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mbryo development of the benthic amphipod Monoporeia affinis as aool for monitoring and assessment of biological effects ofontaminants in the field: A meta-analysis
artin Reutgard ∗, Ann-Kristin Eriksson Wiklund, Magnus Breitholtz, Brita Sundelinepartment of Applied Environmental Science, Stockholm University, Svante Arrhenius väg 8, SE-11418 Stockholm, Sweden
r t i c l e i n f o
rticle history:eceived 8 January 2013eceived in revised form 26 August 2013ccepted 29 August 2013
eywords:ioindicatornvironmental monitoring and assessmentollutionarine Strategy Framework Directiveonoporeia affinis
altic Sea
a b s t r a c t
Embryo malformations in the benthic amphipod Monoporeia affinis have been used as a biological effectindicator of chemical contaminants for more than three decades. The results from field studies alongthe Swedish Baltic Sea coast, comprising more than 50,000 analyzed embryos, were synthesized usinga meta-analytic approach. This approach generated a quantitative and statistically defensible summaryand enabled us to explore potential causative factors. The study aimed at evaluating the usefulness ofembryo malformations as a biological effect indicator of chemical contaminants in the field. The resultshows that malformations in M. affinis are ubiquitous in polluted areas and are negatively correlated withdistance from main sources of contaminants. The result also shows that malformations are significantlymore frequent up to more than 10 km from point sources. We conclude that embryo malformations inM. affinis can provide useful information for management and environmental policy in the Baltic Searegion as: (1) the present study supports evidence from controlled laboratory studies that there is acause and effect relationship between embryo malformations and contaminants; (2) the study suggeststhat the indicator is contaminant-sensitive and can therefore serve as an early warning of biological
effects in the field; (3) the indicator is general, suggesting that it has capability to monitor and detecteffects of a wide variety of known, and yet unknown, chemical contaminants. The usefulness is furtherstrengthened by the fact that M. affinis is a widely distributed species that plays a fundamental role in theBaltic Sea ecosystem. Future research should increase the understanding of how environmental factorsaffect the indicator response and if the response is related to effects at lower and higher levels of biologicalorganization.
. Introduction
The ultimate goal of the European Commission’s Marine Strat-gy Framework Directive 2008/56/EC (MSFD) is to maintain orchieve Good Environmental Status (GES) in Europe’s seas by 2020.ood Environmental Status has been defined by11 qualitativeescriptors which will be used to assess the achievement of theirective’s goal. Descriptor 8 addresses chemical pollution andtates that “Concentrations of contaminants are at levels not givingise to pollution effects”. The group of experts on contaminants and
ollution effects, designated by the Commission, recommends thathe achievement of Descriptor 8 should be based on monitoringrograms. These should apply an ecosystem-based approach,
including assessment of concentrations of contaminants in envi-ronmental matrices, and the quantification of biological effectsusing an integrated approach with biomarkers and bioindicatorsof pollution at different level of biological organization (Law et al.,2010).
