable and mature. Only at BS 8% of mussel gonads were still recovering and another 25% still growing. In summer between 70% at NH, 80% at SY and 100% at BS of intertidal mussel had spawned and were in a phase of recovery (Table 3). In contrast, at the submerged areas 60% at JD and 90% at RS were already in a post-recovery stage and gonads were growing again. In autumn 40% at NH, 50% at BS and 78% at SY of the mussel gonads were sexually distinguishable and in a growing status (Table 3). Submerged growing mussels showed higher values with 75 at JD and 70% of growing and sexually distinguishable gonads. However, mussel with mature gonads were still absent at all sites in autumn (Table 3). 3.5. Lysosomal membrane stability (LMS) The results of the LMS-analysis for the five sampling locations were tested for site and seasonal differences (KruskaleWallis

Results for concentrations of lysosomal lipofuscin taken from the tissue of the digestive glands of the blue mussels are displayed Table 3 Sex determination [%] and gonadal status [%] assessed from blue mussels sampled at four consecutive sampling cycles within the German Bight (n ¼ 12 per siteeseason combination). Season

Winter NH SY BS JD RS Spring NH SY BS JD RS Summer NH SY BS JD RS Autumn NH SY BS JD RS

Sex [%]

Gonadal status [%]

Female

Male

n.d.

Recovering

Premature

Mature

8 17 50 25 42

25 67 42 75 58

67 17 8 0 0

67 8 8 0 0

33 83 92 100 83

0 8 0 0 17

42 50 42 33 75

58 50 50 67 25

0 0 8 0 0

0 0 8 0 0

0 0 25 0 0

100 100 67 100 100

10 10 0 20 50

20 10 0 40 40

70 80 100 40 10

70 80 100 40 10

30 20 0 60 90

0 0 0 0 0

10 22 10 38 0

30 56 40 38 70

60 22 50 25 30

60 22 50 25 30

40 78 50 75 70

0 0 0 0 0


8


Fig. 4. aej: Box-Whisker plots of LMS values for NH peak 1 (a) and 2 (b), SY peak 1 (c) and 2 (d), BS peak 1 (e) and 2 (f), JD peak 1 (g) and 2 (h), and RS peak 1 (i) and 2 (j) assessed from mussels sampled at four consecutive sampling cycles within the German Bight. Differences for peak 1 between spring and summer at JD (g) and RS (i) are significant (Kruskale Wallis ANOVA on ranks, Dunn’s test as post-hoc test, p < 0.05).



9

Fig. 5. aed: Box-Whisker plots for glycogen concentrations in cells of the digestive gland, displayed in mean area % of winter (a), spring (b), summer (c) and autumn (d) samples from five different sites (NH, SY, BS JD and RS) of the German Bight. Differences between SY vs. JD (p < 0.01), SY vs. RS (p < 0.001) and SY vs. NH (p < 0.05) in summer (c) and between SY vs. NH (p < 0.05) and SY vs. BS in autumn (p < 0.05) (d) are significant (KruskaleWallis ANOVA on ranks, Dunn’s test as post-hoc test).

in Fig. 6 aed. Lowest values with highest variances especially for intertidal mussels were detected in winter. Values for all sampling sites increased in spring and summer and showed a slight decrease in autumn. Values from the submerged sampling sites were already high in winter and varied throughout the year only slightly on a high level. In particular the offshore area RS showed extremely high values with low variances. Sites were tested for differences using a KruskaleWallis ANOVA on ranks combined with a Dunn’s test as a post-hoc test. Significant differences were detected in winter between RS vs. SY (p < 0.001), RS vs. NH (p < 0.01) and BS vs. SY (p < 0.01) (Fig. 6 a). In spring values differed significantly between RS vs. JD (p < 0.05), RS vs. SY (p < 0.01), RS vs. NH (p < 0.001) and BS vs. NH (p < 0.05) (Fig 6 b). The analysis in summer resulted in significant differences for RS vs. JD (p < 0.001), RS vs. BS (p < 0.001), RS vs. NH (p < 0.05), JD vs. SY (p < 0.001) and BS vs. SY (p < 0.05) (Fig. 6 c). In autumn significant differences were detected between NH vs. SY (p < 0.01), NH vs. BS (p < 0.01) and NH vs. JD (p < 0.05) (Fig. 6 d). 3.8. Neutral lipids assessment The results of the semi quantitative assessment of the lysosomal accumulations of neutral lipids are displayed in Fig. 7 aed. There was a strong and continuous increase in the accumulation values at all sites over the sampling period from winter to autumn (p < 0.001). Similarly to the glycogen and lipofuscin assessment values, neutral lipids were highest at the submerged sampling

areas with an all year maximum at RS. Differences between sites (KruskaleWallis ANOVA on ranks with a Dunn’s test as a post-hoc test) were significant at RS vs. JD (p < 0.05) and RS vs. BS (p < 0.01) in winter (Fig. 7 a). In spring (Fig. 7 b) significantly different values were detected between RS vs. BS (p < 0.001), RS vs. SY (p < 0.01) and JD vs. BS (p < 0.05). Summer (Fig. 7 c) values differed significantly between RS vs. BS (p < 0.001), RS vs. SY (p < 0.001), JD vs. BS (p < 0.01) and JD vs. SY (p < 0.01). In autumn (Fig. 7 d) both submerged sites differed significantly from all intertidal sites (p < 0.01 from BS and p < 0.05 from NH and SY). 3.9. Multivariate data analyses The parameters season, LMS peak 1, LMS peak 2, sex, gonad status, and glycogen-, lipofuscin- and neutral lipid content were included in the multivariate analyses to examine the associations between biomarkers and seasons. Results on parasite infestation rates were not included in the evaluation since they were conducted using other individuals from sub samples. The bi-plot of the principal component analysis (PCA) (Fig. 8) showed that sampling sites cannot be clearly separated using the approach. The prevailing principal component 1 (PC1, x-axis, Fig. 8) explained 28.69% and the minor principal component 2, 19.12% (PC2, y-axis, Fig. 8) of the variability. Together, they explained 47.82% of the data variability (Fig. 8). Resulting correlation axes showed that the two factors “season” and increased concentrations of “neutral lipids” distinguished most markedly the differences between sampling periods.


