Genetic heterogeneity in populations of the Mediterranean shore crab, Carcinus aestuarii (Decapoda, Portunidae), from the Venice Lagoon

Estuarine, Coastal and Shelf Science 87 (2010) 135–144

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Genetic heterogeneity in populations of the Mediterranean shore crab, Carcinus aestuarii (Decapoda, Portunidae), from the Venice Lagoon Ilaria Anna Maria Marino a, Federica Barbisan a, Micol Gennari b, Folco Giomi a,1, Mariano Beltramini a, Paolo Maria Bisol a, Lorenzo Zane a, * a b

Department of Biology, University of Padova, via G. Colombo 3, I-35121 Padova, Italy Department of Pathology, Microbiology Section, University of Verona, strada Le Grazie 8, I-37134 Verona, Italy

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 July 2009 Accepted 5 January 2010 Available online 15 January 2010

Heterogeneity in genetic composition among recruits, mostly due to a large variance in reproductive success mediated by oceanographic processes, has been reported for marine species but is less understood in coastal lagoons’ organisms. Temporal genetic variation in natural populations of the Mediterranean shore crab Carcinus aestuarii was quantified over a multi-year sample. A total of 486 adult crabs were collected at eight different sites of the Venice Lagoon during the period 2005–2007 and screened for genetic variation using 11 microsatellite loci. Two additional samples (N ¼ 115) from neighbouring sites, located approximately 100 km North and South to the Venice Lagoon, were included for the sake of comparison. Our results show significant differences in allelic frequencies at the micro-geographic scale of the Venice Lagoon, observed between sites of collection, typologies of habitat, and areas with different class of ecological risk or pattern of hemocyanin expression. However, this pattern was not constant between years, with significant differences observed mainly in 2005 and 2006, but not in 2007. Our results indicate significant temporal differences suggesting the existence of dynamic processes that act on the genetic pool of this species. Although natural selection and gene flow might play a role, we suggest that genetic drift linked to large variation in the reproductive success of individuals is the most probable scenario to explain the local genetic patterns of differentiation in the Mediterranean shore crab. Our study, by providing the first evidence for the existence of genetic differences in this species at the micro-geographic scale, suggests that a better comprehension of the link between reproduction, recruitment and oceanography is critical to understand how colonization and maintenance of genetic variation is achieved in ephemeral and vulnerable environments such as coastal lagoons. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Carcinus aestuarii crab Venice Lagoon microsatellite genetic patchiness Temporal genetic variation

1. Introduction Although the dispersal potential of larval stages of marine organisms has long been acknowledged, its impact on population dynamics and evolution is still the focus of recent studies (Caley et al., 1996; Kinlan and Gaines, 2003; Palumbi, 2003). For decades, the genetic structure of marine organisms has been thought to be homogeneous due to their extended egg/larval dispersal capabilities and to the lack of obvious barriers to gene flow in the marine

* Corresponding author. E-mail addresses: [email protected] (I.A.M. Marino), federica.barbisan@ unipd.it (F. Barbisan), [email protected] (M. Gennari), [email protected] (F. Giomi), [email protected] (M. Beltramini), [email protected] (P.M. Bisol), [email protected] (L. Zane). 1 Present address: Alfred-Wegener-Institute, Am Handelshafen 12, D-27570 Bremerhaven, Germany. 0272-7714/$ – see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2010.01.003

environment (Vermeij, 1987; Ward et al., 1994; Hauser and Carvalho, 2008). A large number of marine organisms exhibits large population sizes, external fertilization, high fecundity, an extensive pelagic larval phase, and in many cases a benthic adult stage. These characteristics lead to an expectation of low genetic divergence, due to extensive gene flow during the larval stage, whereas the lower mobility at the adult stage may eventually favour local adaptation (Ward et al., 1994). Yet, this view has been challenged now that complex genetic structures have been discovered in the sea, with the most noticeable examples demonstrating population subdivisions in marine fishes on a limited geographic scale ranging from few to few hundred kilometres (Ruzzante et al., 1998; Knutsen et al., 2003; Taylor and Hellberg, 2003; Nielsen et al., 2004; Olsen et al., 2008). For highly dispersive marine invertebrates, several surveys have shown the existence of significant differences at the small spatial scale (Watts et al.,1990; Hedgecock,1994; Edmands et al.,1996; David et al.,