A biomarker/bioindicator of chemical pollution should meet anumber of criteria in order to provide relevant and useful infor-mation for management and directing environmental policy (Lawet al., 2010; Lyons et al., 2010; van der Oost et al., 2003; Viarengoet al., 2007). Among the most obvious requirement is an establishedrelationship between chemical pollution and biological effect. ICESWorking Group on Biological Effects of Contaminants (WGBEC)also stresses that a biomarker/bioindicator should respond tocontaminant exposure in the field (ICES, 2007). Other importantconsiderations are specificity and sensitivity. Specificity relates
to both the ability to separate chemical pollution effects fromthe influence of environmental factors, and the ability to iden-tify effects due to a specific pollutant or group of pollutants. Abioindicator should be sensitive to contaminants in order to act
s an early warning of adverse environmental effects. Biomarkerst a lower levels of biological organization (i.e. molecular, bio-hemical and physiological) can provide a relatively sensitive andarly warning of toxicological effects that to a greater extent isechanistically understood, and stressors-specific compared to
iomarkers and bioindicators at a higher levels of biological orga-ization (Clements, 2000; Thain et al., 2008). On the other hand,iomarkers and bioindicators at higher levels of biological orga-ization (i.e. organism, population, community and ecosystem)rovide information with ecological relevance, which is generallyore uncertain for biomarkers and bioindicators at low lev-
ls.Embryo malformations in the amphipod Monoporeia affinis has
een used as a bioindicator of chemical pollution for more thanhree decades. Laboratory studies have shown that malformedmbryos arise in M. affinis as a result of exposure to trace metalsnd organic contaminants (Elmgren et al., 1983; Eriksson et al.,996; Eriksson Wiklund et al., 2005; Sundelin, 1983a,b, 1988, 1989;undelin and Elmgren, 1991). Field studies have also demonstrated
higher occurrence of malformed embryos after an oil accidentElmgren et al., 1983) and at sites close to industrial effluents in theulf of Bothnia (Sundelin and Eriksson, 1998). The method has beensed since 1994 in the Swedish national monitoring program as aioindicator of chemical pollution. The geographical coverage of therogram was recently changed to cover a larger proportion of thewedish Baltic Sea coast in 2012. The updated program design haso far not been published, however, a description of the programetween 1994 and 2011 can be found in the annual report from thewedish Institute for the Marine Environment (Havsmiljöinstitutet,011).
The Helsinki Commission (HELCOM) is the EU and regional envi-onmental policy maker that will act as the coordinating platformor implementing the MSFD in the Baltic Sea Region. HELCOM isow in the process of finding a set of biomarkers and bioindica-ors (core indicators) that will be used as a basis for an integratedssessment of the environmental status of the Baltic Sea. Embryoalformations in M. affinis are one of the indicators that currently
re under consideration as a part of the set of core indicators. Theresent study aims at evaluating the usefulness of embryo mal-ormations as a biological effect indicator of chemical pollutionn field. The present study used a meta-analytic approach botho explore the relationship between chemical pollution and theioindicator response and to evaluate the sensitivity and speci-city of the bioindicator. Meta-analysis is a tool for combiningultiple independent studies that, in contrast to narrative synthe-
is, provides a quantitative and statistically defensible summaryf the relationship across studies (Gurevitch et al., 2001; Hedgesnd Olkin, 1985). Meta-analysis is especially useful in cases likehis when sample size and effects size are small in the sensehat it improves the statistical power and decrease the risk ofommitting a type II error (Arnqvist and Wooster, 1995). Theeta-analytic approach can also generate and test new hypothe-
es that otherwise would be difficult due to a limited sampleize.
It is hypothesized that embryo malformations in M. affinis prin-ipally arise from exposure of anthropogenic chemical pollution.his hypothesis was tested by analyzing the relative risk of malfor-ations in chemically polluted coastal areas and reference areas,
nd by investigating the correlation between the relative risk ofalformations and the distance from known sources of chemical
ollution. Specificity in terms of ability to separate chemical pol-ution effects from the influence of other factors was addressed in
eta-regressions that examine depth, latitude, and year of studys potential explanatory factors. Sensitivity was evaluated by strat-fying studies in to groups based on distance from known sourcesf chemical pollution.
icators 36 (2014) 483– 490
2. Material and methods
2.1. Bio-indicator analysis
Reproduction, embryo development and analysis are describedin more detail in Sundelin and Eriksson (1998) and Sundelin et al.(2008). Briefly, M. affinis is a deposit-feeding amphipod that isdistributed over the entire Baltic Sea and is considered a key-stone species of the benthic community (Elmgren et al., 1990).It has a semelparous life history and mating occurs in Novemberto December, after which the female carries the embryos in abrood pouch until the young hatch in February to April (Segerstråle,1967, 1970, 1971). This reproductive strategy enables the assess-ment of the embryo development at an individual level. Embryoscan be carefully dissected from the female and classified understereo microscope as either healthy or aberrant, which are furtherdivided into four categories: malformed, undifferentiated, singledead embryos and dead broods. Visual parasite occurrence anddamages to exoskeleton are also noted.