10


Fig. 6. aed: Box-Whisker plots for lysosomal lipofuscin concentrations in cells of the digestive gland in mean area % of winter (a), spring (b), summer (c) and autumn (d) samples from five different sites (NH, SY, BS JD and RS) of the German Bight. Significant differences were detected in winter between RS vs. SY (p < 0.001), RS vs. NH (p < 0.01) and BS vs. SY (p < 0.01) (a). In spring values differed significantly between RS vs. JD (p < 0.05), RS vs. SY (p < 0.01), RS vs. NH (p < 0.001) and BS vs. NH (P < 0.05) (b). The analysis in summer resulted in significant differences for RS vs. JD (p < 0.001), RS vs. BS (p < 0.001), RS vs. NH (p < 0.05), JD vs. SY (p < 0.001) and BS vs. SY (p < 0.05) (c). In autumn significant differences were detected between NH vs. SY (p < 0.01), NH vs. BS (p < 0.01) and NH vs. JD (p < 0.05) (KruskaleWallis ANOVA on ranks, Dunn’s test as post-hoc test).

However, as only about 48% of the total variance is explained in the first two principal components, the latter finding remains vague. As shown by the example of the offshore sampling site RS these two co-variates and to a certain extent the condition index (CI) separated the samples into two distinct groups (Fig. 8, see violet eclipses). The left eclipse shows individuals with low numbers representing samples from the winter and spring of the sampling period and on the right, samples are displayed with high numbers representing individuals sampled in summer or autumn of the year. Other correlations than the positive ones between “LMS peak 1” vs. “LMS peak 2” and “season” vs. “neutral lipids” were not found to be consistent according to length and angle of the associated axes. In a second step, data were standardized and pooled according to habitat conditions of intertidal (BS, NH and SY) mussels and mussels living permanently submerged at RS and JD. A linear discriminant analysis (LDA) was conducted to predict the percentage of correct classifications over the sampling season. This method enables an estimation of the separability of samples by a linear transformation of the covariates, circumventing problems of dimensional reduction as they appear in the PCA. Data of all parameters measured, except for parasites, were pooled for both the intertidal and hanging-submerged mussel samples. The results (Table 4) showed that there was a fraction of up to 90% (in summer) in which individuals were classified correctly if the samples were pooled only according to habitat conditions (intertidal/submerged)

as a class variable. This ratio increased by up to 100% (also in summer) if samples were finally pooled according to the distance of the sampling site to the shore, resulting in a nearshore group (BS, NH, SY and JD) and an offshore group (consisting only of samples from RS) (Table 4). Pooled samples were also tested according to the main factors influencing the LDA classification. Results showed that, similarly to the PCA, the factors such as “CI” and “neutral lipids” supported the classifications (Table 4). In addition, winter samples of both pooled treatment groups also showed mussels with increased concentrations of lysosomal lipofuscin. 4. Discussion In this integrative study several biomarkers for chemical-, reproductive-, parasitic-, nutritional- and habitat-induced stress were applied to assess the health of blue mussels to classify marine sites according to their environmental quality. Using this approach, significant season- and habitat-related impacts on mussel health became evident. Particularly in temperate climate zones aquatic organisms, including mussels, experience seasonal cycles in which their physiology and reproductive abilities are considerably altered, leading to variable response profiles if individuals are sampled at different times of the year (Bignell et al., 2008). Alterations are most



11

Fig. 7. aed: Box-Whisker plots for neutral lipid concentrations in cells of the digestive gland in categories (0 ¼ no neutral lipids and 7 ¼ maximum concentration of neutral lipids) of winter (a), spring (b), summer (c) and autumn (d) samples from five different sites (NH, SY, BS JD and RS) of the German Bight. Differences between sites were significant at RS vs. JD (p < 0.05) and RS vs. BS (p < 0.01) in winter (a). In spring (b) significantly different values were detected between RS vs. BS (p < 0.001), RS vs. SY (p < 0.01) and JD vs. BS (p < 0.05). Summer (c) values differed significantly between RS vs. BS (p < 0.001), RS vs. SY (p < 0.001), JD vs. BS (p < 0.01) and JD vs. SY (p < 0.01). In autumn (d) both testing submerged sites differed significantly from all intertidal sites (p < 0.01 from BS and p < 0.05 from NH and SY) (KruskaleWallis ANOVA on ranks, Dunn’s test as post-hoc test).

pronounced during or after periods of reproduction when energy reserves are exhausted, easily leading to misinterpretation of the biomarkers for nutritional status such as the condition index or glycogen concentration. In this study, mussel gonads from the two submerged sites were observed to grow more quickly and reach maturity earlier in the year. Mussels from the offshore site were somewhat mature already by winter, whereas sexual organs from the intertidal mussels remained more immature. Therefore, all submerged mussels could be sexually determined by winter or spring. In contrast at the intertidal sites, up to 65% of the samples remained sexually indistinguishable by winter. However, this changed rapidly by spring when most of the samples could be distinguished due to fast maturation. In summer mussels from all sampling locations had spawned and gonads were recovering or were once again in an early growing status. This observation corresponds with a significant decrease in the lysosomal membrane stability especially at the submerged sites, most probably induced by the energy consuming reproduction process. Applying multivariate analyses, the parameter ‘season’ was identified as the major distinguishing factor separating the data. Seasonal effects were more prominent at the sampling areas where mussels lived permanently submerged. The main parameter in turn influencing seasonal impact was the drastic increase of neutral lipids at all sites over the sampling season. This effect was accompanied by a summer increase of the condition index at

