Using Compound-Specific and Bulk Stable Isotope Analysis for

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Ecotoxicology and Human Environmental Health

Using compound-specific and bulk stable isotope analysis for trophic positioning of bivalves in contaminated Baltic Sea sediments Caroline Ek, Henry Holmstrand, Lukas Mustajärvi, Andrius Garbaras, Ruta Bariseviciute, Justina Sapolaite, Anna Sobek, Elena Gorokhova, and Agnes ML Karlson Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05782 • Publication Date (Web): 22 Mar 2018 Downloaded from http://pubs.acs.org on March 23, 2018

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Environmental Science & Technology

1

Using compound-specific and bulk stable isotope analysis for trophic positioning of bi-

2

valves in contaminated Baltic Sea sediments

3 4

Caroline Eka,*, Henry Holmstranda, Lukas Mustajärvia, Andrius Garbarasb, Ruta Barise-

5

viciuteb, Justina Sapolaiteb, Anna Sobeka, Elena Gorokhovaa, Agnes ML Karlsona,c

6 7

a

8

Svante Arrhenius väg 8, SE-106 91 Stockholm, Sweden

Department of Environmental Science and Analytical Chemistry, Stockholm University,

9 10

b

11

LT-02300 Vilnius, Lithuania

Mass Spectrometry Laboratory, Center for Physical Science and Technology, Savanoriu 231,

12 13

c

14

rhenius väg 20, SE-114 18 Stockholm, Sweden

Department of Ecology, Environment and Plant Science, Stockholm University, Svante Ar-

15 16

*Corresponding author:

17 18

Caroline Ek

19 20 21 22 23 24 25 26

Department of Environmental Research and Monitoring, Swedish Museum of Natural History, Frescativägen 40, SE-114 18 Stockholm, Sweden +46 8 5195 4107 [email protected]

ACS Paragon Plus Environment


27

Abstract

28 29

Stable nitrogen isotopes (δ15N) are used as indicators of trophic position (TP) of consumers.

30

Deriving TP from δ15N of individual amino acids (AAs) is becoming popular in ecological

31

studies, because of lower uncertainty than TP based on bulk δ15N (TPBulk). This method would

32

also facilitate biomagnification studies provided that isotope fractionation is unaffected by

33

toxic exposure. We compared TPAA and TPBulk estimates for a sediment-dwelling bivalve

34

from two coastal sites, a pristine and a contaminated. Chemical analysis of PCB levels in

35

mussels, sediments, and pore water confirmed the expected difference between sites. Both

36

methods, but in particular the TPAA underestimated the actual TP of bivalves. Using error

37

propagation, the total uncertainty related to the analytical precision and assumptions in the TP

38

calculations was found to be similar between the two methods. Interestingly, the significantly

39

higher intercept for the regression between TPAA and TPBulk in the contaminated site com-

40

pared to the pristine site indicates a higher deamination rate due to detoxification as a result of

41

chronic exposure and a higher 15N fractionation. Hence, there is a need for controlled experi-

42

ments on assumptions underlying amino acid-specific stable isotope methods in food web and

43

bimagnification studies.

44 45


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46

Introduction

47 48

Stable isotope analysis (SIA) is an essential tool for analysis of food web structure, which is

49

increasingly used in ecosystem management and risk assessment of environmental contami-

50

nants. The ratio of stable nitrogen isotopes (15N:14N, expressed relative to an international

51

standard; δ15N value) can be used for establishing the trophic position (TP) of consumers due

52

to its stepwise increase up the food chain. According to recommendations set in EU legisla-

53

tion,1 the TP estimates are of particular relevance for monitoring of contaminants in biota un-

54

der the Water Framework Directive. For chemical status assessment the European Commis-

55

sion recently suggested normalisation of contaminant concentrations in monitored species to a

56

specific TP before checking compliance with Environmental Quality Standard (EQSBiota) val-

57

ues. This procedure would both minimize natural variability and allow for flexibility in the

58

choice of monitoring species between member states.1 A common approach for TP estimation

59

is to measure δ15N of different food web components in specific tissues or whole body sam-

60

ples, bulk δ15N values. However, with this approach, information on the so called isotope

61

baseline δ15N (ultimate nitrogen source, which can vary considerably between ecosystems)

62

and the trophic shift (∆15N; the trophic enrichment in

63

called trophic discrimination factor) are also required to assign particular consumers to specif-

64

ic TPs in the food web.2 The accuracy of TP estimates based on bulk δ15N (TPBulk) are de-

65

pendent on the predictability of the isotope transfer between the diet and the consumer, with