2.2. Study selection, inclusion criteria and data collection
The term study is hereafter used to refer to a particular studyoccasion of a particular coastal area. Each study normally includesseveral sampling stations within the coastal area, hereafter referredto as sites. To be included in the meta-analysis, studies needed tocomprise at least one site considered as reference site located morethan 30 km away from known point sources. An exception wasmade for the study of the Tsesis oil spill in Stockholm archipelagoin 1978 where the same site was sampled before and after the oilspill (Elmgren et al., 1983). Results were pooled if studies includedmultiple reference sites by calculating the sum of normal and mal-formed embryos, respectively, for all reference sites.
For each studied site, data were collected on the number ofnormally developed embryos and malformed embryos. Malformedembryos were defined as both deformed and membrane damagedembryos (Sundelin and Eriksson, 1998). Effect sizes were calculatedas the arcsine risk difference between the proportion of malformedembryos at polluted site and the reference site (Rücker et al., 2009).To increase interpretability of results, effect sizes were also shownas untransformed risk difference. The following information wasalso extracted from each study for use in the meta-regression: (1)distance from known point source(s); (2) depth; (3) year of studyand (4) latitude. The Water Information System Sweden were usedto identify main point sources in the studied areas (WISS, 2011).
There are no comparable data sets over hazardous substancesfrom all the sampled areas/sites. To justify the use of distance as aproxy for degree of chemical pollution, principal component anal-yses were done on chemical substance data from 3 of the studiedareas. The relationship between the distance and the site spe-cific score for the first principal component was then explored bylinear regression analysis. Concentrations of chemicals were logtransformed and standardized to zero mean and unit variance.The principal component analyses were done using the function“prcomp” in R 2.15.1 (R Development Core Team, 2012).
2.3. Overall effect size
A random-effects model was used in the meta-analysis to com-bine effect sizes from all studied sites into a single overall effectsize for the proportion of malformed embryos in polluted coastalwaters. Individual effect sizes were weighted and combined into
an overall effect size and a 95% confidence interval was formed.Weights of the ith study were set to 1/(vi + �2), where vi is samplingerror and �2 is the heterogeneity among true effect sizes. A differ-ence in proportion of malformed embryos between polluted and
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eference site was considered significant if the confidence inter-al did not overlap zero. Calculations of individual effect sizes andariances as well as the combining meta-analysis were done usinghe package metafor in R 2.15.1 Software (R Development Coreeam, 2012; Viechtbauer, 2010). The heterogeneity among sitesas estimated using the restricted maximum likelihood method
Viechtbauer, 2005). The t-distribution with k − 1 degrees of free-om was used to generate confidence intervals, where k is theumber of effect size estimates.
The underlying assumption of sampling independence is vio-ated as most studies include more than one polluted site per study,nd are compared to a common reference site within the sametudy. To account for potential within-study variability, resultsere compared with a method for robust variance estimation ofependent effect size estimates (Hedges et al., 2010). The robustariance estimation requires a specified within-study correlationhich is unknown. A sensitivity analysis was therefore performed
y varying the within-study correlation among estimates from 0 to.9 in increments of 0.1.
.4. Explanatory factors
Spearman’s rank correlation test was used to analyze the corre-ation between the proportion of malformed embryos at referenceites and latitude, depth and year. Mixed effects meta-regressionodels were then used to examine the heterogeneity among effect
izes of malformed embryos. The study of Tsesis oil spill (Study) was not included in the meta-regression analyses as it is diffi-ult to estimate a distance from main source of chemical pollution.
two-step approach was used; first, based on prior knowledge, hypothesis-driven analysis was used to test the null hypoth-sis that the proportion of malformed embryos is unrelated tohe distance from sources of chemical pollution. Second, since nonowledge exists on the relationship between latitude, depth orear of study, an exploratory approach was then adopted using atepwise forward selection procedure. A p-value of 0.1 was useds a selection criterion in the stepwise forward selection proce-ure. Meta-regressions were done using the package metafor in
2.13.0 Software (R Development Core Team, 2012; Viechtbauer,010). The t-distribution with k − p degrees of freedom was usedo test coefficients and generate confidence intervals, where k ishe number of effect size estimates and p is the number of modeloefficients including the intercept. Distance from known pointource(s), depth, year of study and latitude was tested for co-inearity using Pearson’s correlation coefficient and was excludedf proven significant (p < 0.05). For comparison, meta-regressions
ere also done taking into account for potential within-study vari-bility according to the method described in Hedges et al. (2010).ensitivity analysis was performed by varying the within-study
orrelation among estimates from 0 to 0.9 in increments of 0.1.
