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Estuarine, Coastal and Shelf Science 79 (2008) 679–686

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Estuarine, Coastal and Shelf Science

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Relationship between heavy metal accumulation and geneticvariability decrease in the intertidal crab Pachygrapsusmarmoratus (Decapoda; Grapsidae)

Sara Fratini a,*, Lorenzo Zane b, Lapo Ragionieri a, Marco Vannini a, Stefano Cannicci a

a Department of Evolutionary Biology ‘‘L. Pardi’’, University of Florence, v. Romana 17, 50125, Firenze, Italyb Department of Biology, University of Padova, Via G. Colombo 3, I-35121, Padova, Italy

a r t i c l e i n f o

Article history:Received 25 March 2008Accepted 16 June 2008Available online 24 June 2008

Keywords:bioaccumulationheavy metalsMediterranean basinmean d2

microsatellitesnatural marine populations

* Corresponding author.E-mail addresses: sarafratini@unifi.it (S. Fratini), l

lapo.ragionieri@unifi.it (L. Ragionieri), vannini_m@ucannicci@unifi.it (S. Cannicci).

0272-7714/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.ecss.2008.06.009

a b s t r a c t

The ‘‘genetic erosion’’ hypothesis posits that heavy metal stress is related to a loss of genetic diversity atthe population level. The genetic diversity of natural populations can, however, be affected by naturalprocesses as well as by human impact. We studied the relationship between heavy metal bio-accumulation and genetic variability in the intertidal crab Pachygrapsus marmoratus. Tissue samples werecollected from 40 individuals inhabiting four polluted and four unpolluted sites along the Tuscan coast(Mediterranean basin), and were examined for four heavy metals (arsenic, As, cadmium, Cd, lead, Pb, andcopper, Cu). We also assessed the genetic variability of 235 crabs from the same localities using sixmicrosatellite loci.Our results show that the bioaccumulation levels of these individuals accurately reflect the levels ofpollution in their immediate environment, and that heavy metals accumulate more in the hepatopan-creas than in the gills. Moreover, populations from polluted sites have significantly less genetic vari-ability, measured as mean standardized d2, and a significantly lower percentage of unrelated individuals,than populations from unpolluted sites. This evidence supports the ‘‘genetic erosion’’ hypothesis formetal heavy exposure in natural environments.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

The standing levels of genetic diversity in populations contrib-ute to species’ long-term viability in several ways (Frankham, 1995).First, the ability of populations to adapt to changing environmentsis directly dependent on their amount of genetic diversity. Second,small populations that lose genetic diversity may have reducedfitness and increased risk of extinction. Populations that areadapted to local conditions may also become less fit if individualsfrom other areas that are adapted to different conditions areallowed to interbreed with them.

Genetic diversity can be altered by an extensive array of naturalprocesses. Gene flow between populations, interspecific hybrid-isation processes, and new mutations generally increase geneticvariability; while geographic isolation, bottleneck events and nat-ural selection, especially when induced by sudden environmentalchanges, usually lead to its dramatic reduction.

orenz@bio.unipd.it (L. Zane),nifi.it (M. Vannini), stefano.

All rights reserved.

Human impact and habitat alteration and loss can also inducegenetic changes in natural populations (for a review, see Bickhamet al., 2000; Belfiore and Anderson, 2001): populations can besubject to a reduction in their genetic variability because of theirfragmentation and loss of numbers (bottleneck effect) and theeffect of selective pressure. On the other hand, populations can besubject to an increase in genetic variability as a consequence of anincrease in the induced mutagenesis rate, for example, followingexposure to radioactive materials (Ellegren et al., 1997). Natural andanthropogenic environmental changes acting together lead tochanges in genetic diversity, both within and among populations.Thus, measuring genetic diversity can provide insights into theconsequences of environmental change.

The initial studies on the effects of contaminants at the geneticlevel focused on the mechanisms of DNA damage, testing organ-isms under laboratory conditions (see Belfiore and Anderson,2001). Genetic ecotoxicological studies (i.e., those studies focusedon genetic changes at the population level) dating from the mid-1980s underline that exposure to several pollutants inducesremarkable changes in intrapopulation genetic variability. Thesestudies also stress the importance of considering the evolutionaryconsequences that anthropogenic alterations can have on theenvironment (Ross et al., 2002; Whitehead et al., 2003; Berckmoes

Fig. 1. Map of Tuscany showing the study sites. White circles indicate the reference(unpolluted) and black circles the polluted sites. Population abbreviations are as inTable 1.