66

average values of 2-4 ‰2-4 commonly being used. However, large variability in ∆15N (-3.2 ‰

67

to +9.7 ‰;4,5) exists which introduce uncertainty to the TPBulk estimates. Other areas of con-

68

cern with respect to the accuracy of TP estimates are variations in the baseline isotopic com-

69

position with potential differences on both a spatial6 and temporal scale,7,8 which require rel-

70

evant sampling of either primary producers or primary consumers to provide the δ15N corre-

71

sponding to TP 1 or TP 2, respectively.9,10

72 73

The recent improvement in the SIA-based approach for TP assessment, which is termed com-

74

pound specific isotope analysis (CSIA), provides information on stable isotope ratios in spe-

75

cific compounds, e.g. amino acids (AAs, AA-CSIA). Some AAs, often referred to as trophic

76

AAs, increase in δ15N between the diet and the consumer, whereas others, source AAs, show

77

none or very little change in their δ15N. Chikaraishi et al.11 proposed a new method to esti-

78

mate TP (TPAA) using the relationship between glutamic acid (Glu, trophic AA) and phenylal-

79

anine (Phe, source AA). The method suggested thus allows for both trophic shift information

15

N between diet and consumers, also



80

(δ15NGlu) and an integrated baseline δ15N value (δ15NPhe) from a single sample of a consumer.

81

CSIA can therefore provide a more accurate TP estimation compared to previous methods.11-

82

14

83

pared to the TPBulk estimates.15,16

84 85

Trophic magnification factors (TMFs) are used to assess contaminant biomagnification in a

86

food web,17 as opposed to assessing magnification only between two TPs. The TMF is the

87

slope of the regression between contaminant concentration and TP for consumers in the food

88

web and thus indicates the average increase in contaminant concentration per trophic level.

89

Improved understanding of food web structure, variability in TP, and, especially variations in

90

the baseline isotopic composition, are critical for accurate TMF assessments. Application of

91

TPAA instead of TPBulk may allow for more precise comparisons between ecosystems and im-

92

prove TMF accuracy, provided that isotope fractionation is unaffected by toxic exposure in

93

contaminated environments. The latter concern stems from recent experimental studies indi-

94

cating that δ15N and δ13C values may be affected by various physiological responses to con-

95

taminant exposure.18-21 Energetic costs imposed by toxicity have been linked to various re-

96

sponse mechanisms related to active removal, biotransformation and excretion of harmful

97

compounds.22 Many of these processes directly affect nitrogen fluxes, and thus may influence

98

15

99

AA-δ15N values are products of their specific biosynthetic pathways.24 Therefore, any stress

100

(e.g. contaminant exposure) that alters amino acid metabolism may lead to profound changes

101

in individual AA-δ15N values. Since the amount, and therefore relative abundances, of AAs

102

contribute to variations in bulk δ15N, it is possible that changes in dominant AAs will translate

103

into altered bulk δ15N values.

104 105

Effects of toxic exposure on δ15N and δ13C values have so far been explored mostly in labora-

106

tory settings.18-20,25,26 Therefore, field evaluations of the impact of toxic exposure on TP esti-

107

mates in chronically polluted systems are necessary. Here, we focus on the comparative anal-

108

ysis of TP estimates (TPBulk and TPAA) obtained for a facultative deposit- and suspension-

109

feeding bivalve in relatively pristine and heavily contaminated environments. Suspension-

110

feeders are commonly used as a δ15N baseline in food web analysis because these animals

111

presumably integrate the isotopic signal of primary producers, which decreases δ15N variabil-

112

ity related to seasonal and spatial heterogeneity of the first trophic level.9,10 We addressed two

113

questions: (1) do TPs inferred from bulk (TPBulk) and amino acid (TPAA) stable isotope analy-

In food web studies, this method was found to produce more reliable TP estimates com-

N fractionation via, for example, imbalance in protein synthesis and protein catabolism.23


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sis differ, and (2) do TPs differ between areas experiencing different contaminant load. As a

115

test species, we used a small marine bivalve Limecola balthica (Linnaeus, 1758) categorised

116

as a primary consumer with a TP of 2,8 although it is likely that the actual TP of the species

117

can be closer to 2.5 as it feeds on sediment possibly containing organic material originating

118

from higher trophic levels e.g. heterotrophic bacteria, excrements from other organisms and

119

resting eggs of zooplankton.27 The animals were collected together with sediment (proxy for

120

their diet28) in a coastal area of the northern Baltic proper. The animal and sediment samples

121

were analysed for isotope composition (bulk and AA-CSIA) and contaminant concentrations

122

(PCBs; omnipresent and bioaccumulative persistent contaminants listed under the Water

123

Framework Directive), and the TPBulk and TPAA values were calculated for each site and com-

124

pared.