the offshore site intensifying the separating effect of neutral lipids. A possible explanation for higher concentrations of metabolic end products, such as liposfuscin and neutral lipids, in the submerged mussels could be the permanent feeding mode (leading to higher ingestion of food and associated chemical contaminants). In contrast, intertidal mussels in the German Bight have only half as much time to feed and within this time must rest from feeding which is necessary for digestion, repair and detoxification processes. Using the linear description analysis (LDA) an affiliation of samples to one of these groups could be correctly predicted with a fraction of up to 90%. This prediction percentage was even higher when samples were pooled according to the distance of the respective sampling site to the shore. Here, a correct prediction of up to 100% in summer was possible. This highlights the influence of habitat conditions on biomarker response. Interestingly, in this study the values for lysosomal membrane stability did not consistently correlate with glycogen, lipofuscin or with neutral lipid accumulations during the sampling period as has been suggested by other authors. According to Krishnakumar et al. (1994, 1997) there is a clear negative correlation between high concentrations of lipofuscin and neutral lipids and low LMS values in mussels exposed to chemical pollutants, both in field and laboratory experiments. Analogue findings for lipofuscin and LMS have been reported for exposed mussels collected in the field by Aarab et al. (2008) and also for e.g. flatfish collected in the field by


12


Interestingly, the glycogen concentrations did not show notable changes during the year and were relatively high at all sampling sites, indicating a consistently good nutritional status. An expected seasonal-related pattern of glycogen concentrations correlating to a changing food supply was not reflected in the results of the present study and cannot explain the observed differences in the condition indices. In contrast gonad status is negatively or partially negatively correlated, to glycogen and the condition index, suggesting that gonadal maturation is an energy consuming process depleting glycogen storages and even decreasing the condition index. Further, the infestation with parasites, another parameter potentially explaining changes in the condition indices of the mussels, also showed a differentiated pattern. Mussels grown hanging and submerged were essentially free of parasites over the entire year, which was not reflected in a generally higher condition index. In particular mussels from the nearshore sampling area did not differ noticeably from the highly infested intertidal samples. Also, the intertidal samples were, although differently infested by parasites, not distinguishable regarding their condition indices. Further, parasites of several fish species (e.g. Sasal et al., 2007; Pérez-del-Olmo et al., 2009; Carreras-Aubets et al., 2012) and mussels (e.g. Fairley, 1988; Kim et al., 2008; Sures, 2008) are increasingly used as indicators for environmental quality. These studies show that pollution can increase or decrease levels of parasites depending on numerous interacting variables (Sures, 2008). For example, certain contaminants may actually increase the number of parasites by allowing increased propagation by excluding their natural predators, by reducing the resistance of their hosts, or by providing improved living conditions for their intermediate hosts (Kim et al., 2008). Also contaminants may interfere with parasite transmission or proliferation within hosts and thus reduce parasite burden (Lafferty and Kuris, 1999) or in contrast making the hosts more sensitive to certain pollutants, e.g. metals (Pascoe and Cram, 1977; Boyce and Yamada, 1977). In oysters exposure to pesticides and metals revealed an inverse correlation to common parasites. Some species were positively correlated, whereas others were exclusively negatively associated to certain pollutants, thus being potentially useful as biomarkers of anthropogenic pollution (Kim et al., 2008). The results of this study show that caution is recommended when parasites are used as biomarkers in studies on environmental health assessment. Species spectrum and intensities of infestations may vary drastically due to habitat characteristics of the sampling sites, and are not always primarily due to concentrations of pollutants. Federal Maritime Hydrographic Agency (BSH) measurements presented in Brenner et al. (2012) show that chemical organic pollutants and metals are distributed evenly throughout the water column and suspended matter in all of the German Bight. However, the highest chemical concentrations occur offshore at the sampling site Roter Sand. This site is situated at the south-west edge of an

Fig. 8. PCA bi-plot for all measured parameters (without parasites infestation values) separating the samples from the 5 different sampling sites (green BS, blue JD, red NH, violet RS and orange SY) according to principal factors season, neutral lipids and CI. Principal components (PC) explain together 47.82% of the data variability.

Koehler et al. (2002). Findings from Hyötyläinen et al. (2002) in fresh water mussels suggest a diminishing effect of high concentrations of contaminants on the glycogen storage capacities of the investigated organisms. However, in the present study neither the accumulation of neutral lipids nor variable lipofuscin accumulations negatively affected LMS values at the end of the sampling period, nor did high energy reserves such as high glycogen levels improve the lysosomal membrane stability. Furthermore, strong seasonal influences were detected in the comparison of the condition indices. Here, mussels from the offshore site RS showed significantly higher values particularly in spring and summer, clearly separating these samples from all other groups. In the other seasons and site combinations a differentiation and a seasonal trend regarding the condition indices was, however, not observed. Lipofuscin concentrations also showed seasonal trends. Levels varied in winter, but increased in spring and then remained high for the rest of the year. Again this pattern was most prominent in the mussels at the permanently submerged offshore site.