S. Fratini et al. / Estuarine, Coastal and Shelf Science 79 (2008) 679–686680

et al., 2005; Domınguez-Domınguez et al., 2005; Maes et al., 2005;Garderstrom et al., 2006; Pineira et al., 2007).

In this study, we investigated the effect of heavy metal exposureand bioaccumulation on genetic variability in the intertidal crabPachygrapsus marmoratus, testing the ‘‘genetic erosion’’ hypothesis.This hypothesis states that heavy metal stress may cause a loss ofgenetic diversity (Van Straalen and Timmermans, 2002). Toxicantexposure may alter the genetic composition of a populationfavouring more tolerant genotypes, it may alter migration patternsand/or cause genetic bottlenecks (Van Straalen and Timmermans,2002). To evaluate these effects, the total concentrations of fourheavy metals (arsenic, As, cadmium, Cd, lead, Pb, and copper, Cu)from two body organs (the gills and the hepatopancreas) werecorrelated with the pattern of genetic variability at six micro-satellite loci. To consider both the effect of human impact and thenatural biogeographic background, especially important for marinespecies with high dispersal ability and big effective population sizes(Whitehead et al., 2003; Maes et al., 2005), our sampling procedurewas designed to include sites from different geographic areas aswell as sites with different levels of metal accumulation.

2. Material and methods

2.1. Study species

Pachygrapsus marmoratus (Fabricius, 1787) is an intertidal crab,common and abundant throughout the Mediterranean basin (Zar-iquiey Alvarez, 1968; Ingle, 1980) where it occupies the upper andmiddle levels of rocky shores. It lives in the crevices of breakwatersand on pier pilings, often found in ports and marinas.

Adults are relatively sedentary being faithful to a specific areathat they actively defend from co-specific intrusion, the extent ofwhich depends on the sex and size of the owner (Cannicci et al.,1999). On the other hand, the species has a high dispersal potentialdue to a larval planktonic stage lasting about 4 weeks (Cuesta andRodriguez, 2000). In addition, the species is found in both pollutedand unpolluted areas, and it accumulates heavy metals, mainly inthe hepatopancreas (Mouneyrac et al., 2001). All these character-istics make P. marmoratus a good model species to investigate theeffects of both the natural biogeographic environment and humanimpact at a genetic level.

2.2. Sampling sites and procedures

The coastline of Tuscany is about 400 km long and is mainlycharacterised by low- or medium-energy sandy shores and shallowflats. The few high-energy rocky shores are represented by fivepromontories projecting from the main coastline neatly divided bywide low-energy gulfs. On the basis of these geomorphologic con-straints we selected eight discrete sites, four of which are purport-edly subject to heavy metal pollution (hereafter polluted sites) andfour sites are not impacted by heavy metals (hereafter, referencesites) (Fig. 1, Table 1). The designation of polluted or reference siteswas determined on the basis of data from the Tuscan EnvironmentalAgency (ARPAT), which has been monitoring the pollution levels ofthe Tuscan coast for several years (Ferrara et al.,1993; Renzoni et al.,1993; Dall’Aglio et al., 2001). Although the selected polluted siteshad different environmental features, all of them were charac-terised by water concentrations of Cd and Pb ranging from 20 to30 ng/l and from 140 to 150 ng/l, respectively; while our referencesites were characterised by concentrations 2–3 times lower (e.g.,about 8.5 ng/l and 82 ng/l of Cd and Pb, respectively) (Renzoni et al.,1993). The polluted sites consisted of a large industrial port, Livorno(PL), a mixed commercial/tourist harbour, Porto Santo Stefano (PSF),a large marina near a metallurgic area, Porto di Piombino (PP), anda small creek affected by an anomalous concentration of heavy

metals accumulated from the Piombino metallurgic activities due toprevailing winds and surface marine currents, Cala Violina (CV)(Leoni and Sartori, 1997). A reference site was chosen from the samearea as each of the polluted sites (see Fig. 1). This was not, however,possible for the Cala Violina creek site (CV) as most of the gulf isdominated by sandy beaches and the influence of sedimentspolluted by heavy metals is strong throughout the gulf. Thus, weselected a reference site further south (VD).