125 126

Material and Methods

127 128

Study sites

129 130

Two study sites in the north-western Baltic proper were selected: Ålöfjärden (N58° 40’45;

131

E17° 8’28; contaminated site) and Uttervik (N58° 50’; E17° 32; reference site). Both sites are

132

shallow bays in the southern Stockholm Archipelago, located approximately 25 km apart. The

133

Uttervik site corresponds to the monitoring station 6001 (depth 20 m) in the combined Swe-

134

dish national-regional monitoring program for benthic fauna and sediments in the Baltic prop-

135

er; no point sources of environmental contaminants are known from this area. The Ålöfjärden

136

site is highly contaminated due to an active steelwork industry in the vicinity. This bay is

137

classified as having very high contaminant load for PAHs,29 with reported PAH concentra-

138

tions in sediment of 16 to 50 µg g DW-1.30,31 According to the national monitoring programs

139

and a recipient-control study in Ålöfjärden,30,32 the basic environmental conditions (sediment

140

type, average chlorophyll concentration, benthic and pelagic species composition) are similar

141

between the Ålöfjärden and Uttervik sites (Table S1).

142 143

Field sampling of Limecola balthica and sediment

144 145

The specimens of Limecola balthica (formerly known as Macoma balthica) as well as surface

146

sediment were collected from 7.5 m (n=2) and 20 m (n=1) bottom depth on October 10 (7.5 m

147

depth) and November 19 (20 m depth) 2014 in Ålöfjärden, and from 7, 8 and 20 m bottom

148

depth in Uttervik (ntot=3) on November 19, 2014. Sampling was conducted using a benthic



149

sled set at collecting the oxidised upper 2 cm of sediment from each site and depth. Sediments

150

were sieved on a 1 mm mesh and Limecola specimens of similar size were picked out and

151

immediately frozen at -20 °C. Additional sediment samples for contaminant analyses in total

152

sediment and pore water were sampled with a gravity corer from the same stations.

153 154

Chemical analysis of polychlorinated biphenyls (PCBs)

155 156

Seven indicator PCBs (CB28; CB52; CB101; CB118; CB138; CB180; PCB7), were analysed

157

in the animals, sediment and pore water. Pore water concentration was included since it is a

158

more reliable indicator of exposure to sediment-living organisms than total sediment concen-

159

tration.33 PCB7 were chosen as indicators for contamination levels since they have been moni-

160

tored for almost two decades in the Baltic Sea and are commonly found in environmental

161

samples.34 Sediment and mussels were extracted by Accelerated Solvent Extraction (ASE)

162

and the passive samplers used for pore water analysis were extracted by ultrasonication; both

163

methods used hexane:acetone as solvent. Clean-up was performed with open silica gel col-

164

umns and all samples were analysed on a gas chromatographer coupled to a mass spectrome-

165

ter (GC-MS). See Supporting Information Chemical analysis of Polychlorinated biphenyls

166

(PCBs) for the details on the chemical analysis.

167 168

Stable isotope ratio analyses

169 170

Limecola samples (1 mg of freeze-dried individual specimens, homogenized using a mortar

171

and pestle) as well as sub-samples of sediment (15 mg DW) were analysed for bulk δ15N and

172

δ13C at the Center for Physical Science and Technology, Vilnius, Lithuania. Subsamples (5

173

mg, freeze-dried animals) originating from the same individual as for the bulk analysis were

174

analysed for AA-CSIA at the Department of Environmental Science and Analytical Chemis-

175

try, Stockholm University, Sweden.

176 177

Bulk isotope ratio analysis

178

A Flash EA 1112 Series Elemental Analyzer connected via a Conflo III to a DeltaV Ad-

179

vantage isotope ratio mass spectrometer (all Thermo Finnigan, Bremen, Germany) was used

180

to analyse bulk samples. The stable isotope ratios

181

to the international standards atmospheric air (δ15N) and Vienna Pee Dee Belemnite (δ13C).