Several studies are related since some areas have been studiedore than once. To account for this, analyses of overall effect size
nd meta-regressions were also done by including only one of the
able 1esults based on arcsine transformed risk difference for the random effects meta-analysariance estimate and robust variance estimate (RVE) according to Hedges et al. (2010).
Model Variable Estimate Std. err.
Random Overall mean 0.075 0.015
Random (RVE) Overall mean 0.096 0.025
Mixed Intercept 1.1539 0.3686
Distance −0.0044 0.0014 −Latitude −0.016 0.0058 −
Mixed (RVE) Intercept 1.3 0.64
Distance −0.0045 0.0016 −Latitude −0.018 0.010 −
icators 36 (2014) 483– 490 485
related studies. The study with highest number of effect sizes waschosen (Study A, C, E, F, G, K).
2.5. Bioindicator sensitivity
Meta-analyses were conducted for three groups with as nearequal sample size as possible and of similar distance intervallength to get an approximation of the bioindicator sensitivity:(1) 0–10 km; (2) 11–20 km; (3) 21–30 km. For the three distancegroups, potential within-study correlation was ignored because ofthe small sample number (13–14 studied sites).
3. Results
3.1. Data collection
Eleven field studies that have used embryo development inM. affinis as a bioindicator of chemical pollution met the inclu-sion criteria. The studies were conducted between 1978 and 2011and include data from 64 sampled sites and a total of more than50,000 analyzed embryos (Appendix A; Table A1). The first principalcomponent effectively summarized the variation in contaminantconcentrations (Appendix B; Table B1) and the component scoresshowed a strong correlation with the distance from the pollutionsource for all three sites included in the analysis (Figure B1).
3.2. Overall effect size
Across all studies, the overall effect size based on arcsinetransformed risk difference was 0.075 (95% confidence interval,0.046–0.10), indicating that overall effect size is significantly higherthan zero (Table 1). In all coastal areas except one, significant effectswere seen in at least one site (Fig. 1). There was considerableheterogeneity among effect sizes (Q (df = 41) = 516.7, p < 0.0001).Accounting for within-study correlation, the overall effect size was0.096 (95% confidence interval, 0.039–0.15) (Table 1).
3.3. Explanatory factors
No significant correlation was detected between the proportionof malformed embryos at reference sites and latitude (Spear-man’s rho = 0.0089, p = 0.97), depth (Spearman’s rho = 0.29, p = 0.28)or year (Spearman’s rho = −0.096, p = 0.72) (Appendix C; Fig. C1a–c and Table C1). A significant negative relationship was foundbetween the observed effects and distance from point sources(adjusted R2 = 0.17, p = 0.0046) (Fig. 2). Adding latitude into themodel accounts for an additional 10% of the variability in the data(adjusted R2 = 0.27, p < 0.001) (Table 1). The result was similar whenaccounting for within-study correlation, however, latitude was no
more significant (p = 0.12) as an explanatory variables (Table 1).Thewithin-study correlation was arbitrarily set to the default value of0.8 because the sensitivity analysis showed a marginal differenceacross the range of values from 0.1 to 0.9 (heterogeneity among
es and mixed effects meta-regressions. Result are given for models using ordinary
Fig. 1. Forest plot showing the observed effects (risk difference) at 42 sites in polluted coaof the estimates.