2.3. Heavy metal accumulation assessment

Five P. marmoratus adult males at the intermolt stage with car-apace of 15–20 mm wide were collected from each site, for a total of40 crabs. The crabs were frozen at �20 �C for 12 h and then dis-sected. The hepatopancreas and gills were separated and preservedfor the analyses of heavy metal concentrations. Total concentra-tions of As, Cd, Pb and Cu in each organ were determined by Plasmaemission spectrophotometer (Perkin Elmer Optima 2000 DV). Theaccuracy of the procedure was assessed by simultaneous digestionand analysis of standard reference materials (SRMs). SRM 1566a‘‘oyster tissue’’ from the National Institute of Standards and Tech-nology (NIST, Gaithersburg, USA) was used. The results areexpressed in mg metal g�1 wet weight of the organ.

2.3.1. Bioaccumulation data analysesA two-way full factorial Permutational Multivariate Analysis of

Variance (PERMANOVA, Anderson, 2001) was used to test the nullhypothesis of no difference in As, Cd, Pb and Cu accumulation be-tween the two groups of locations (polluted vs. reference sites) andbetween the two organs (gills vs. hepatopancreas). As a secondstep, Canonical Analysis of Principal coordinates (CAP, Andersonand Robinson, 2003; Anderson and Willis, 2003) was used asa constrained ordination procedure to visualise patterns of differ-ence in heavy metal accumulation between the two groups ofcapture locations and between the two organs. The similaritiesmatrix for each pair of samples was calculated using Euclidean

Table 1Pachygrapsus marmoratus study populations. GPS coordinates, metal pollution status (po¼ polluted; ref¼ reference, i.e., unpolluted), number of individuals analysed (N),allelic richness per locus (Na), number of private alleles (Np: present only in one population), standardized mean d2 values (d2), percentage of unrelated individuals (% un-r),observed heterozygosity (Ho), expected unbiased heterozygosity (He) and P-value of departure from Hardy–Weinberg equilibrium (P) are reported for each population. All thegenetic diversity indices were calculated after correction for null alleles

# Population GPS coordinates Metal pollutionstatus

N Na Np d2 % un-r Ho He P

PL Porto di Livorno 43� 320 47.2400 N 10� 170 56.4700 E po 30 9.57 2 0.578 0.837 0.71 0.68 0.89C Calafuria 43� 280 15.8000 N 10� 200 02.8000E ref 30 9.41 2 0.746 0.848 0.71 0.70 0.36PP Porto di Piombino 42� 560 09.1000 N 10� 320 28.5600 E po 30 8.64 2 0.572 0.834 0.68 0.67 0.46GB Golfo di Baratti 42� 590 39.4700 N 10� 29049.0400 E ref 26 10.10 1 0.593 0.88 0.71 0.73 0.45CV Cala Violina 42� 510 17.0800 N 10� 460 22.4900 E po 29 9.05 1 0.557 0.852 0.73 0.72 0.64VD Villa Domizia 42� 260 06.3700 N 11� 090 09.2600 E ref 30 10.10 2 0.613 0.869 0.69 0.69 0.77PSF Porto Santo Stefano 42� 260 11.7500 N 11� 070 24.7500 E po 30 9.43 2 0.581 0.839 0.71 0.70 0.45LV Le Viste 42� 230 08.8100 N 11� 120 32.0800 E ref 30 9.22 2 0.713 0.869 0.71 0.69 0.75

S. Fratini et al. / Estuarine, Coastal and Shelf Science 79 (2008) 679–686 681

Distance on ln (xþ 1) transformed data. Multivariate analyses wereperformed using the FORTRAN programs PERMANOVA (Anderson,2005) and CAP (Anderson, 2004).

2.4. DNA extraction and microsatellite amplification

A total of 235 individuals were collected for genetic analysis. Foreach individual, either a cheliped or a pereopod was removed andplaced in a vial containing absolute ethanol. The animals were thenreleased into their natural environment from where they had beencaptured and they will reform the lost appendage at their next moult.

Total genomic DNA was extracted from the muscle tissue usingthe Qiagen tissue DNA extraction kit. The DNA was eluted either inthe buffer supplied with the kit or in water, and kept at þ4 �C forshort-term use or at �20 �C for long-term storage.

We screened for genetic diversity at six microsatellite loci (pm-101, pm-108, pm-79, pm-187, pm-183 and pm-99: Fratini et al.,2006), specifically isolated from a P. marmoratus enriched library bythe authors. Details on marker isolation and conditions for micro-satellite amplifications are reported in Fratini et al. (2006).