Table 4 Results of LDA predicting the correct classification [%] of pooled sampling sites: submerged vs. intertidal (JD, Rs vs. BS, NH, SY) and nearshore vs. offshore (BS, NH, SY, JD vs. RS). Additional columns show the influence of assessed parameters on correct prediction. LDA [%] corr. classification Submerged vs. intertidal Winter 75 Spring 86 Summer 90 Autumn 80 Nearshore vs. offshore Winter 93 Spring 98 Summer 100 Autumn 85

Condition Index [CI]

Neutral lipids

Glycogen

LMS1

Lipofuscin

Gonad status

0.44 0.32 0.83 0.69

0.33 0.71 0.69 1.03

0.03 0.71 0.56 0.21

0.31 0.18 0.22 0.22

0.80 0.04 0.32 0.15

0.47 0.23 0.28 0.65

0.30 1.05 1.24 0.89

0.73 0.22 0.56 0.60

0.49 0.18 0.24 0.21

0.11 0.08 0.13 0.43

1.02 0.62 0.88 0.12

0.13 0.09 0.03 0.67



intermixing zone of estuarine run-offs of the rivers Weser and Elbe (Brenner et al., 2012). Due to the extensive drainage area of the Elbe, the river is burdened with higher loads of contaminants (BSH, 2005; UBA, 2009), leading to higher pollutant concentrations offshore vs. nearshore. Other monitoring studies have also shown that the southern part of the North Sea, including the German Bight, remains seriously contaminated, with unacceptably high concentration levels of metals and organic pollutants in the sediment and biota, posing health risks to the local communities (OSPAR, 2008). That all the mussels in this study, regardless of habitat location, conditions, and sampling season throughout the German Bight exhibit low LMS values (5e8 min) is evidence that such stress is caused by the relatively high levels of chemical pollutants at all sampling sites. Based on this overall lysosomal response the mussel population must be considered as severely stressed, already exhibiting pathologies as described by Viarengo et al. (2007). The results of this study highlight that mussels although belonging to the same species and living in the same geographic marine area, may differ markedly with respect to physiological parameters if grown suspended in the water column or laying intertidal on the seafloor. In particular mussels grown from the larval stage while hanging in the water column mature much earlier in the year, are essentially free of parasites, yet accumulated more metabolic concentrations of lipofuscin and neutral lipids compared to the intertidal mussels. The hanging mussels also developed a lighter shell, leading to a significantly different shell-bodyweight relationship, thus making comparisons to intertidal mussels less valid if normal formulae are used to calculate condition index. We therefore suggest that for mussels displaying a linear growth, a lengtheweight relationship to calculate condition index should be used to equalize these differences. Some of these findings might also apply to transplanted mussels. If mussels are collected from intertidal areas and are transplanted for use in a caging experiment for example, they most probably do not get rid of parasites or develop a lighter shell, yet may change their metabolic rates due to permanent inundation. A similar pattern may arise if transplanted mussels are compared to mussels grown wild in the water column at poles or buoys. These mussels may have similar metabolic rates, since they have grown under the same conditions; however, wild mussels will probably have lighter shells and no parasites, compared to the transplanted individuals. These scenarios may cause variable biomarker responses although both groups are exposed to identical environmental stress. Similar metabolic shifts resulting in significantly different biomarker results have been reported in previous studies (Lüdeking, 2004). In the Lüdeking study changes in temperature, salinity, oxygen, and food availability in coastal areas caused differences in gene expression and consequently in the ability of mussels to cope with chemical pollutants. The shift in maturation times demonstrated here should also be taken into account when designing a monitoring strategy to avoid a misleading comparison of individuals at different maturity stages. In the present study we analysed only a spectrum of relevant physiological differences between intertidal and submerged mussels. However, this phenomenon should be considered carefully in the design of monitoring programmes, especially for larger marine areas, in order to produce reliable data sets of biomarker responses for the identification of environmental stressors. Therefore we suggest, as a conceptual framework for large scale environmental monitoring ranging from coastal to offshore areas where both submerged and intertidal mussels are sampled, a splitting strategy into subgroups of individuals exposed to similar biotic and abiotic conditions.

13

Acknowledgements The authors are grateful to the Water and Shipping Authority (WSA) of Bremerhaven and Wilhelmshaven, Germany, the State Fishery Authority of Bremerhaven, Germany, and the Fishermen of Lower Saxony, Germany, who generously allowed the use of the offshore test field in the Weser estuary near the lighthouse Roter Sand for the mussel experiments. The authors are especially thankful to the WSA, who organized modification, shipping and anchoring of the buoyancy at Roter Sand. Further, we would like to thank the Niedersachsen Port Authorities (NPorts) of Wilhelmshaven, Germany, who generously allowed us to use the cargo bridge at Jade Bay for growing nearshore mussels on collector ropes. Special thanks go also to Captain Charly Lürs and his crew of the AWI research vessel FK Uthörn who guided and supported us during every ship excursion. The sampling at the offshore site Roter Sand would not have been possible without the help of the AWI research diving group who spent much time and energy to obtain the mussels for all the analyses. This work was conducted as part of the project FV 168 MytiFit financed by the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany, and the Ministry of Construction, Environment, Traffic and Europe of the Federal State of Bremen, Bremen, Germany.