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Distance from point source(s) (km)
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Fig. 2. The relationship between weighted effect sizes (risk difference) and distancefrom point source(s). The circles for observed effects are drawn proportional to theprecision of the estimates.
stal areas. The symbols for observed effects are drawn proportional to the precision
true effects = 0.0060–0.0062).Year of study does not show any sig-nificant relationship with effect size. Depth showed a significantcorrelation with the distance from point sources and was thereforenot included in the analyses.
Only including one of the studies that have been conducted inthe same area (Study A, C, E, F, G, K) gives an overall effect sizeof 0.058 (95% confidence interval, 0.031–0.085). A meta-regressionincluding distance from point source and latitude as explanatoryvariables also shows a significant negative relationship with dis-tance from point sources (p = 0.033), and a negative borderlinesignificant relationship with latitude (p = 0.057).
3.4. Bioindicator sensitivity
The stratification of effect sizes into three groups based on dis-tance from point source is shown in Fig. 3. The result indicatesthat significant effects are generally seen up to11–20 km from pointsources.
4. Discussion
4.1. Robustness of study
Meta-analysis is a widely used tool in several scientific dis-ciplines, particularly within medical and social science. Despite
M. Reutgard et al. / Ecological Ind
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km (8 studies, 13 sites)
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Fig. 3. Forest plot showing the overall effects size (risk difference) and 95% confi-dence interval at three distance intervals: 0–10 km, 11–20 km and 21–30 km frompbp
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oint sources. The number of studies and sites in each distance interval is given inrackets to the left. The symbols for observed effects are drawn proportional to therecision of the estimates.
ometimes obvious similarities in questions addressed, it is stillelatively uncommon in the field of ecotoxicology. In the presenttudy, biological effect data that naturally have a high variabilitynd low effect size was successfully combined to extract statisti-ally defensible conclusions. The meta-analytic approach enableds to more robustly test the existing hypothesis of a cause-and-ffect relationship between malformed embryos and chemicalollution in the field. The meta-analytic approach also allowed us toxplore geographical and temporal variations that would not haveeen possible due to a limited sample size in single studies. Theesult demonstrates that the bioindicator malformed embryos in. affinis holds important key features that make it a useful bioindi-
ator for monitoring and assessment of contaminant effects in thealtic Sea.
The underlying assumption of sampling independence is vio-ated when studies include more than one polluted site per study,nd are compared to a common reference site within the sametudy. To account for within-study variability, results were com-ared with a method for robust variance estimation of dependentffect size estimates (Hedges et al., 2010). The comparison showshat accounting for within-study variability gives similar result foristance from point sources as an explanatory factor, but latitude isore uncertain (p = 0.12). Results are also similar when taking into
ccount that studies are related when the same area have beentudied more than once.
The current meta-analysis includes both published and unpub-ished data and is therefore not subject to publication bias fromhe fact that statistically significant results are more likely to beublished than data that supports a null hypothesis.
.2. Causes of the observed effects
An established cause and effect relationship between a bioindi-ator response and stressor is a prerequisite for any type ofioindicator (van der Oost et al., 2003). For biological effect indica-ors intended to be used in monitoring programs it is also importanthat the indicators have shown to respond to exposure in theeld (ICES, 2007). The meta-analysis demonstrates that the over-ll effect size is significantly higher than zero, which means that
he proportion of malformed embryos is generally higher in chem-cally polluted coastal waters than in reference sites (Table 1). Theandom model of untransformed risk difference indicates a riskifference of 0.033 (0.033, 95% confidence interval, 0.020–0.046)
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(Fig. 1). This risk difference implies a nearly doubled risk of malfor-mations in chemically polluted areas compared to reference areaswhich shows a mean proportion of malformed embryos of 0.035(Appendix C; Table C1). In addition, the meta-regression showsthat malformed embryos responds in a concentration-dependent(dose-effect) manner in that the effect size decreases significantlywith the distance from known sources of chemical pollution (Fig. 2and Table 1). These results, together with results from previouslaboratory studies showing that embryo malformations in M. affi-nis arise from exposure to both metals and organic contaminants,strengthens the existing hypothesis that there is a cause and effectrelationship between embryo malformations in M. affinis and con-taminants in the field (Eriksson et al., 1996; Eriksson Wiklund et al.,2005; Sundelin, 1983a,b, 1988, 1989). It has previously been shownthat the proportions of malformed embryos in M. affinis are signif-icantly higher at sites close to industrial effluents (Sundelin andEriksson, 1998). However, the number of sampled stations havetypically been low (mean = 5.3 sites/studied area), which result inlow statistical power for analyzing the relationship with potentiallyrelated factors. The meta-analysis allowed us to combine nearly allstudies in a single analysis and increase the statistical power, andtherefore also the weight of evidence concerning the main hypoth-esis of cause-effect relationship between anthropogenic chemicalpollution and embryo malformations.