Microsatellite polymorphism was detected using a fluorescentdetection method: forward primer for each locus was 50-labelledwith one of four fluorophores (TET, HEX, FAM, NED). The labelledPCR products from the six loci were then divided into two sets (pm-101 TETþ pm-108 HEX; and pm-79 HEXþ pm-183 FAMþ pm-187NEDþ pm-99 FAM). For each set, 1–5 ml of each PCR product wascombined with H2O in a final volume of 10 ml for successive di-mensional analysis. Sizing was done in an ABI Prism 310 GeneticAnalyzer (Applied Biosystems) at the C.I.B.I.A.C.I (University ofFlorence) by reference to an internal size standard (TAMRA500 forthe set TETþHEX, and ROX400 for the set HEXþ FAMþNED) usingGENOTYPER ver. 3.7 and GENESCAN ver. 3.7 (Applied Biosystems).

2.5. Genetic data analysis

The observed and expected heterozygosity, the number of al-leles and the allelic richness for each locus and population werecalculated using FSTAT ver. 2.9.3 (Goudet, 1995).

We also calculated two other measures of population variability.First, to take into account the stepwise mutational model of micro-satellites, we calculated for each individual the standardized meand2 (Coulson et al., 1998; Tiira et al., 2006), which is the square of thedifference in repeat units between two alleles at one locus, stan-dardized with its locus specific standard deviation, and averagedbetween loci. The standardisation removes, in part, the much largercontribution of the more variable loci to the multilocus d2 estimates(Coulson et al., 1998). As a last step, the standardized d2 values wereaveraged for each population and the mean value was taken asa measure of genetic variability. Second, to determine whether thesamples were random or made up of related family groups,

a pairwise comparison was made within each population using thesoftware ML-RELATE (Kalinowski et al., 2006) which estimated thepercentage of unrelated individuals of each population pair-by-pair.The unrelated pairs were identified by log-likelihood ratios takinginto account the incidence of null alleles (Wagner et al., 2006).

Hardy–Weinberg (HWE) and linkage equilibrium among loci wasassessed for each population using GENEPOP ver. 3.4 (Raymond andRousset, 1995; on line version at http://wbiomed.curtin.edu.au/genepop). We used MICROCHECKER 2.2.3 (Oosterhout van et al.,2004) to evaluate whether heterozygote deficiencies may beexplained by the existence of null alleles, using equation 2 fromBrookfield (1996). We then used the software FREENA (available athttp://www.montpellier.inra.fr/URLB) to compute a genotype dataset corrected for null alleles following the INA method described inChapuis and Estoup (2007). This new data set was used in all furtheranalyses, and for recalculating the measures of population variability.

Genetic differentiation was estimated by the exact test of pop-ulation differentiation (Raymond and Rousset, 1995) implementedin GENEPOP and by AMOVA (Excoffier et al., 1992) using ARLEQUINver. 2.000 (Schneider et al., 2000). In addition, following recentsuggestions (Ryman, 2006), the hypothesis of no-differentiationamong populations was also tested using the Pearson’s chi-squaremethod as implemented in CHIFISH (Ryman, 2006).

The exact test of population differentiation was performed ondifferences in allele frequency at each locus and for each populationpair. Single locus P-values were calculated using a Markov chainwith 1000 batches and 1000 iterations per batch, combined overloci using the Fisher method, and corrected using the Bonferronicorrection for multiple tests.

The AMOVA procedure was designed to assess the partitioningof genetic variation among groups of populations defined by theirgeographic location and level of pollution. Significance values werecomputed by permutation tests from 10,000 random permutations.

Correlations between genetic (Fst values) and geographical(calculated as effective distance by sea) distances were evaluated bythe Mantel test, as implemented in ARLEQUIN, with 10,000 randompermutations.

Finally, genetic diversity indices (observed heterozygosity, allelicrichness, standardized mean d2) and the percentage of unrelatedpairs of each population were compared between populations frompolluted and reference sites using a one-way ANOVA as imple-mented in GMAV5 software (University of Sydney, Australia).

3. Results

3.1. Heavy metal accumulation assessment

Individuals from all of the studied P. marmoratus populations hadaccumulated As, Cd, Pb and Cu both in the gills and in the hepato-pancreas (Fig. 2). The values of pollutants were higher in crabs

Metal co

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reference polluted050100150200250300350400450

reference polluted

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CuPb

Cdgillshepatopancreas

Fig. 2. Accumulation of As, Cd, Pb and Cu in the gills and hepatopancreas of adult P. marmoratus individuals at the intermolt stage.