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1997; Moberg and Burton, 2000) often referred as ‘‘chaotic genetic patchiness’’, unpatterned genetic heterogeneity among local populations (Johnson and Black, 1982). For instance, studies on the distribution of genetic variability of the black-lipped pearl oyster (Pinctada margaritifera) from the Central Pacific (Arnaud-Haond et al., 2008) suggested a natural restriction to gene flow at the large scale (more than 1000 km), partly obscured by recent spat translocations (Arnaud-Haond et al., 2004), and a substantial homogeneity at a medium scale (>10–100 km); in contrast, at the intra-lagoon scale, a stochastic dynamic of spat recruitment (Friedman et al., 1998) resulted in ‘‘chaotic’’ genetic differences in both space and time (Arnaud-Haond et al., 2008). This trend seems to be rather common. Planktonic dispersal itself, although causing uniformity on the medium or large scale, can determine locally a stochastic variation in the numbers and genotypes of recruits, thus producing fine-scale genetic patchiness (Johnson and Black, 1982). On the other hand, consistent genetic differences at microgeographic scale attributable to natural selection has been observed using allozymes in marine invertebrates (Koehn et al., 1980; Hilbish and Koehn, 1985). In these cases, the occurrence of differential selection in space and time favouring genetic differentiation of recruited cohorts has been found to produce clines in genetic polymorphisms or correlations with varying environmental factors (see Crawford and Powers, 1989; Oleksiak et al., 2002 for fish). In addition to direct identification of selective effects that requires a strong genetic linkage between the markers used and some of the fitness component (Arnaud-Haond et al., 2008), indirect effects of selection on multilocus genetic diversity has been reported using microsatellites (Domı´nguez-Domı´nguez et al., 2005; Maes et al., 2005; Fratini et al., 2008). In fact, under the ‘‘genetic erosion’’ hypothesis, toxicant exposure may alter the genetic composition of a population favouring more tolerant genotypes, it may alter migration patterns and/or cause genetic bottlenecks (Van Straalen and Timmermans, 2002) thus resulting in a decrease in multilocus genetic variability. The existence of genetic differentiation or natural selection effects at the small scale, independent of the possible large scale homogeneity of population and gene flow among distant sites, underlines the importance of sampling scale for population and biogeographic studies (David et al., 1997; Benzie, 2000; ArnaudHaond et al., 2008). In this study, we focus on the pattern of population differentiation in a high dispersal species, the Mediterranean shore crab (Carcinus aestuarii, Decapoda: Portunidae) at the micro-geographic scale. Carcinus aestuarii is a euryhaline and eurytherm species occupying estuarine and lagoon waters of the Mediterranean Sea and it is closely related to the Atlantic European green crab Carcinus maenas. Due to substantial overlapping of morphological and ecological characters, the taxonomic status of the two species has been disputed (Cohen et al., 1995; Bulnheim and Bahns, 1996; Behrens Yamada, 2001). Recently, genetic studies confirmed the existence of fixed differences between the Atlantic and Mediterranean forms (Geller et al., 1997; Roman and Palumbi, 2004) suggesting their specific status, but the possibility that the two forms hybridize in the contact zone of the Gibraltar strait is still open (Behrens Yamada and Hauck, 2001). Both C. aestuarii and C. maenas are highly invasive species that can adversely affect marine communities by altering food webs, disturbing habitats, displacing native species, and preying on commercially important clams, mussels, oysters, and juvenile native crabs (Carlton and Cohen, 2003). It has been suggested that the same life-history characteristics that allow for successful invasions – wide environmental tolerance, high fecundity and long larval stages – should lead to weakly or seemingly unstructured populations of the species (Waples, 1998).