References Ansaldo, M., Nahabedian, D.E., Holmes-Brown, E., Agote, M., Ansay, C.V., Guerrero, N.R.V., Wider, E.A., 2006. Potential use of glycogen level as biomarker of chemical stress in Biomphalaria glabrata. Toxicology 224 (1e2), 119e127. Aarab, N., Pampanin, D.M., Nævdal, A., Øysæd, K.B., Gastaldi, L., Bechmann, R.K., 2008. Histopathology alterations and histochemistry measurements in mussel, Mytilus edulis collected offshore from an aluminium smelter industry (Norway). Mar. Pollut. Bull. 57, 569e574. Aarab, N., Godal, B.F., Bechmann, R.K., 2011. Seasonal variation of histopathological and histochemical markers of PAH exposure in blue mussel (Mytilus edulis L.). Mar. Environ. Res. 71, 213e217. Au, D.W.T., 2004. The application of histo-cytopathological biomarkers in marine pollution monitoring: a review. Mar. Pollut. Bull. 48, 817e834. Au, D.W.T., Wu, R.S.S., Zhou, B.S., Lam, P.K.S., 1999. Relationship between ultrastructural changes and EROD activities in liver of fish exposed to benzo[a] pyrene. Environ. Pollut. 104, 235e247. _ J., Lehtonen, K.K., Koehler, A., Broeg, K., Vuorinen, P.J., Lang, T., Barsiene, _ J., Dedonyte, V., Rybakovas, A., Repe Pempkowiak, J., syvokiene, cka, R., Vuontisjärvi, H., Kopecka, J., 2006. Biomarker responses in flounder (Platichthys _ tinge_ area (Baltic Sea). flesus) and mussel (Mytilus edulis) in the Klaipeda-B u Mar. Pollut. Bull. 53, 422e436. Bignell, J.P., Dodge, M.J., Feist, S.W., Lyons, B., Martin, P.D., Taylor, N.G.H., Stone, D., Travalent, L., Stentiford, G.D., 2008. Mussel histopathology: effects of season, disease and species. Aquat. Biol. 2, 1e15. Boren, J., Brindle, K.M., 2012. Apoptosis-induced mitochondrial dysfunction causes cytoplasmic lipid droplet formation. Cell Death Differ. 19, 1561e1570. Boyce, N.P., Yamada, S.B., 1977. Effects of a parasite, Eubothrium salvelini (Cestoda: Pseudophyllidea), on the resistance of juvenile sockeye salmon, Oncorhynchus nerka, to zinc. J. Fish Res. Board Can. 34, 706e709. Brenner, M., Ramdohr, S., Effkemann, S., Stede, M., 2009. Key parameters consumption suitability of offshore cultivated blue mussels (Mytilus edulis L.) in the German Bight. Euro. Food Res. Technol. 230, 255e267. Brenner, M., Buchholz, C., Heemken, O., Buck, B.H., Köhler, A., 2012. Health and growth performance of the blue mussel (Mytilus edulis L.) from two hanging cultivation sites in the German Bight: a nearshore e offshore comparison. Aquac. Int. 20 (4), 751e778. BSH, 2005. System Nordsee e Zustand 2005 im Kontext langzeitlicher Entwicklung. Federal Maritime Hydrographic Agency, Hamburg, Germany, p. 270. Report, 44. Buck, B.H., Thieltges, D.W., Walter, U., Nehls, G., Rosenthal, H., 2005. Inshoreoffshore comparison of parasite infestation in Mytilus edulis: implications for open ocean aquaculture. J. Appl. Ichthyol. 21 (2), 107e113. Cajaraville, M.P., Díez, G., Marigómez, J.A., Angulo, E., 1990. Responses of basophilic cells of the digestive gland of mussels to petroleum hydrocarbon exposure. Dis. Aquat. Org. 9, 221e228. Cajaraville, M.P., Robledo, Y., Etxeberria, M., Marigómez, I., 1995. Cellular biomarkers as useful tools in the biological monitoring of environmental pollution: molluscan digestive lysosomes. In: Cajaraville, M.P. (Ed.), Cell Biology in Environmental Toxicology. University of the Basque Country Press Service, Bilbo, pp. 29e55. Cajaraville, M.P., Cancio, I., Orbea, A., Lekube, X., Marigómez, I., 1998. Detection, control and monitoring of pollution using early warning cellular biomarkers:


14


conventional and innovative approaches based on biotechnology. Cuad. Investig. Biol. 20, 545e548. Cajaraville, M.P., Bebianno, M.J., Blasco, J., Porte, C., Sarasquete, C., Viarengo, A., 2000. The use of biomarkers to assess the impact of pollution in coastal environments of the Iberian peninsula: a practical approach. Sci. Total Environ. 247, 295e311. Carreras-Aubets, M., Montero, F.E., Kostadinova, A., Carrassón, M., 2012. Parasite communities in the red mullet, Mullus barbatus L., respond to small-scale variation in the levels of polychlorinated biphenyls in the Western Mediterranean. Mar. Pollut. Bull. 64, 1853e1860. Culling, C.F.A., 1974. Handbook of Histopathological and Histochemical Techniques (Including Museum Techniques), third ed. Butterworth, London. Dethlefsen, V., 1970. On the Parasitology of Mytilus edulis (L. 1758). International Council for the Exploration of the sea (ICES), Hamburg, p. 11. C.M. 1970/K: 16. Dethlefsen, V., 1972. Zur Parasitologie der Miesmuschel (Mytilus edulis L., 1758). Berichte der Deutsch. wiss. Komm. für Meeresforsch. 22, 344e371. Dondero, F., Dagnino, A., Jonsson, H., Caprὶ, F., Gastaldi, L., Viarengo, A., 2006. Assessing the occurrence of a stress syndrome in mussels (Mytilus edulis) using a combined biomarker/gene expression approach. Aquat. Toxicol. 78S, 13e24. Einsporn, S., Köhler, A., 2008a. Immuno-localizations (GSSP) of subcellular accumulation sites of phenanthrene, aroclor 1254 and lead (Pb) in relation to cytopathologies in the gills and digestive gland of the mussel Mytilus edulis. Mar. Environ. Res. 66 (1), 185e186. Einsporn, S., Köhler, A., 2008b. Electron-microscopic localization of lipophilic chemicals by an antibody-based detection system using the blue mussel Mytilus edulis as a model system. Environ. Toxicol. Chem. 27 (3), 554e560. Ernst, W., Weigelt, S., Rosenthal, H., Hansen, P.D., 1991. Testing bioconcentration of organic chemicals with the common mussel (Mytilus edulis). In: Nagel, L., Loskill, R. (Eds.), Bioaccumulation in Aquatic Systems: Contributions to the Assessment. Proceeding of International Workshop, Berlin, 1990. VCH-Verlag, Weinheim, New York, Basel, Cambridge, pp. 99e131. Fairley, A., 1988. A Computerized Coding system for organs, tissues, Lesions, and parasites of bivalve Mollusks and its application in pollution monitoring with Mytilus edulis. Mar. Environ. Res. 24, 243e249. Ferreira, A., Dolder, H., 2003. Cytochemical study of spermiogenesis and mature spermatozoa in the lizard Tropidurus itambere (Reptilia, Squamata). Acta Histochem. 105, 339e352. Fulton, T.W., 1904. The rate of growth of fishes. Fish Board Scotl. Annu. Report 22, 141e241. Goldberg, E.D., 1975. The Mussel Watch e a first step in global marine monitoring. Mar. Pollut. Bull. 6, 111. Guillou, J., Bachelet, G., Desprez, M., Ducrotoy, J.P., Madani, I., Rybarczyk, H., Sauriau, P.G., Sylvand, B., Elkaim, B., Glemarec, M., 1990. Les modalités de la reproduction de la coque (Cerastoderma edule) sur le littoral français de la Manche et de l’Atlantique. Aquat. Living Resour. 3, 29e41. Heincke, F., 1908. Bericht über die Untersuchungen der Biologischen Anstalt auf Helgoland zur Naturgeschichte der Nutzfische, pp. 67e155. Die Beteiligung Deutschlands an der Internationalen Meeresforschung 4/5. Hyötyläinen, T., Karels, A., Oikari, A., 2002. Assessment of bioavailability and effects of chemicals due to remediation actions with caging mussels (Anodonta anatina) at a creosote-contaminated lake sediment site. Water Res. 36, 4497e4504. ICES, 2006. Report of the Working Group on Biological Effects of Contaminants (WGBEC), 27. e 31. March 2006, p. 79. Copenhagen, Denmark. ICES CM 2006/ MHC: 04. Kim, Y., Powell, E.N., Wade, T.L., Presley, B.J., 2008. Relationship of parasites and pathologies to contaminant body burden in sentinel bivalves: NOAA Status and Trends ‘Mussel Watch’ Program. Mar. Environ. Res. 65, 101e127. Koehler, A., Wahl, E., Soeffker, K., 2002. Functional and morphological changes of lysosomes as prognostic biomarkers of toxic liver injury in a marine flatfish (Platichthys flesus L.). Environ. Toxicol. Chem. 21 (11), 2434e2444. Koehler, A., Marx, U., Broeg, K., Bahns, S., Bressling, J., 2008. Effects of nanoparticles in Mytilus edulis gills and hepatopancreas e a new threat to marine life? Mar. Environ. Res. 66, 12e14. Krakau, M., Thieltges, D.W., Reise, K., 2006. Native parasites adopt introduced bivalves of the North Sea. Biol. Invasion 8, 919e925. Krishnakumar, P.K., Casillas, E., Varanasi, U., 1994. Effect of environmental contaminants on the health of Mytilus edulis from Puget Sound, Washington, USA. 1. Cytochemical measures of lysosomal responses in the digestive cells using automatic image analysis. Mar. Ecol. Prog. Ser. 106, 249e261. Krishnakumar, P.K., Casillas, E., Varanasi, U., 1997. Cytochemical responses in the digestive tissue of Mytilus edulis complex exposed to microencapsulated PAHs or PCBs. Comp. Biochem. Physiol. Part C Pharmacol. Toxicol. Endocrinol. 118, 11e 18. Lafferty, K.D., Kuris, A.M., 1999. How environmental stress affects the impacts of parasites. Limnol. Oceanogr. 44, 925e931. Lauckner, G., 1983. Diseases of Mollusca: Bivalvia. In: Kinne, O. (Ed.), Diseases of Marine Animals. Introduction, Bivalvia to Scaphopoda. Biologische Anstalt Helgoland/Westholsteinische Verlagsdruckerei Boyens & Co.,, Hamburg/Heide, pp. 477e961. Lepitzki, D.A.W., Scott, M.E., McLaughlin, J.D., 1994. Influence of storage and examination methods on the recovery and size of metacercaria of Cerastoderma edule (L.) from commercial beds of the lower Thames estuary. Z. für Parasitenkd. 56, 1e11. Lillie, R.D., Ashburn, L.L., 1943. Supersaturated solutions of fat stains in dilute isopropanol for demonstration of acute fatty degeneration not shown by Herxheimer’s technique. Arch. Pathol. 36, 432e440.