S. Fratini et al. / Estuarine, Coastal and Shelf Science 79 (2008) 679–686682

collected from polluted sites in all cases except for the accumulationof As in the gills (Fig. 2A). The concentration of all pollutants washigher in the hepatopancreas than in the gills in all cases except forthat of Pb at the polluted sites (Fig. 2C). However, the two-wayPermutational MANOVA showed that the total bioaccumulation ofthe heavy metals was significantly higher at the polluted sites thanat the reference sites (MS¼ 8.72; F¼ 5.12; df¼ 1; P¼ 0.017), andthat the hepatopancreas had significantly higher concentrationsthan the gills (MS¼ 8.10; F¼ 4.75; df¼ 1; P¼ 0.021).

CAP analysis confirmed the PERMANOVA results, showinga significant effect of both pollution vs. reference site groups, witha squared canonical correlation of d2¼ 0.620 (P¼ 0.006), and organof accumulation, d2¼ 0.320 (P¼ 0.04). Moreover, the plot producedby visualising the values obtained on the new canonical axes cor-responding to the two main effects (Fig. 3) graphically separatedthe gills and hepatopancreas of the populations collected at thepolluted sites from the organs of the crabs colonising the referenceones. In particular, all samples from the polluted sites are charac-terised by negative values on both canonical axes, indicating highconcentrations of all heavy metals (Table 2).

-0.4

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-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.41° Canonical Axis for organ (δ2

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gills/referencegills/pollutedhepatop/referencehepatop/polluted

Fig. 3. Two-dimensional scatter plot of the canonical axis for the polluted and refer-ence site groups and of the canonical axis for accumulation organ of P. marmoratusoverall As, Cd, Pb and Cu bioaccumulation.

3.2. Pattern of genetic variation

All loci were highly polymorphic (number of alleles: 32 for pm-101, 13 for pm-108, 26 for pm-187, 19 for pm-183 and 18 for pm-99)with the exception of locus 79 for which we detected only twoalleles. All the populations were characterised by a high meannumber of alleles per locus, ranging from 9.0 to 10.5, and bya number of private alleles ranging from 1 to 2 (Table 1).

A linkage disequilibrium was evident from only two pairwisecomparisons of loci across all populations: the comparisons be-tween pm-183/pm-99 and pm-183/pm-101, due to a significantdeviation recorded in the populations PSF (P< 0.001) and PP(P< 0.001).

All the populations but one (CV) showed a significant hetero-zygosity defect. Considering the HWE results by locus and bypopulation, locus pm-101 highly deviated from HWE in all pop-ulations except one (CV) and locus pm-99 in three populations (PSF,VD and GB). The other four loci were at equilibrium in all of thepopulations. The observation of many cases of excess homozygosityat a single locus (pm-101) suggested the presence of null alleles.This was confirmed by analyses with MICROCHECKER. After cor-rection for null alleles with FREENA, all populations were at HWE(Table 1), and no population Fis value, calculated locus by locusfollowing Weir and Cockerham (1984), showed a significant de-parture from HWE (Probability test). For this reason, subsequentanalyses were performed on the corrected data set and the resultswere cross-checked with those obtained from the original data.

The Fisher’s exact test results rejected the hypothesis of ho-mogeneity between samples (c2¼ 23.4, df¼ 12, P¼ 0.02), but thiswas due only to one highly significant pairwise comparison

Table 2Correlation coefficients for individual pollutants with the canonical axes for organsof accumulation and for the effect on the two groups of sites, polluted vs. reference,i.e., the two axes utilised to plot the data in Fig. 3

Pollutant Correlation factor withcanonical axis for organ

Correlation factor with canonicalaxis for polluted vs. reference pop

As �0.2529 �0.8494Cd �0.3325 �0.9002Pb �0.68 �0.825Cu �0.8146 �0.7748

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Fig. 4. Allelic richness (A), observed heterozygosity (Ho, B), standardized mean d2

values (C) and percentage of unrelated individuals (D) in populations inhabiting pol-luted and reference sites. *Indicates a significant P-value (P< 0.05).

S. Fratini et al. / Estuarine, Coastal and Shelf Science 79 (2008) 679–686 683

(P< 0.001, following Bonferroni corrections) involving the pop-ulations VD and PP. The hypothesis of no-differentiation amongpopulations was also strongly rejected by the chi-square test(c2¼ 830.63, df¼ 742, P¼ 0.013), and this differentiation was notrelated to any specific locus.

An AMOVA performed without grouping the populations con-firmed the existence of significant though weak genetic subdivision(Fst¼ 0.003, P¼ 0.05). Population pairwise Fst indicated that pop-ulation PL is differentiated from LV and GB (data not shown).