Evidence for weak differentiation has been reported in Carcinus maenas using the cytochrome c oxidase I gene (Roman and Palumbi, 2004; Darling et al., 2008). In particular, after removing samples from offshore locations such as the Faeroe Islands and Iceland in which highly differentiated haplotypes were found, genetic structure was very slight along the European shore. In fact, a small but significant break explaining 1.2–2.9% of the genetic variation, was found between Western and Northern Europe biogeographic regions, whereas differentiation within the two regions was negligible on a scale of several hundreds kilometres. Considerably weaker structure was detected using microsatellites (Darling et al., 2008) with estimates of genetic differences associated with onshore/offshore and European shore locations and biogeographic regions ranging from 1.6% to 1.0% respectively. Here, we aim at extending these large scale results by investigating the pattern of genetic differentiation between multi-year samples of Carcinus aestuarii collected at different sites of the Venice Lagoon (Northern Adriatic Sea, Italy), separated by a maximum of about fifty kilometres. The Lagoon of Venice (45.2 – 45.6 N, 12.2 –12.6 E), the largest Italian Lagoon and one of the largest in Europe, is sub-divided in three sub-basins separated by two watersheds along which the intensity of the tidal currents is low. Recent studies show that the lagoon is a complex system, which presents both stable ecological differences between sites than variability, in time and space, of several parameters including temperature, salinity, dissolved nutrients, chlorophyll a, dissolved oxygen, and turbidity (MAV-CVN, 2002a,b). In addition, the impact of the anthropogenic activities affects the various sub-areas to a different extent and adverse effects, such as anoxic crises and pollutant accumulation in the sediment, generally occur in the more confined sub-areas (Wenning et al., 2000; Critto and Marcomini, 2001; Bellucci et al., 2002). In this context, by collecting Carcinus aestuarii genotypes of 11 microsatellite loci, we intend to: (1) Identify the existence of genetic differences among sites to reveal the existence of locally adapted populations. (2) Identify differences between years of collection and partition the amount of genetic differentiation into an inter- and intra-annual component to address the existence of temporal variation in the local genetic pool of the species. (3) Compare samples from areas characterized by different habitat typology (Guerzoni and Tagliapietra, 2006; Tagliapietra et al., 2009b), reflecting the confinement (sensu Gue´lorget and Perthuisot (1983), but see Tagliapietra et al., 2009a) and by different level of pollution (Critto et al., 2005). (4) Compare population samples characterized by different patterns of hemocyanin subunit expression, a phenotypic parameter that depends on chemical and physical factors such as salinity, dissolved oxygen and temperature (Mangum and Rainer, 1988; DeFur et al., 1990; Mangum, 1990) and might then reflect differential exposure to these stressors. 2. Materials and methods 2.1. Sampling A total of 486 specimens of the Mediterranean shore crab Carcinus aestuarii were collected over three years, between 2005 and 2007, at several sites in the Venice Lagoon (Fig. 1, Table 1). For the sake of comparison, we included in the analysis two population samples (N ¼ 115) from Marano and Goro, located about 100 km North and South of the Venice Lagoon (Fig. 1, Table 1). After collection, the crabs were brought to the laboratory and frozen. One cheliped or both were taken and frozen at 80  C for DNA extraction. The sampling sites in the Venice Lagoon were characterized by different habitat typologies following the classification specifically

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Fig. 1. Sampling location of Carcinus aestuarii, labelled as in Table 1.