Livingstone, D.R., García-Martínez, P., Michel, X., Narbonne, J.F., O’Hara, S., Ribera, D., Winston, G.W., 1990. Oxyradical production as a pollution mediated mechanism of toxicity in the common mussel, Mytilus edulis L., and other molluscs. Funct. Ecol. 4, 415e424. Lowe, D.M., Moore, M.N., Clarke, K.R., 1981. Effects of oil in the digestive cells in mussels: quantitative alterations in cellular and lysosomal structure. Aquat. Toxicol. 1, 213e226. Lüdeking, A., 2004. Multi-xenobiotic Resistance (MXR) Transporters and Biotransformation Enzymes in the Blue Mussel Mytilus Edulis. Faculty of Medicine, University of Amsterdam, p. 127 (Dissertation). Marigómez, J.A., Vega, M.M., Carajaville, M.P., Angulo, E., 1989. Quantitative response of the digestive-lysosomal system of winkles to sublethal concentrations of cadmium. Cell Mol. Biol. 35, 555e562. Marigómez, I., Baybay-Villacorta, L., 2003. Pollutant-specific and general lysosomal responses in digestive cells of mussels exposed to model organic chemicals. Aquat. Toxicol. 64, 235e257. Marigómez, I., Soto, M., Cancio, I., Orbea, A., Garmendia, L., Cajaraville, M.P., 2006. Cell and tissue biomarkers in mussel and histopathology in hake and anchovy from Bay of Biscay after the Prestige oil spill (Monitoring Campaign 2003). Mar. Pollut. Bull. 53, 287e304. Marigómez, I., Zorita, I., Izagirre, U., Ortiz-Zarragoitia, M., Navarro, P., Etxebarria, N., Orbea, A., Soto, M., Cajaraville, M.P., 2013. Combined use of native and caged mussels to assess biological effects of pollution through the integrative biomarker approach. Aquat. Toxicol. 136-137, 32e48. Moore, M.N., 1985. Cellular responses to pollutants. Mar. Pollut. Bull. 16 (4), 134e139. Moore, M.N., 1976. Cytochemical demonstration of latency of lysosomal hydrolases in the digestive cells of the common mussel, Mytilus edulis, and changes induced by thermal stress. Cell Tissue Res. 175, 279e287. Moore, M.N., Bubel, A., Lowe, D.M., 1980a. Cytology and cytochemistry of the pericardial gland cells of Mytilus edulis and their lysosomal response to injected horseradish peroxidase and anthracene. J. Mar. Biol. Assoc. U. K. 60, 135e149. Moore, M.N., Koehn, R.K., Bayne, B.L., 1980b. Leucine aminopeptidase (aminopeptidase-I), N-acetyl-hexosaminidase and lysosomes in the mussel, Mytilus edulis L., in response to salinity changes. J. Exp. Zool. 214, 239e249. Moore, M.N., Lowe, D.M., Koehler, A., 2004. Biological Effects of Contaminants: Measurements of Lysosomal Membrane Stability. In: ICES Techniques in Marine Environmental Sciences (TIMES), vol. 36. ICES, Copenhagen, p. 31. Nott, J.A., Moore, M.N., Mavin, L.J., Ryan, K.P., 1985. The fine structure of lysosomal membranes and endoplasmic reticulum in the digestive cells of Mytilus edulis exposed to anthracene and phenanthrene. Mar. Environ. Res. 34, 226e229. OSPAR, 2008. 2006/2007 CEMP Assessment - Trends and Concentrations of Selected Hazardous Substances in the Marine Environment. OSPAR Commission, London, UK, p. 63, 2007. Pascoe, D., Cram, P., 1977. The effect of parasitism on the toxicity of cadmium to the three-spined stickleback, Gasterosteus aculeatus L. J. Fish Biol. 10, 467e472. Patterson, M.A., Parker, B.C., Neves, R.J., 1999. Glycogen concentration in the mantle tissue of freshwater mussels (Bivalvia: Unionidae) during starvation and controlled feeding. Am. Malocol. Bull. 15 (1), 47e50. Pearse, A.G.E., 1985. Histochemistry: Theoretical and Applied. In: Analytical Technology, fourth ed., vol. 2. Churchill Livingstone, Edinburgh, London, Melbourne and New York, p. 748. Pérez-del-Olmo, A., Montero, F.E., Raga, J.A., Fernández, M., Kostadinova, A., 2009. Follow-up trends of parasite community alteration in a marine fish after the Prestige oil-spill: shifting baselines? Environ. Pollut. 157, 221e228. Praveena, S.M., Kwan, O.W., Aris, A.Z., 2012. Effect of data pre-treatment procedures on principal component analysis: a case study for mangrove surface sediment datasets. Environ. Monitor. Assess. 184, 6855e6868. Pulfrich, A., 1997. Seasonal variation in the occurrence of planktic bivalve larvae in the Schleswig-Holstein Wadden Sea. Helgol. Wiss. Meeresunters. 51, 23e49. Rainbow, P.S., 1995. Biomonitoring of heavy metal availability in the marine environment. Mar. Pollut. Bull. 31, 183e192. Rainbow, P.S., Phillips, D.J.H., 1993. Cosmopolitan biomonitors of trace metals. Mar. Pollut. Bull. 26, 593e601. Reid, M.K., Spencer, K.L., 2009. Use of principal components analysis (PCA) on estuarine sediment datasets: the effect of data pre-treatment. Environ. Pollut. 157, 2275e2281. Regoli, F., Nigro, M., Orlando, E., 1998. Lysosomal and antioxidant response to metals in the Antarctic scallop Adamussium colbecki. Aquat. Toxicol. 40, 375e392. Sarasquete, M.C., Gonzales de Canales, M.L., Gimeno, S., 1992. Comparative histopathological alterations in the digestive gland of marine bivalves exposed to Cu and Cd. Eur. J. Histochem. 36, 223e232. Sasal, P., Mouillot, D., Fichez, R., Chifflet, S., Kulbicki, M., 2007. The use of fish parasites as biological indicators of anthropogenic influences in coral-reef lagoons: a case study of Apogonidae parasites in New-Caledonia. Mar. Pollut. Bull. 54, 1697e1706. Seed, R., 1968. Factors influencing shell shape in the mussel Mytilus edulis. J. Mar. Biol. Assoc. U. K. 48, 561e584. Shugart, L.R., McCarthy, J.F., D’Surney, S.J., Greeley, M.S., Hull, C.G., 1990. Molecular and cellular markers of toxicity in the Japanese medaka (Oryzias latipes). In: Gardner Jr., H.S. (Ed.), Compendium of the FY 1990 & FY 1992 Research Reviews for the Research Method Branch. U.S. Army Biomedical Research & development Laboratory Fort Detrick Frederick, pp. 34e51. Report/MD-21702-5010.