182

Caffeine (IAEA-600) was used as secondary reference material for the reference gas calibra-

15

N:14N and 13C:12C are expressed relative


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tion. An internal standard, fish muscle tissue (Esox lucius), was analysed in the beginning,

184

middle and end of each run (n = 6); overall analytical precision was less than 0.1 ‰ for both

185

δ15N and δ13C. Elemental composition of nitrogen and carbon (%N and %C, respectively) are

186

expressed as the percentage content of the sample dry weight. Calibration curves for the %N

187

and %C quantification were created using EMA P2 reference material (Elemental Microanal-

188

ysis). For analytical precision of elemental composition, a series of samples (n = 9) each con-

189

taining a single individual of the crustacean Daphnia magna collected from a culture (size

190

340-520 µg) were used. The overall analytical precision was 0.2% and 0.9% for %N and %C,

191

respectively

192 193

Amino acid compound specific isotope analysis (AA-CSIA)

194

Samples of freeze-dried mussel powder were hydrolysed in 6 M hydrochloric acid (HCl), pu-

195

rified using a cation exchange resin and derivatized in two consecutive reactions. First an es-

196

terification using an acetyl chloride:isopropanol mixture (1:4) was carried out to remove the

197

carboxylic group, followed by the second derivatization; an acylation using a trifluoroacetic

198

anhydride:dichloromethane mixture (1:3) to remove the amino group. The samples were then

199

dissolved in ethyl acetate and transferred to GC-vials and analysed using a GC-combustion-

200

isotope ratio MS (GC-C-IRMS). For more details, see Supporting Information, Amino acid

201

compound specific isotope analysis (AA-CSIA). After every fourth sample, a standard sample

202

was analysed that generated an analytical precision of 1.1 ‰ (nGlu = 10) and 1.5 ‰ (nPhe = 8)

203

for Glu and Phe, respectively.

204 205

Data analysis

206 207

The estimates for TPBulk and TPAA were calculated using equation 12 and 2,11 respectively. For

208

TPBulk, 3.4 ‰ was used as the average trophic shift (∆15NBulk) and average sediment δ15N val-

209

ues for the site in question as a baseline. The bulk of the organic carbon and nitrogen content

210

in Baltic Sea sediments is considered to consist mainly of blooms from microalgae and cya-

211

nobacteria,35 generally from seasonal phytoplankton blooms; in particular the spring bloom of

212

diatoms.36,37 For this reason we have chosen to set the TPbase=1 although it is likely that also

213

other types of organic matter with potential higher TPs can contribute as food sources e.g.

214

heterotrophic bacteria, excrement from other organisms and resting eggs from zooplankton.27

215

For TPAA, the offset in glutamic acid and phenylalanine in primary consumers (βGlu/Phe) can

216

vary substantially depending on mode of photosynthesis. For planktonic algae and cyanobac-



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217

teria this difference is assumed to be +3.4 ‰ whereas for terrestrial C3 vascular plants it has

218

instead been reported to be -8.4 ‰.38 Such difference will impact the TP estimate substantial-

219

ly in mixed systems where the plant sources are not monolithic39 wherefore it is important to

220

consider and assess the relative contribution of different plant sources at the base of the food

221

web at the specific study site. In this study we have examined shallow coastal ecosystems that

222

can potentially be such mixed systems. However, based on the resemblance of δ13C values for

223

both bivalves (Uttervik: -19.4 ‰; Ålöfjärden: -20.2 ‰) and sediment (Uttervik: -21.6 ‰;

224

Ålöfjärden: -23.8 ‰) in our study to planktonic δ13C (-18 to -24 ‰), a large contribution of

225

terrestrial carbon (-25 to-37 ‰) is not likely.35,40-42 Moreover, terrestrial material can be high-

226

ly refractory and do not promote growth in deposit-feeders.21,41 For an extended description of

227

potential food sources see table S1 in the Supporting Information.

228 229

For the TPAA calculations, the difference in glutamic acid and phenylalanine in primary con-

230

sumers (βGlu/Phe) was set to +3.4 ‰ representing cyanobacteria and microalgae, and the

231

trophic shift between glutamic acid and phenylalanine (∆15NGlu-Phe) set to 7.6 ‰, respectively,

232

according to Chikaraishi et al.:11

233 234

= ( − )⁄∆ + 1

235

= ( − −

236 237

Uncertainties associated with TPBulk and TPAA estimates were calculated using error propaga-

238

tion.43 For TPBulk, the analytical precision of δ15N determination for animals and sediment

239

(this study) and ∆15NBulk (±0.98 ‰;2) were used. For TPAA, the analytical precision of δ15N

240

determination in Glu and Phe (this study) as well as βGlu/Phe (±0.9 ‰) and ∆15NGlu-Phe (±1.2 ‰)

241

as recommended by Chikaraishi et al.11 were used.