developed in the framework of CORILA (Consortium for Coordination of Research Activities Concerning the Venice Lagoon System), which identifies several classes with a different degree of confinement determined using morphological, hydrodynamic and chemical–physical characteristics (research responsible Dr. D. Tagliapietra, CNR ISMAR, Venice, details available on request from www.corila.it). Specifically our sites resulted to be classified as ‘‘Marginal Fringe Zone’’, ‘‘Remote Fringe Zone’’, ‘‘Inner Fringe Zone’’, ‘‘Marine Tidal Delta’’ (Tagliapietra et al., 2009b, Table 1). In addition, sampling sites were classified by environmental risk classes (ERC) reported in Critto et al. (2005). ERC is represented by

the number of contaminants (from 1 to 7 in this case) above the benchmark TEL (Threshold Effect Level); at concentrations below TEL contaminants are expected to have no toxic effects on aquatic life. Finally, a subset of population samples was classified on the basis of the patterns of hemocyanin subunit expression. To this end, hemolymph proteins, of which the oxygen carrier protein represents more than 90% (Ellerton et al., 1983), were separated by polyacrylamide gel electrophoresis in 50 mM Glycine/NaOH, 1 mM EDTA pH 9.6 at alkaline pH and in the presence of EDTA to resolve the subunits by inducing dissociation of the native oligomeric

Table 1 Carcinus aestuarii samples analyzed in this study. Samples are arranged from North to South. Location, acronym (in italics for Venice Lagoon), basin, geographic coordinates, sampling date, number of individuals assayed at microsatellite loci (N), habitat typology (see text), class of ecological risk (ERC) and pattern of hemocyanin expression are reported for each sample. Station

A) Marano B) Venice 1) Ca’ Zane 2) Saline 3) Paludi della Centrega 4) Campalto 5) San Clemente 6) Fusina

7) Ca’ Roman lagoon side

8) Ca’ Roman sea side C) Goro

Acronym

Basin

Geographic coordinatesa East

North

Sampling date

N

Habitat typology

ERCb

Pattern of hemocyanin expression

MA06

–

2377021

5065219

Apr 2006

55

–

–

–

CZ06 SAL06 PC06 CA07 SC07 FU05 FU06 FU07 CI05 CI06 CI07 CE05 CE06 GO06

North North North Central Central Central Central Central South South South – – –

2323188 2322481 2320471 2309954 2312642 2307034 2307034 2307034 2307659 2307659 2307659 2309394 2309394 2306952

5044292 5041132 5042153 5037751 5032711 5032721 5032721 5032721 5013224 5013224 5013224 5013568 5013568 4967093

Apr 2006 Apr 2006 Apr 2006 Jun 2007 Jun 2007 Apr 2005 May 2006 Jun 2007 Apr 2005 May 2006 Jun 2007 Apr 2005 May 2006 Apr 2006

47 40 20 25 25 50 47 25 32 44 25 37 69 60

remote inner inner marginal marine marginal marginal marginal marine marine marine – – –

3–4 1–2 1–2 3–4 3–4 6–7 6–7 6–7 3–4 3–4 3–4 – – –

– – – lowc low high high low high low low low low –

– Not available. a Geographic coordinates are expressed in Gauss Boaga Roma 40. b Class of ecological risks as reported by Critto et al. (2005). c Significant differences in the pattern of expression of hemocyanin subunit 5 were identified in 2005 and 2006 (see text for details).