The principal component analyses on hazardous substancesshow that a majority of the variation in these data sets can beexplained by a single principle component, which in turn corre-lates strongly with the distance from point sources (Appendix B;Table B1). These results suggest that distance from point sourceis a reasonable proxy for degree of chemical pollution. The meta-regression indicates that about 17% of the variation among effectsizes is explained solely by the distance from point sources. How-ever, it should be noted that the distance from point sources is acoarse estimate of the actual site-specific chemical exposure. Site-specific chemical exposure in any given area at a certain distancefrom point sources will obviously depend on e.g. discharge load,abiotic and biotic factors such as dilution, current characteristicsand food-chain variations between sites. The correlation coefficientmay therefore be seen as a conservative estimate of the actual rela-tionship between contaminants and embryo malformations in M.affinis.
4.3. Specificity
The ecological relevance of a biomarker or bioindicator isgenerally assumed to increase with increasing level of biologi-cal organization. However, the increase in ecological relevance ofbiomarkers and bioindicators is often accompanied by a decreasedsensitivity, specificity, and a limited understanding of the cause andeffect relationships (Clements, 2000).
Studies of the etiology of embryo malformations in M. affinis andother amphipods are limited, but it is well known that embryo mal-formations have a multi-causal nature in various animals includinghumans (Beckman and Brent, 1984; Brent, 2004; Hall and Reed,1982; Kovacic and Somanathan, 2006; Paskova et al., 2011). Thisis in line with previous laboratory studies, which have shown thatembryo malformations in M. affinis arise from exposure of bothmetals and organic contaminants (Eriksson et al., 1996; ErikssonWiklund et al., 2005; Sundelin, 1983a,b, 1988, 1989). Concordantly,the present study shows that malformed embryos in M. affinis areubiquitous in the studied coastal areas despite different sources of
chemical pollution. These facts suggest that embryo malformationsin M. affinis are a general bioindicator for various types of chemicalpollutants that can provide a wide and comprehensive assessmentof all potentially toxic chemicals.
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.4. Specificity: environmental factors
The results from the meta-regression indicate that the effectize, i.e. the difference in proportion of malformed embryosetween reference and chemically polluted site, increases fromorth to south (Table 1). There are several plausible explanationsnd it is not possible to elucidate what caused this trend. Thencreased effect size could be due to an interactive effect betweennvironmental factors and hazardous substances causing toxicityo increase southwards. However, at reference sites, proportion of
alformed embryos did not show any significant association withatitude. The studied area is characterized by a temperature and
salinity gradient, both declining northwards (Voipio, 1981). Aigher temperature and a higher nutrient load in the south increaseroductivity, which in turn also makes the southern part morerone to low levels of oxygen and hypoxia in the bottom water.
Eriksson Wiklund and Sundelin (2001) studied the effects ofow oxygen concentrations in M. affinis. The study indicated, elu-ively due to low sample size, that low oxygen levels (3.6 ± 1.3SD) mg O2 l−1) may cause malformed embryos. In contrast, fieldata from non-polluted monitoring stations have not indicatedny correlation between low oxygen levels (min 3.0 mg O2 l−1) andalformed embryos (Eriksson Wiklund and Sundelin, 2004). The
elationship between low oxygen levels and embryo malformationss currently not very well understood but it cannot be ruled out thathe generally lower oxygen levels in south have contributed to theouthward increase in the effect size.