242 243

Bioaccumulation factors in bivalves (lipid normalized) based on pore water concentrations

244

(Cpw, BAF) and total sediment concentrations (Ctot, BSAF) of each PCB congener was calcu-

245

lated as:

/ )⁄∆ "

(Equation 1) +1

(Equation 2)

246 &'())*+

247

#$% =

248

#.$% =

(Equation 3)

&,&'())*+

(Equation 4)

&/0/

249


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250 251

Statistics

252 253

Sediment data (%C, %N, δ15N and δ13C) were compared between the sites to evaluate similar-

254

ities in the baseline δ15N and ultimate carbon sources. This was done using generalised least

255

square models with restricted maximum likelihood (GLS-REML) to account for incomplete

256

independency of samples within each site. To test the site effect on δ15N and δ13C values in

257

the bulk samples, generalized linear models (GLMs) were used with δ15N (δ13C) as a response

258

variable and site, sampling depth and the interaction term site × depth as explanatory varia-

259

bles. Models were evaluated using Shapiro-Wilks test for normality and the most parsimoni-

260

ous model was selected based on the Akaike Information Criterion (AIC). To test if the bioac-

261

cumulation factors (BAF for pore water and BSAF for sediment concentrations) for PCBs

262

differed between sites (Ålöfjärden vs. Uttervik), paired t-tests were performed with the seven

263

PCB congeners as pairs (log-transformed mean values within sites for each congener was

264

used, Table S2).

265 266

Two-way ANOVA was used for evaluating differences in TP between the estimation methods

267

(TPBulk vs. TPAA) and sites (Ålöfjärden vs. Uttervik); Sidaks’ post-hoc test was used for pair-

268

wise comparisons. Whether the TP estimates deviated from the nominal TP of 2, correspond-

269

ing to primary consumers such as bivalves and gastropods,10 was tested using one sample t-

270

test. Moreover, to test the effect of site (indicating exposure effect) on the relationship be-

271

tween TPAA and TPBulk, a GLM with TPAA as response variable and site, TPBulk and the inter-

272

action term site × TPBulk as explanatory variables was carried out. To interpret the main ef-

273

fects of the model, TPbulk values were centred.44 The rationale for this test was to evaluate

274

how TPAA changes in concert with TPBulk in a contaminant area and thus if chemical exposure

275

can influence this relationship. The statistical software R version 3. 3. 2 (2016-10-31) was

276

used for all tests except for the two-way ANOVA and the paired t-tests which were performed

277

in Statistica 8.0 (StatSoft, USA) and the one sample t-tests done in GraphPad.45 The signifi-

278

cance level was set to α = 0.05. Values are presented as mean ± SD unless specified other-

279

wise.

280 281

Results

282 283

Differences in PCB concentrations between the sites



284

In the animals from Ålöfjärden, ΣPCB7 concentrations were ~7 times higher compared to

285

those from Uttervik, with individual congeners varying from 1.3 to 5.1 ng g DW-1 in

286

Ålöfjärden and 0.07 to 1.3 ng g DW-1 in Uttervik (see Supporting Information, Table S2, for

287

lipid-normalised concentrations). PCB concentrations in mussels thus followed observed PCB

288

concentrations in sediment and sediment pore water at the two sites (see Supporting Infor-

289

mation, Table S2, for details on specific congener concentrations measured in the animals,

290

sediment and pore water). Accordingly, bioaccumulation factors (BAF, BSAF) were not sig-

291

nificantly different at the two sites; BAF: t= -0.28, df=6, p=0.79, BSAF: t=1.14, df=6, p=0.30.

292

It is worth noting however, that the two low-chlorinated PCBs (28 and 52) had lower values

293

for both BAF and BSAF at Ålöfjärden than in Uttervik (Figure S1).

294 295

Sediment elemental and isotopic composition

296

The sediment %N was significantly higher in Uttervik compared to Ålöfjärden (t13,1 = -11.68,

297

p < 0.001), whereas %C values were uniform between the sites (t13,1 = -0.14, p > 0.8; Figure

298

1A, Supporting Information Table S1). Furthermore, the sediment δ15N values in Ålöfjärden

299

were 1.4 ‰ higher (t13,1 = 4.02, p < 0.001) compared to those in Uttervik, whereas the δ13C

300

values showed the reverse pattern, being 2.2 ‰ lower in Ålöfjärden compared to Uttervik

301

(t13,1 = -5.1, p < 0.0001; Figure 1B, Supporting Information Table S1).

302 303

Bulk and compound specific stable isotope values in Limecola balthica

304

No effect of sampling depth was found for neither δ15N nor δ13C values of the bivalves for the

305

bulk samples. The samples were therefore pooled within the sites for further analyses. The

306

bulk δ15N values of the bivalves from Ålöfjärden were 1.3 ‰ higher compared to those from

307

Uttervik (t15,1 = 4.32, p < 0.001; Table 1, Figure 2A). Moreover, the bulk δ13C values were 1.2

308

‰ lower in the bivalves from Ålöfjärden (t15,1 = -2.75, p < 0.015; Table 2, Figure 2A). The

309

two-way ANOVA identified a significant effect of site on δ15N in amino acids, (F1,14 = 5.511,

310

p < 0.04), with higher values observed in Ålöfjärden compared to Uttervik (δ15NGlu by 1.7 ‰

311

and δ15NPhe by 0.7 ‰; Table 1, Figure 2B).