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protein (Markl et al., 1979; Dainese et al., 1998; Giomi et al., 2007). After Comassie staining, a total of 6 bands were reliably identified, putatively corresponding to the hemocyanin subunits, and numbered I–VI as a function of increasing electrophoretic mobility. In a pilot study, the correspondence between electrophoretic bands and hemocyanin subunits was confirmed by immunoblotting with hemocyanin antibodies. Gels, stained with Comassie, were scanned at high resolution (3200 dpi) with an Epson Perfection 1660 scanner and quantification of the densitometric signals was carried out using MATHEMATICA 5.0 software. The relative abundance of each fraction was expressed as percent of a given densitometric area of the Gaussian-fitted intensity to that of the total area signal. Percentages were arcsine-root transformed and, for each year, populations with a significantly different level of expression of a given band were identified by Mann–Whitney test and grouped in different classes of hemocyanin expression. 2.2. Microsatellite analysis. DNA extraction and genotyping Total genomic DNA was extracted using a salting-out protocol (Patwary et al., 1994) in a dedicated laboratory. Eight polymorphic microsatellite loci specific for Carcinus aestuarii, Cae01, Cae07, Cae14, Cae17, Cae30, Cae33, Cae71, Cae86 (Marino et al., 2008) and 3 additional microsatellite loci specific for the sibling species Carcinus maenas, Cma02EPA, Cma04EPA, Cma14EPA (Tepolt et al., 2006), were used for genetic analysis. Polymerase chain reaction was performed, under a laminar hood and using a dedicated room and reagents, with the following conditions: total volume 20 ml, Taq buffer 1 (Promega, 50 mM KCl, 10 mM Tris–HCl pH ¼ 9 at 25  C, 0.1% TritonX-100), MgCl2 1 mM, 150 nM of each primer, 70 mM dNTPs, 0.08 U of Taq polymerase (Promega) and 50 ng of genomic DNA, as described in detail in Marino et al. (2008). Appropriate negative and positive controls were included. The forward primer for each pair was labelled with a fluorescent dye (HEX, 6-FAM, TAMRA). Loci were amplified using the following PCR profile: (1) predenaturation at 94  C for 2 min; (2) 30–35 cycles: denaturation at 94  C for 30 s, annealing at the primer-specific annealing temperature for 40 s, extension at 72  C for 1 min; (3) additional extension for 5 min at 72  C. Post PCR steps were performed in a separate laboratory. The polymerase chain reaction product was loaded on an ABI Prism 3100 or 3700 automated sequencers performed at the BMR Genomics Molecular Biology service (www. bmr-genomics.it). Sizing was obtained by comparison with the internal standard GS 400 Hd Rox (Applied Biosystems). Fragment analysis was performed on 96-well plates (batches of 96 individuals); to ensure reproducibility of results and minimize differences due to capillary electrophoresis, each plate was routinely composed by 85–90% of new individuals and 10–15% of replicates. Scoring was performed using the program GENOTYPER 3.7 (Applied Biosystems). In order to minimize the negative consequences of a poor allele calling, binning was automated with the software FLEXIBIN version 2 (Amos et al., 2007). The scoring was manually checked by the authors and loci were analyzed for null alleles presence with MICROCHECKER version 2.2.3 (Van Oosterhout et al., 2004). 2.3. Genetic diversity, Hardy–Weinberg equilibrium, linkage disequilibrium, population structure Number of alleles, observed (Hobs) and expected (Hexp) heterozygosity per locus, departure from Hardy–Weinberg (HW) and linkage equilibrium, and exact differentiation tests between population samples were calculated using GENEPOP version 3.4 (Raymond and Rousset, 1995). Exact test of differentiation, which verifies the existence of significant differences in allelic frequencies among samples at each locus using a permutation approach and

combine the results by Fisher’s exact test, were performed on all the population samples and by pooling them by site of collection, sub-basins, year, habitat typology, ecological risk class and different patterns of hemocyanin subunit expression. The effect of sample size on the power of detecting differentiation between population samples was investigated using a rarefaction approach; this effect is potentially important because some samples in our dataset, noticeably all those from the 2007 collection, are composed by only 20–25 individuals (Table 1) and this might prevent the identification of genetic differences. We normalized all the population samples to this minimum size by randomly extracting, without replacement, the genotypes of 20 or 25 individuals from each population using POPTOOLS (Hood, 2009). The procedure was repeated 100 times and each time exact test of differentiation was performed and the significance recorded. FSTAT version 2.9.3.2 (Goudet, 1995) was used to compute the allelic richness (AR), based on the smallest sample size across all the populations. Population pairwise Fst were determined with GENETIX (Belkir et al., 2005), using 1000 permutations for all comparisons. Significance for tests involving multiple comparisons was obtained using the q-value test (Storey et al., 2004).