Additional AMOVA were performed on populations groupedaccording to biogeographic (hypothesis 1 and 2), anthropogenic(hypothesis 3) and pairwise biogeographic and anthropogenic hy-potheses (hypothesis 4). The results, summarized in Table 3, showthat differentiation is associated only with geographic subdivision.The highest levels of differentiation were found when three groups,corresponding to northern, central, and southern Tuscany wereanalysed (hypothesis 2). Otherwise, the level of contamination isnot able to explain the partitioning of molecular variance intogroups of populations, also when it is considered in a biogeographiccontext (as revealing by the not significant value of Fct in thehypothesis 4).

We did not find any relation between pairwise genetic differ-entiations and geographic distances using the Mantel test (r¼ 0.11,P¼ 0.27). This means that the hypothesis of isolation-by-distance isnot appropriate for our data and other factors have to be consideredin order to explain the observed differentiation.

Considering the comparison of genetic diversity indices be-tween populations from polluted and references sites (Fig. 4), thepolluted group exhibits a significant reduction of genetic variabilitywhen expressed as mean standardized d2 values (dpo

2 ¼ 0.57� 0.01and dref

2 ¼ 0.67� 0.04: F¼ 6.76, df¼ 1, P¼ 0.04; ANOVA test), anda significant decrease in the percentage of unrelated individuals(unrelpo¼ 0.84� 0.006 and unrelref¼ 0.88� 0.007: F¼ 9.07, df¼ 1,P¼ 0.02; ANOVA test). The allelic richness appears to be lower inthe populations from polluted sites but this trend is not supportedstatistically (ARpo¼ 9.17� 0.21 and ARref¼ 9.71�0.23: F¼ 2.99,df¼ 1, P¼ 0.13; ANOVA test). Finally, no differences exist in Hovalues between the two groups of populations (Hopo¼ 0.71�0.01and Horef¼ 0.71�0.004: F¼ 0.00, df¼ 1, P¼ 0.95; ANOVA test).

4. Discussion

4.1. Heavy metal bioaccumulation and genetic variability

All P. marmoratus collected on the Tuscan coast evinced contactwith heavy metals such as As, Cd, Pb and Cu and had accumulatedthese pollutants in their tissue. These results thus clearly suggestthat virtually no areas along the coast of this Italian region are safefrom heavy metal pollution, a finding communicated more than 10

Table 3Analysis of molecular variance testing for partitioning the genetic variation under biogeographic and contamination hypotheses using six microsatellite loci. Degrees offreedom (df), sum of squared (SS), variance, percentage of total variation, F-statistics and P-values are reported. Significant P-values are shown in italic

Hypothesis Source of variation df SS Variance Variance (%) F-statistics P

1) Northern Tuscany (PL,C, PP, GB) vs. SouthernTuscany (CV, PSF, VD, LV)

Between groups 1 2.91 0.0023 0.11 Fct¼ 0.001 0.20Among populations within groups 6 14.28 0.005 0.24 Fsc¼ 0.002 0.11Among populations 462 952.22 2.09 99.65 Fst¼ 0.003 0.05

2) Northern Tuscany (PL, C,PP, GB) vs. Central Tuscany(CV) vs. Southern Tuscany(PSF, VD, LV)

Among groups 2 6.53 0.0082 0.39 Fct¼ 0.004 0.03Among populations within groups 5 10.66 0.007 0.03 Fsc¼ 0.003 0.37Among populations 462 952.22 2.09 99.65 Fst¼ 0.004 0.05

3) Polluted (PSF, CV, PL, PP) vs.reference (C, GB, VD, LV)

Between groups 1 2.81 0.0018 0.09 Fct¼ 0.0009 0.25Among populations within groups 6 14.38 0.005 0.25 Fsc¼ 0.0025 0.10Among populations 462 952.22 2.09 99.65 Fst¼ 0.003 0.05

4) Pairwise polluted vs.reference (PL vs. C; PP vs. GB;CV vs. VD; PSF vs. LV)

Among groups 3 7.59 0.0011 0.05 Fct¼ 0.005 0.38Among populations within groups 4 9.60 0.005 0.26 Fsc¼ 0.003 0.13Among populations 462 952.22 2.09 99.65 Fst¼ 0.003 0.05

S. Fratini et al. / Estuarine, Coastal and Shelf Science 79 (2008) 679–686684

years ago by Leoni and Sartori (1997) after analysis of the coastalmarine sediments. However, the crabs colonising the purportedlypolluted sites were characterised by a higher bioaccumulation thanthat of the crabs from the reference sites for all the studied pollut-ants, confirming that those sites were particularly exposed to As, Cd,Pb and Cu. Our study also confirmed that these intertidal crabs ac-cumulate heavy metals mainly in the hepatopancreas, as shown byMouneyrac et al. (2001). Moreover, the concentrations of Cu and Cdwe recorded were of the same order of magnitude of those reportedby Mouneyrac et al. (2001), with accumulation of Cu hundreds oftimes higher than the accumulation of the other elements. Finally,our results underline that individual levels of bioaccumulation in anattested bioindicator species may provide a good indirect estimateof the environmental quality of the seawater.