Previous studies have shown that temperature stress duringonad and embryo development can cause serious harm to repro-uctive success. Increased temperature has been found to impairexual maturation, decrease fertilization success and fecundity,nd increase the proportion of undifferentiated and dead embryosEriksson Wiklund and Sundelin, 2001; Jacobson et al., 2008). How-ver, no effect of increased temperature has been documented onhe proportion of malformed embryos.
The effect of salinity has not been studied on the proportion ofalformed embryos. M. affinis is an euryhaline species which can
e found in both fresh and brackish water up to a salinity of about8 psu, and laboratory studies have shown that M. affinis can tol-rate salinities over 20 psu (Filippov, 2006). Several studies havehown that euryhaline species have the highest resistance to toxichemicals at isosmotic conditions, presumably due to a minimizedsmotic stress (Hall and Anderson, 1995). The most southern sam-led stations have a salinity of 6–7 psu, considerably lower thanhe salinity tolerance limit, and closer to isosmotic for M. affinisSchmidt-Nielsen, 1997). This suggests that a higher salinity wouldather decrease toxicity southwards and it is therefore not likelyhat salinity can explain the increased occurrence of malformedmbryos. Furthermore, toxicity of metals is known to generallyncrease at low salinity due to a higher bioavailability of metal ionstoxic form) at lower salinity conditions (Hall and Anderson, 1995).
The main purpose of environmental in situ monitoring andssessment using biological effect indicators of hazardous sub-tances is to study the overall biological effects of exposure toll potentially harmful substances, taking into account additive,ynergistic and antagonistic interactions between substances, andnvironmental factors. However, an indicator response can be con-ounded if a response can be induced by natural environmentalactors unrelated to contaminants (Lam, 2009). The usefulness ofiomarkers and bioindicators will therefore also depend on speci-city in terms of the ability to separate effects of chemical pollution
rom other environmental factors. The results from this study and
he previous studies above suggests that environmental factorsuch as temperature, salinity and oxygen concentrations are notikely to be key explanatory factor for malformed embryos in M.ffinis.
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4.5. Solutions to meet the multi-causal nature of embryomalformations
The problem of distinguishing between effects of chemical pol-lution and a potential effect of low oxygen levels may partly beovercome by combining different reproductive variables. Deadbroods in M. affinis have been shown in laboratory and field stud-ies to be related to low oxygen levels, while no relationship hasbeen observed between chemical pollution and dead broods. Thus,by combining these two reproduction variables in M. affinis thatdiffers in sensitivity to various stressors, it may be possible to dis-criminate between effects of chemical pollution and stress fromlow oxygen levels (Eriksson Wiklund and Sundelin, 2004). How-ever, if the frequency of both dead broods and malformations arehigh, it is difficult to distinguish whether effects are due to con-taminants and/or oxygen stress. Interpretation must in this caseintegrate information on the level of hazardous substances andoxygen levels during gonad and embryo development.
The bioindicator malformed embryos in M. affinis may be furtherimproved if used in an integrated approach together with otherbiomarkers and bioindicators. Recent studies have indicated thatthe uncertainty of influencing environmental factors on M. affinismay be overcome by using subcellular biomarkers that generatemore stressor-specific information. Gorokhova et al. (2010, 2013)found that some biomarkers in M. affinis were more strongly relatedto contaminants (acetyl choline esterase, glutathione-s-tranferase,superoxide dismutase), while other responded to hypoxia, anda combination of both hypoxia and contaminants (lipid peroxi-dase status). The combination of these low level biomarkers withembryo malformations at higher level could potentially provide avaluable monitoring tool that is both general, and has the abilityto diagnose likely cause. Moreover, applying a suit of biomarkersand bioindicators at different levels of biological organization isgenerally agreed among ecotoxicologists to be needed in order tojustly assess and monitor ecological integrity (Adams et al., 2001;Clements, 2000; Galloway et al., 2004; Thain et al., 2008). This is alsoin line with the ecosystem approach of the European CommisionMarine Strategy Framework Directive with regard to Descriptor 8,which denotes that chemical pollution effects ought to be consid-ered at various biological levels of organization, taking into accountinteraction effects of both abiotic and biotic factors (Law et al., 2010;Lyons et al., 2010).