312 313

Trophic positioning

314

The overall uncertainties associated with TPBulk and TPAA estimates were similar, varying

315

from 0.34 TP to 0.36 TP (see Supporting Information, Table S3). The TPBulk values were sig-

316

nificantly higher than TPAA (Table 1), but with no significant difference between sites (two-


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317

way ANOVA; Table 2, Figure 3). At both sites, the TPBulk estimates were not significantly

318

different from the TP 2 of a primary consumer (Figure 3, Uttervik: p > 0.6, and Ålöfjärden: p

319

> 0.6). In contrast, TPAA was significantly lower in Uttervik (p < 0.01), and marginally lower

320

in Ålöfjärden (p = 0.1) than 2. Moreover, individual TPAA values were significantly positively

321

related to TPBulk (t13,1 = 2.28, p < 0.04), and noteworthy, with significantly higher TPBulk-

322

specific TPAA estimates for animals from Ålöfjärden compared to animals from Uttervik

323

(t13,1=2.42, p < 0.03).

324



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325 326 327

Discussion

328

AA-CSIA and to examine whether these estimates are affected by contaminant load. These

329

questions were addressed by analysing δ15N of bulk samples and AA-δ15N of the primary

330

consumer Limecola balthica. We found a significant difference between the TPBulk and TPAA

331

estimates, with TPAA values significantly underestimating the actual TP of these animals.

332

Moreover, some support for elevated TPAA estimates at the contaminated site was also found

333

(see Table 2 and Figure 2B), whereas the TPBulk estimates were similar between the sites.

334 335

The observed differences in the bivalve bulk δ15N and δ13C (Figure 2A) values between the

336

sites reflected the difference in the sediment bulk isotopic composition (Figure 1B). There-

337

fore, no difference was found for either TPBulk or TPAA in mean TP values between the sites .

338

However, the relationship between TPAA and TPBulk had a significant site effect, implying that

339

bivalves from Ålöfjärden had slightly higher TPAA than those from Uttervik for the same

340

TPBulk values. The most likely explanation for this small difference would be higher

341

fractionation in the bivalves from Ålöfjärden (p=0.054) reflecting higher trans- and deamina-

342

tion rates46 due to detoxification processes while baseline δ15N between the sites (15NPhe) was

343

similar. However, although the analysis of PCB levels in the animals, sediment and pore wa-

344

ter has shown that Ålöfjärden is indeed a contaminated area as suggested by earlier reports,30

345

it is unclear whether the measured concentrations were sufficiently toxic to cause physiologi-

346

cal effects. For example, the measured ΣPCB7 body concentration in Ålöfjärden (1.8 µg g

347

DW-1) was half of the internal concentration (3.9 µg g DW-1) measured in Mytilus edulis ex-

348

posed to Aroclor 1248 (mixture of PCBs), for which no effects on the body condition index

349

was found.47 Moreover, sub-cellular effects in Mytilus galloprovincialis exposed to Aroclor

350

1254 and PCB-138, were observed only at concentrations 4- and 10-fold higher48 than the

351

internal concentrations observed in the bivalves from Ålöfjärden. This suggests that the PCB

352

concentrations in Ålöfjärden might not be high enough to induce a pathological condition and,

353

hence, cause altered

354

study, whereas PAHs and heavy metals were also present in the sediments as well as other

355

contaminants that may contribute to toxicity. The concentration of the sum of 13 PAHs in

356

sediment from Ålöfjärden were up to 80 ug g DW-1,30 exceeding safe levels according to

357

guidelines for sediment risk assessment.29 However, it is also possible that environmental

358

factors other than contaminant load are more influential in setting the stage for isotopic signa-

The aim of this study was to compare the trophic position values estimated using bulk and

15

15

NGlu

N fractionation. On the other hand, we measured only PCBs in this


Page 13 of 24


359

tures in biota. For example, the slightly higher %N in the Uttervik sediments could contribute

360

to the lower δ15N values, both in the sediment and in the bivalves due to the negative correla-

361

tion between N availability and fractionation.49 In a previous study, Karlson et al.28 found the

362

sediment-Limecola δ15N difference to correlate negatively with sediment N content, however,

363

in our study the difference in sediment %N between sites were smaller (0.43 to 0.75%).