M. Brenner et al. / Marine Environmental Research xxx (2013) 1e15 Shugart, L.R., McCarthy, J.F., Halbrook, R.S., 1992. Biological markers of environmental and ecological contamination: an overview. Risk Anal. 12, 353e360. Smolders, R., Bervoets, L., Wepener, V., Blust, R., 2003. A conceptual framework for using mussels as biomonitors in whole effluent toxicity. Hum. Ecol. Risk Assess. 9, 741e760. Sures, B., 2008. Environmental Parasitology. Interactions between parasites and pollutants in the aquatic environment. Parasite 15, 434e438. Szefer, P., Fowler, S.W., Ikuta, K., Osuna, F.P., Ali, A.A., Kim, B.-S., Fernandes, H.M., Belzunce, M.-J., Guterstam, B., Kunzendorf, H., Wolwicz, M., Hummel, H., Deslous-Paoli, M., 2006. A comparative assessment of heavy metal accumulation in soft parts and byssus of mussels from subarctic, temperate, subtropical and tropical marine environments. Environ. Pollut. 139, 70e78. Terman, A., Brunk, U.T., 2004. Lipofuscin. The Int. J. Biochem. Cell Biol. 36, 1400e 1404. UBA, 2009. Umwelt Bundesamt, Dessau-Roßlau, Germany. http://www. umweltbundesamt-umwelt-deutschland.de/umweltdaten/public/ (assessed 01.07.09.). Viarengo, A., 1985. Biochemical effects of trace metals. Mar. Pollut. Bull. 16 (4), 153e 158. Viarengo, A., Moore, M.N., Mancinelli, G., Mazzucotelli, A., Pipe, R.K., 1985. Significance of metallothioneins and lysosomes in cadmium toxicity and homeostasis

15

in the digestive gland cells of mussels exposed to the metal in presence or absence of phenanthrene. Mar. Environ. Res. 17, 184e187. Viarengo, A., Nott, J.A., 1993. Mechanisms of heavy metal cation homeostasis in marine invertebrates. Comp. Biochem. Physiol. C Pharmacol. Toxicol. Endocrinol. 104, 355e372. Viarengo, A., Lowe, D., Bolognesi, C., Fabbri, E., Koehler, A., 2007. The use of biomarkers in biomonitoring: a 2-tier approach assessing the level of pollutantinduced stress syndrome in sentinel organisms. Comp. Biochem. Physiol. 146C, 281e300. von Moos, N., Burkhardt-Holm, P., Köhler, A., 2012. Uptake and effects of microplastics on cells and tissue of the blue mussel Mytilus edulis L. after an experimental exposure. Environ. Sci. Technol. 46 (20), 11327e11335. Watermann, B., Die, I., Liebe, S., 1998. Krankheiten der Miesmuschel (Mytilus edulis) an der ostfriesischen Küste. In: VII. Tagung der Deutschen Sektion der European Association of Fish Pathologists (EAFP) - Krankheiten der Aquatischen Organismen. 23.e25. September 1998, Schmallenberg-Grafschaft, pp. 177e187. Wang, L., Mizaikoff, B., 2008. Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy. Anal. Bioanal. Chem. 391, 1641e1654.


Hydrolytic enzymes associated with the granular haemocytes of the marine mussel Mytilus edulis.

Toxicity of copper oxide nanoparticles in the blue mussel, Mytilus edulis: a redox proteomic investigation.

A novel mitochondrial genome organization for the blue mussel, Mytilus edulis.

Synthesis of adhesive peptides similar to those found in blue mussel (Mytilus edulis) using papain and tyrosinase.

A mussel (Mytilus edulis) tissue certified reference material for the marine biotoxins azaspiracids.

Control of NADP+-dependent isocitrate dehydrogenase activity in the mussel Mytilus edulis L [proceedings].

The Influence of Organic Material and Temperature on the Burial Tolerance of the Blue Mussel, Mytilus edulis: Considerations for the Management of Marine Aggregate Dredging.

Thermal inactivation kinetic modeling of human norovirus surrogates in blue mussel (Mytilus edulis) homogenate.

The contribution of the pentose phosphate cycle to the central pathways of metabolism in the marine mussel, Mytilus edulis L [proceedings].

Sex-related differences in steroid concentrations in the blue mussel (Mytilus edulis trossulus) from the southern Baltic Sea.

The occurrence and in vitro effects of molecules potentially active in the control of growth in the marine mussel Mytilus edulis L.

Seasonal changes in the triacylglycerol fatty acids of the mantle tissue of the mussel Mytilus edulis L [proceedings].

Monitoring bio-available marine contaminants with mussels (Mytilus edulis L) in the Netherlands.

First record of the green microalgae Coccomyxa sp. in blue mussel Mytilus edulis (L.) from the Lower St. Lawrence Estuary (Québec, Canada).

Genotoxic and immunotoxic potential effects of selected psychotropic drugs and antibiotics on blue mussel (Mytilus edulis) hemocytes.

Generation of reactive oxygen metabolites by the haemocytes of the mussel Mytilus edulis.

Genetic, Ecological and Morphological Distinctness of the Blue Mussels Mytilus trossulus Gould and M. edulis L. in the White Sea.

Population differences of aspartate aminotransferase and peptidase in the bay mussel Mytilus edulis.

The occurrence of cadmium in sub-cellular particles in the kidney of the marine mussel, Mytilus edulis, exposed to cadmium. The use of electron microprobe analysis.

Selective retention of polychlorinated biphenyl components in the mussel, Mytilus edulis.

A multibiomarker approach to the assessment of pollution impacts in two Baltic Sea coastal areas in Sweden using caged mussels (Mytilus trossulus).

Adaptive genetic variation distinguishes Chilean blue mussels (Mytilus chilensis) from different marine environments.

Larval development of the northern horse mussel, Modiolus modiolus (L.), including a comparison with the larvae of Mytilus edulis L. as an aid in planktonic identification.

Comparison of intermittent and continuous exposures to inorganic mercury in the mussel, Mytilus edulis: accumulation and sub-lethal physiological effects.