3. Results Analysis of 601 individuals with 11 microsatellite loci revealed relatively high molecular variation (Table 2). All microsatellite loci were polymorphic, with allele number ranging from 7 to 69, whereas observed heterozygosity and expected heterozygosity ranged from 0.11 to 0.91 and 0.12 to 0.96, respectively. A general excess of homozygotes was found at all loci. Tests for Hardy–Weinberg equilibrium, calculated for each locus/population and combined by Fisher’s test for a global value, revealed significant deviations for seven out of eleven loci (Cae01, Cae07, Cae14, Cae33, Cae86, Cma02EPA and Cma14EPA loci, Table 2). For these loci, the software MICROCHECKER (Van Oosterhout et al., 2004) suggested the presence of null alleles in most of the population samples. However, after correction for null alleles, all tests for HWE remained significant (Supplementary Appendix 1), suggesting that deviation was due to biological factors rather than to PCR artefacts. According to results from HWE and MICROCHECKER, all the tests of differentiation were performed considering both uncorrected data and data corrected for the presence of null alleles; this correction does not affect the final results and, for this reason, we report results only for uncorrected data. Table 3 reports the level of variability for different samples. Observed heterozygosity ranged from 0.68 in the sample collected at Paludi della Centrega in 2006 to 0.76 for the sample from Ca’

Table 2 Carcinus aestuarii microsatellites. For each locus we report the overall number of alleles (N), the average observed (Hobs) and expected heterozygosity (Hexp), and the uncorrected probability of Hardy–Weinberg equilibrium (P) obtained by Fisher exact test. Locus

N

Hobs

Hexp

Cae01 Cae07 Cae14 Cae17 Cae30 Cae33 Cae71 Cae86 Cma02EPA Cma04EPA Cma14EPA

24 69 19 18 7 54 40 25 41 17 17

0.826 0.887 0.538 0.852 0.113 0.754 0.914 0.874 0.747 0.778 0.743

0.845 0.958 0.632 0.861 0.118 0.954 0.940 0.927 0.912 0.792 0.809

P 0.001 <0.001 <0.001 0.308 0.967 <0.001 0.125 <0.001 <0.001 0.138 <0.001

I.A.M. Marino et al. / Estuarine, Coastal and Shelf Science 87 (2010) 135–144 Table 3 Carcinus aestuarii heterozygosity. Reported are: population acronyms (as in Table 1), the average allelic richness among samples (AR), multilocus observed (Hobs) and expected heterozygosity (Hexp), and the uncorrected probability of Hardy–Weinberg equilibrium (P) obtained by combining single locus estimates with the Fisher exact test. Samples

AR

Hobs

Hexp

P

MA06 CZ06 SAL06 PC06 CA07 SC07 FU05 FU06 FU07 CI05 CI06 CI07 CE05 CE06

9.402 9.495 10.036 8.946 10.320 10.347 9.574 9.810 9.643 9.449 9.922 9.337 10.072 9.776

0.714 0.691 0.727 0.684 0.752 0.754 0.722 0.727 0.746 0.718 0.756 0.755 0.746 0.747

0.804 0.787 0.800 0.795 0.785 0.818 0.788 0.795 0.778 0.796 0.780 0.795 0.817 0.775

<0.001 <0.001 <0.001 <0.001 0.035 <0.001 <0.001 <0.001 <0.001 <0.001 0.306 <0.001 <0.001 0.154