364 365 366

In Limecola and other bivalves, a TP of 2 has previously been used,8,39 and our TPBulk esti-

367

mates were in the range of the expected TP (min-max: 1.8-2.3, average 2.1). However, it

368

should be noted that a TP of 2 most likely underestimate the real TP since Limecola also feed

369

on e.g. resting eggs in the sediment and likely various microorganisms associated to decom-

370

posing particles. The TPAA values were even lower (1.3 to 2.1, average 1.7) which is obvious-

371

ly biased. This implies that in spite of food web-based reports demonstrating superior accura-

372

cy of TPAA estimates,15,50 the CSIA-based assessment for field-collected Baltic Limecola

373

would underestimate its TP considerably. Notably, other field studies also provide evidence

374

for biased/artificial TPAA estimates. For example, Vokhshoori and McCarthy51 found that

375

TPAA for the Californian mussel Mytilus californianus ranged between 1.0 and 1.8 compared

376

to the nominal TP 2. This was explained by a deviation from the commonly assumed trophic

377

enrichment factor between glutamic acid and phenylalanine value in the consumers (∆15NGlu-

378

Phe);

379

meta-analysis on the application of AA-δ15N for TP estimates, Nielsen et al.52 found that

380

∆15NGlu-Phe of 6.6 ‰ instead of the commonly used 7.6, and βGlu/Phe of 2.9 instead of 3.4 ± 0.9

381

‰11 may better explain variations in TP for a wide range of organisms. Therefore, in addition,

382

TPAA estimates using revised discrimination factors (6.6 for βGlu-Phe and 2.9 ∆15NGlu-Phe) by

383

Nielsen et al.52 were also calculated together with associated uncertainties (± 1.7 for ∆15NGlu-

384

Phe

385

these lower values would be used instead together with associated uncertainties, TPAA would

386

be in the range of TP 2 and statistically non-distinguishable from the TPBulk values (Uttervik:

387

p=0.2, and Ålöfjärden p=0.8). However, even though TPAA estimates for bivalves in our study

388

had a higher accuracy using data from Nielsen et al.42 compared to Chikaraishi et al.,11 the

389

precision was lower (SDtotal 0.45-0.46). Altogether, this suggests that (1) species-specific val-

390

ues might be needed for accurate TP assessment, or (2) the uncertainties associated with both

391

∆15NGlu-Phe (± 1.7 ‰) and βGlu/Phe (± 2.0 ‰) reported by Chikaraishi et al.11 need to be revised

392

(see e.g. Nielsen et al.42) and included in the TP analysis. This is also partly supported by a

3-5 ‰ compared to 7.6 ± 1.2 ‰ reported in Chikaraishi et al.11 Moreover, in a recent

and ± 2.0 for βGlu-Phe) using error propagation (see Supporting Information, Table S3). If



393

recent review by Ohkouchi et al.,53 where the ∆15NGlu-Phe was generally found to vary between

394

6 ‰ to 8‰ for wild-caught organisms and controlled feeding studies although greater varia-

395

tion was also found (0‰ to >10 ‰). As the authors suggested, the observed variation was

396

likely due to differences in food quality and mode of nitrogen excretion previously known to

397

influence bulk δ15N,4,54 and therefore also fractionation of trophic amino acids like glutamic

398

acid. Thus, it can be expected that to achieve more accurate TPAA estimates one would require

399

both species- and habitat-specific values for ∆15NGlu-Phe. Finally, studies have also shown that

400

the βGlu-Phe value is species- and context-dependent with varying values for terrestrial, pelagic

401

and mixed systems which can require an assessment of potential plant sources at the base of

402

the food web to accurately derive true βGlu-Phe in certain systems.39,55 If using this approach for

403

our data and assuming bivalves feeding on (heterotrophically conditioned) organic matter of

404

terrestrial origin in the more enriched spectra (-25 ‰), the TP of bivalves accordingly in-

405

crease to above 2. Hence, future studies need to include measure of primary producers to al-

406

low mix βGlu-Phe values to be calculated.38 By accounting for uncertainties associated with

407

measured δ15N in the bulk samples, glutamic acid and phenylalanine, together with uncertain-

408

ties for ∆15NBulk, ∆15NGlu-Phe and βGlu/Phe, we were able to assess the variability related to the

409

TP estimates (Figure 3 and Supporting Information, Table S3). Both methods (Post2 and

410

Chikaraishi et al.11) had very similar overall uncertainty, even though analytical precision for

411

AA-δ15N was much lower. This means that even though TPAA have less uncertainty related to

412

the trophic shift and fractionation by primary producers the high analytical error does not re-

413

sult in overall improvement in the TP uncertainty in our study. Methodological improvements

414

in AA-CSIA, particularly related to derivatization steps56 can increase analytical precision and

415

thus improve future TPAA estimates.