Analysis of the pattern of genetic variability revealed anthro-pogenically induced changes at the population genetic level.Overall, our data supports the ‘‘genetic erosion’’ hypothesis (VanStraalen and Timmermans, 2002): populations from polluted siteswere genetically less variable than those from reference sites. Thisis particularly evident when measuring genetic variability as meanstandardized d2 and when considering the percentage of unrelatedindividuals. However, the populations from the polluted sites didnot exhibit any differences in the heterozygosity values and allelicrichness when compared with populations from reference sites(notwithstanding the existence of a certain reduction trend).

Together with the highly correlated mean d2, the standardizedmean d2 has been proposed as an alternative to heterozygosity tomeasure genetic variability with microsatellite data (Coltman et al.,1998; Coulson et al., 1998). The application of these statistics todetect correlations between fitness and genetic variability is highlycontroversial. In fact, while several studies (i.e., Hansson et al.,2001; Rossiter et al., 2001; Tiira et al., 2006) report a significantpositive association between mean or standardized mean d2 andfitness-related traits, one meta-analysis study has shown thatmultilocus heterozygosity is a better measure of inbreeding (Colt-man and Slate, 2003). Coltman and Slate’s, 2003 study indicatedthat heterozygosity is suited to detecting recent inbreeding eventswhereas mean d2 may detect the influence of more ancestral eventssuch as admixture events. Considering the lack of differences inheterozygosity between the polluted and reference sites of thisstudy, our results obtained with standardized mean d2 seem toindicate that the observed reduction in genetic diversity reflectsa lower level of admixture in populations from polluted sites,expected if only adapted individuals can survive in these condi-tions. This scenario gains further support from the large proportionof related individuals found in the populations from polluted sites,expected in cases of limited and/or non-random recruitment (i.e.,some genotypes should be favoured over others). In this respect, itis important to note that the presence in our population samples ofgenetically related individuals, while in line with recent findings forother marine organisms (i.e., Carlsson et al., 2006; Garoia et al.,2007), contradicts the prediction of a large, randomly matingpopulation and requires further investigation. In turn, the weakdeficit of heterozygotes observed at all loci could be, at least in part,related to the presence of a subtle family structure in all oursamples.

The choice of the genetic markers for tracing the effect of con-taminants at the genetic level was difficult as we could not predicta priori the possible outcomes. We chose microsatellites since theyare considered to be one of the most useful approaches for this kindof study (see Belfiore and Anderson, 2001): it is known that they areneutral hypervariable markers, but it cannot be ruled out that theymay uncover polymorphism related to adaptation for linkage tocodon regions (for a review, see Belfiore and Anderson, 2001).

Few studies on the effects of environmental pollution have beenperformed on natural populations using microsatellites, and even

fewer (Domınguez-Domınguez et al., 2005; Maes et al., 2005) wereable to detect genetic differences between contaminated and ref-erence sites. Conversely, an increase in the mutation rate atmicrosatellite loci was found in barn swallows exposed to Cher-nobyl radiation (Ellegren et al., 1997), i.e., in really extreme con-taminant conditions. Thus the results presented here are novel,showing that microsatellites are able to record the genetic modi-fications coupled with heavy metal exposure and bioaccumulationin natural low-medium exposed populations.

Finally, our aim was to investigate the more prevalent heavymetals in the Tuscan environment as a gauge to the overall pollu-tion status. Other pollutants (e.g., other heavy metals or toxicorganic contaminants) are certainly present at both the pollutedand reference sites and may affect the biology of our study pop-ulations. Thus, while our results clearly support the ‘‘genetic ero-sion’’ hypothesis (Van Straalen and Timmermans, 2002), we cannotattribute the cause of such genetic variability reduction directly toany of the analysed heavy metals nor to any other specific pollutant.

4.2. Population genetic structure

Pachygrapsus marmoratus exhibits a weak but significant pop-ulation genetic structure along the Tuscan coast with the existenceof three genetically distinct groups of populations, correspondingto northern (PL, C, GB, PP), central (CV) and southern Tuscany (VD,PSF, LV). Moreover, the evidence of a non-panmictic population inthe study area is confirmed using different statistical methods (theexact test, F-statistics and a chi-square test).