4.6. Sensitivity
Stratifying the studied sites into three groups based on distance,0–10, 11–20 and 21–30 km from known point sources, indicatesthat the method is sensitive enough to detect biological effects ona regional scale (>10 km) (Fig. 3). One of the studied sites indi-cates significant effect as far as 29 km from known point source(Site C/3, Fig. 1). It is impossible from the present analysis to com-pare the relative sensitivity of embryo malformations in M. affinisas a bioindicator compared to other biomarkers or bioindicators.However, previous studies in polluted coastal waters along thenorthern Baltic Sea have demonstrated biological effects at a com-parable distance from sources of chemical pollution at differentlevels of organization in both flora and fauna. For example, Kautsky(1992) showed effects on phytobenthic communities in terms ofdecreased growth rates and diversity in areas closer than 7.5 kmfrom pulp-mill effluent discharges. Andersson et al. (1988) indi-cated effects in perch (Perca fluviatilis) at subcellular (cytochrome
P-450 induction), and organ level (reduced gonad size and hema-tological alterations) in the range of 5–10 km from a kraft bleachplant. Balk et al. (1993) indicated significant effects (gonadosomaticindex, hematocrit, and ethoxyresorufin-0-deethylase activity) of
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leached kraft mill effluents as far as 20–40 km from dischargeoint.
.7. Future research
The present study indicates that there is a relationship betweenatitude and embryo malformations, and controlled experimentsave shown that low oxygen levels may have the potential to inducembryo malformations (Eriksson Wiklund and Sundelin, 2001).hese uncertainties and the fact that malformations are generallynown to be of multi-causal nature show that there is a need to fur-her investigate the relationships between environmental factorsnd malformations in M. affinis.
Further research is also needed to better understand sub-ellular effects of hazardous substances and environmental factorsn M. affinis, and whether these sub-cellular effects propagateo higher levels of biological organization, such as malformedmbryos. Integrating the bioindicator malformed embryos withore stressor specific biomarkers has the potential to increase
he weight of evidence by improving our ability to discriminateetween effects of hazardous substances and environmental fac-ors.
Amphipods are found throughout the world in polar, temper-te, and tropical regions, occupying both terrestrial and aquaticnvironments, and covering many functional groups with regardo their mode of feeding and habitats (Lincoln, 1979). All amphi-od species display a similar embryo development and the methodould therefore potentially be developed for monitoring and assess-ents of biological effects of contaminants for a wide variety of
mphipod species and pollution problems (Sundelin et al., 2008).
. Conclusions
We conclude that embryo malformations in the benthic amphi-od M. affinis fulfill a number of important bioindicator criteriand can therefore provide useful information for management andnvironmental policy: (1) the present study supports evidencerom controlled laboratory studies that there is a cause and effectelationship between embryo malformations and contaminants;2) the study suggests that the indicator is contaminant-sensitivend can therefore serve as an early warning of biological effects ofontaminants in the field; (3) the indicator is general suggestinghat the indicator has capability to monitor and detect effects of aide variety of known, and yet unknown, chemical contaminants.
he usefulness is further strengthened by the facts that M. affiniss a widely distributed species that plays a fundamental role in thealtic Sea ecosystem.
cknowledgements
We want to give special thanks to Professor Elena Gorokhovaor giving valuable input on the manuscript. I also want to thankor funding from the Swedish Environmental Protection Agencynd the European Community’s Seventh Framework ProgrammeFP/2007-2013) under Grant agreement no. 217246 made withhe joint Baltic Sea research and development programme BONUSBEAST project).
ppendix A. Supplementary data
Supplementary data associated with this article can be found,n the online version, at http://dx.doi.org/10.1016/j.ecolind.2013.8.021. These data include Google maps of the most importantreas described in this article.
icators 36 (2014) 483– 490 489
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