416 417

In conclusion, contrary to the common assumption that TP estimates based on AA-CSIA are

418

more accurate, we found that using bulk δ15N provided more ecologically relevant estimates

419

for a deposit- and suspension-feeding bivalve although both methods produced TP estimates

420

that are likely underestimates. The AA-CSIA method underestimated TP values considerably

421

and suffered relatively high analytical uncertainties. Although average TP values were not

422

significantly different between the sites experiencing different contaminant load, the animals

423

from the contaminated site had significantly higher TPAA for a given TPBulk estimate. To fur-

424

ther investigate whether stable isotope ratios may respond to chronic exposure in polluted

425

areas, the assessment of the population in question should include measurements that can be


Page 14 of 24

Page 15 of 24


426

used to infer physiological and biochemical responses indicative of the health status of the test

427

animals, such as physiological rates, biomarkers and body condition indices. Finally, there is a

428

need for more controlled studies to make the best use of amino-specific stable isotope meth-

429

ods in food web studies.

430 431



432

Supporting information available

433 434

SI includes information on Sediment characteristics (Table S1), Chemical analysis of Poly-

435

chlorinated biphenyls (PCBs) and measured concentrations in mussel, sediment and pore wa-

436

ter (Table S2), information on amino acid compound specific isotope analysis (AA-CSIA) and

437

a summary of uncertainties involved in TP calculation (Table S3). This information is availa-

438

ble free of charge via the Internet at http://pubs.acs.org.

439 440

Acknowledgement

441 442

This study was supported by the Delta Facility of the Faculty of Science, Stockholm Universi-

443

ty, Department of Environmental Science and Analytical Chemistry (trans-unit collaboration

444

funding), the Swedish Institute (Stable Isotope network in the Baltic Sea region) and the For-

445

mas project (HOC Flux Grant no #2012-1211). We thank Nesrine Mansouri for PCB analysis

446

of mussels, Karin Ström for help with the amino acid extraction and Maria Lagerström for

447

advices on amino acid extraction, Jakob Walve for sharing unpublished data on cyanobacteri-

448

al composition in Ålöfjärden and Svealands kustvattenvårdsförbund for supplying the phyto-

449

plankton data. Askö Laboratory Staff helped with field sampling. We thank Katrine Borgå

450

and three anonymous reviewers for valuable comments and Douglas Jones for proofreading

451

this manuscript.


Page 16 of 24

Page 17 of 24


452

Table 1. Summary of endpoints measured or calculated in Limecola from Uttervik (reference)

453

and Ålöfjärden (contaminated) sites.a DW

δ15N

δ13C

Glu

Phe

TPBulk

TPAA

mean

17.9

7.2

-20.4

10.6

2.6

2.1

1.6

SE

1.2

0.2

0.3

0.6

0.4

0.06

0.07

mean

18.7

8.6

-21.6

12.2

3.3

2.1

1.8

SE

0.5

0.2

0.3

0.5

0.3

0.06

0.06

Uttervik

Ålöfjärden

454

a

DW, dry weight in mg; δ15N, in bulk sample; δ13C, in bulk sample; Glu, δ15N value in glu-

455

tamic acid; Phe, δ15N value in phenylalanine; TPBulk, bulk δ15N-based TP; TPAA, AA-CSIA-

456

based TP. All stable isotope ratios are presented in ‰.

457 458 459

Table 2. Two-way ANOVA applied to compare the TP estimates within (TPBulk vs. TPAA;

460

Sidaks’ multiple comparisons test; see Figure 3) and between sites (Uttervik vs. Ålofjärden).

461 ANOVA table

SS

MS

F1, 30

P value

Interaction

0.03254

0.03254

0.3522

0.5573

Site

0.08810

0.08810

0.9534

0.3367

TPBulk vs. TPAA

1.742

1.742

18.85

0.0001

Residual

2.772

0.09241

462 463



464 465

Figure 1. Variability in the elemental (%N and %C; panel A) and isotopic (δ15N and δ13C val-

466

ues, ‰; panel B) composition of the sediment at the study sites: Uttervik (reference) and

467

Ålöfjärden (contaminated). Asterisks indicate significant effects for a variable between the

468

sites (unpaired t-test with Welch correction for unequal variances; **: p < 0.001; ***: p

Using Compound-Specific and Bulk Stable Isotope Analysis for

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