GO06

9.657

0.708

0.786

<0.001

Roman lagoon side 2006. Similarly average allelic richness for sample was low in Paludi della Centrega 2006 (8.95) but reached the highest value in San Clemente 2007 (10.35). No significant differences between sites were found for the two statistics (One Way ANOVA, P ¼ 0.967 and 0.998 respectively). Overall, tests for linkage disequilibrium among all loci showed no significant departures from expected values. Pattern of population differentiation was investigated using exact test of population differentiation for several hypotheses of sample subdivision (Table 4). First, we tested overall differentiation between individuals from Marano and Goro respect to the ones collected in the Venice Lagoon obtaining a highly significant P-value (P-value < 0.001, test A of Table 4). Pairwise analysis (Table 5) showed that this result is due mainly to comparisons involving the Marano and Venice Lagoon samples, which were significant in 9 out of the 13 tests performed; a weaker differentiation was observed for the Goro sample for which differences were obtained only in 2 test (PC06 and CI05). Second, we tested differentiation among all the 13 population samples of the Venice Lagoon. We obtained a highly significant P-value (P < 0.001, test B of Table 4), supported by 4 out of eleven loci, that allowed to reject the hypothesis of panmixia; this result was due to 22 out of 78 pairwise comparisons significant after qvalue test, most of which involved the PC06, CI05 or the FU05 samples (Table 5). Third, we tested differentiation for population samples collected in different sites of the Venice Lagoon, obtaining highly significant values (Tests C of Table 4). This pattern of differentiation was not constant between years because we obtained highly significant values between sites in 2005 and 2006 (P < 0.001 and P ¼ 0.004, respectively) but no differentiation in 2007 (P ¼ 0.412). This result does not depend on the smaller size of the 2007 samples. In fact, by rarefacting the 2005 populations samples to 25 and the 2006 to 20 individuals (Table 1), we still detected significant differences in 92% and 77% of the replicates. Similarly, differences between sub-basins (Northern basin: Ca’ Zane, Paludi della Centrega and Saline; Central basin: Fusina, San Clemente and Campalto; Southern basin: Ca’ Roman lagoon side) were significant only in 2005 (P ¼ 0.001, Test D of Table 4). Accordingly, we obtained highly significant values for differentiation between years (Test E of Table 4) indicating the presence of temporal variation. In particular, pairwise analyses revealed highly significant differences between samples collected in 2007 and those collected in 2005 (P < 0.001) and a weaker differentiation for

139

2005/2006 and 2006/2007 comparisons (P ¼ 0.037 and P ¼ 0.012, respectively; all significant after q-value test). Significant differences were also obtained when we tested differentiation among samples from areas characterized by different habitat typology, level of ecological risk (ERC) and patterns of hemocyanin subunit expression (Tests F–H of Table 4). Independence of these parameters and their dependence on spatial locations was not rejected by a Fisher exact test that provided P values ranging from 0.141 to 0.850. To allow the comparison based on hemocyanin, the hemolymph of organisms sampled in the various sites was further analyzed electrophoretically. For a given individual, a variable intensity of bands in the hemocyanin pattern, reflecting the different level of expression of each component, can be identified (Fig. 2A, B). By using the percent of each band as parameter to compare the individuals from different sampling sites, we identified significant differences in expression of band 5 that allowed to perform a further classification of our samples, reported in Table 1. In particular, in 2005, band 5 was significantly less expressed in CE05 sample than in CI05 and FU05 (Fig. 2C) (P < 0.001 and 0.05 respectively) and, in 2006, both CE06 and CI06 samples showed a significantly lower expression for this band than FU06 (P < 0.001 in both cases, Mann–Whitney test). In all the cases (habitat typology, ERC, class of hemocyanin expression; Tests F–H of Table 4), differences in allele frequencies were found among the 2005 samples together with, for habitat typology, a weak differentiation among 2006 samples. Fst statistics of differentiation were estimated following the same hypotheses verified for exact test, but no significant values were obtained with fixation indices ranging from negative values to 0.001 (P-value range 0.526–0.093). Compared to global Fst, pairwise comparisons showed a wider range of variation, reaching a maximum of 0.015 (Table 6); importantly values of Fst were higher within the Venice Lagoon than between adjacent lagoons. None of these pairwise Fst was significant after q-value test, though thirteen comparisons were significant for the uncorrected 0.05 threshold. 4. Discussion This study shows significant genetic heterogeneity between samples of Carcinus aestuarii collected at the micro-geographic scale of the Venice Lagoon. Genetic structure was detected using exact test of population differentiation but not with Fst statistics, which have been shown to be a less powerful approach when differences are small (Ryman et al., 2006; Waples and Gaggiotti, 2006). According to our results, we detected differences in allele frequencies between sites of collection. However, this pattern was not constant between years with significant differences mostly observed in 2005 and 2006. Thus, our results indicated significant temporal differences suggesting the existence of dynamic processes that act on the genetic pool of this species and that, from the genetic point of view, C. aestuarii does not form a homogeneous and completely mixing population, but is rather structured in a mosaic of slightly differentiated, dynamic, genetic patches. Genetic heterogeneity on a micro-geographic scale is generally accepted to result from temporal variation in the genetic composition of recruits (Johnson and Black, 1982; David et al., 1997; Planes and Lenfant, 2002) and the hypotheses invoked to explain such sub-structure include selection on larval populations, immigration from differentiated populations and stochastic processes linked to reproduction and recruitment (Arnaud-Haond et al., 2008). In the case of natural selection, different selective histories of the larval pools might explain differences in the genetic composition of recruits (David et al., 1997). The occurrence of differential selection in space and time favouring genetic differentiation of