It is important to note that the population genetic structure ofP. marmoratus in this region, as recorded by the AMOVA test, cannotbe explained on the basis of the pattern of exposure to contami-nants but on the basis of biogeography. Thus, the genetic effect ofcontaminant stress in partitioning the genetic variation is not themajor factor affecting the distribution of genetic variation. Thisresult is not surprising since our target species disperses via a larvalphase lasting about a month, i.e., a comparatively long span of timefor dispersion. It is likely that at each new generation populations inthe entire area recruit new individuals from a shared pool. Thepresence of many common genotypes among all the populationssupports this conclusion.

The genetic structure recorded is consistent with the geo-graphic barriers present in our study area. The Tuscan coast, infact, is washed in the north by the Ligurian Sea and in the south bythe Tyrrhenian Sea (Fig. 1). Thus, the northern and southern Tus-can coasts belong to two chemically, physically and ecologicallyvery distinct Mediterranean basins, connected via the CorsicanChannel, where winds and currents have diverse directions andintensities (Millot, 1987; Artale et al., 1994; Astraldi et al., 1994).Two major surface currents flow into the southern part of Ligurianbasin: the West Corsican Current that brings modified Atlanticwater from the Algerian Basin, and the Tyrrhenian Current that,having lapped the coasts of Latium and Tuscany, brings waternorthward through the Corsican Channel from the Tyrrhenian Sea(Millot, 1987; Artale et al., 1994; Astraldi et al., 1994). Therefore, onone hand, there is a persistent south–north current along the westItalian coast that connects the Tyrrhenian and Ligurian basins andmay genetically homogenise populations of intertidal and marineorganisms; on the other hand, within each Mediterranean basin,there are local circulation gyrations that may isolate different partsof the system.

The differences in salinity and temperature between the twoseas could also limit the dispersion of P. marmoratus in its larvalphase. The megalopa of this species, in fact, can actively regulate itstransport by means of behavioural responses to chemical–physicalstimuli (Shanks, 1985; Tankersley et al., 1995) and thanks to itsability to swim (Shanks, 1995).

S. Fratini et al. / Estuarine, Coastal and Shelf Science 79 (2008) 679–686 685

Finally, the central population (CV), washed by the TyrrhenianSea, is located in a creek. This could explain why it is geneticallyseparated from both the Northern and Southern populations. Infact, it may be assumed that most of the larvae (if not all) aretrapped within the creek after the spawning and thus are them-selves the main source of recruitment in this area.

As commonly recorded for marine organisms with planktoniclarval phases (Palumbi, 1996), genetic distances are not correlatedwith geographic distances: this confirms that genetic separationamong our studied populations is not caused simply by restricteddispersal, but that other factors are involved in determining agenetic population structure, as explained above.

5. Conclusion

This study consolidates the theoretical and experimental ideathat genetic diversity may serve as an indicator of environmentalcondition. Indeed, we underline the importance of considering thestressor effect at a genetic level within a biogeographical context inorder to avoid erroneous conclusions (see also Belfiore andAnderson, 2001; Staton et al., 2001; Whitehead et al., 2003; Maeset al., 2005). In fact, biogeographical and historical events coulddetermine how populations respond to anthropogenic environ-mental changes in the present. Genetic modifications due tostressor exposition could be less evident in interconnected pop-ulations than in isolated populations thanks to the counter-balancing action of gene flow.

Finally, loss of genetic variability in natural populations, espe-cially when they consist of few individuals, may dramatically in-fluence the populations themselves. Indeed, this may precipitatetheir disappearance. Thus, a complete environmental quality as-sessment should investigate the effects of human impact not onlyat the ecosystem and individual levels but also at the molecularlevel.

Acknowledgements

Many thanks are due to S. Barbaresi, G. Pitruzzella, C. Papetti andF. Barbisan for laboratory support, and to G. Innocenti and R. Ror-andelli for helping with the collection of the samples. Our warmthanks also go to M. Gori and E. Goti of the C.I.B.I.A.C.I (University ofFlorence) for laboratory support and for their patience. M. Barattiprovided very useful comments throughout all stages of this re-search. Prof. T. Patarnello kindly invited S.F. into his laboratory toperform part of the analyses. Maxine Iversen and Prof. J. Sandersprovided some help with the language.

This work was supported by a COFIN project (no.2002058313_003) and by a MURST ex-60% research project awardto Prof. M. Vannini.

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