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Table 4 Exact test of population differentiation. Reported are: hypothesis tested, population samples subdivided in different groups, P-value of the probability test and number of loci showing significant differences for each differentiation test. Significant values after q-value test are indicated in bold. N loci

Hypothesis tested

Dataset

P

A.

Differences between lagoons

15 population samples in 3 lagoons

<0.001

2

B.

Differences between samples from the Venice Lagoon

13 population samples

<0.001

4

C1. C2. C3.

Differences between sites between sites 2005 between sites 2006 between sites 2007

8 3 6 4

0.005 <0.001 0.004 0.412

2 2 2 1

D1 D2. D3.

Differences between basins between basins 2005 between basins 2006 between basins 2007

11 population samples in 3 basins (Northern, Central, Southern) 2 population samples in 2 basins (Central, Southern) 5 population samples in 3 basins (Northern, Central, Southern) 4 population samples in 2 basins (Central, Southern)

<0.001 0.001 0.303 0.749

3 2 1 0

E.

Differences between years

13 population samples in 3 years (2005, 2006, 2007)

<0.001

3

F. F1. F2. F3.

Differences between habitat typologya between habitat typology 2005 between habitat typology 2006 between habitat typology 2007

11 population samples in 4 classes (marginal, remote, inner, marine) 2 population samples in 2 classes (marginal, marine) 5 population samples in 4 classes (marginal, remote, inner, marine) 4 population samples in 2 classes (marginal, marine)

<0.001 0.001 0.033 0.363

4 2 2 1

G1. G2. G3.

Differences between ERCa between ERC 2005 between ERC 2006 between ERC 2007

11 population samples in 2 groups (high ERC, low ERC) 2 population samples in 2 groups (high ERC, low ERC) 5 population samples in 2 groups (high ERC, low ERC) 4 population samples in 2 groups (high ERC, low ERC)

0.024 0.001 0.315 0.448

3 2 0 0

Differences between pattern of hemocyanin expressionb

10 population samples in 2 groups (high hemocyanin expression pattern, low hemocyanin expression pattern) 3 population samples in 2 groups (high hemocyanin expression pattern, low hemocyanin expression pattern) 3 population samples in 2 groups (high hemocyanin expression pattern, low hemocyanin expression pattern)

0.001

4

0.039

1

0.259

1

C.

D.

G.

H. H1. H2.

between pattern of hemocyanin expression 2005 between pattern of hemocyanin expression 2006

population population population population

samples samples samples samples

in in in in

3 3 6 4

sites sites sites sites

(CI, CE, FU) (CI, CE, FU) (CZ, CI, CE, FU, PC, SAL) (CA, CI, FU, SC)

a For these two tests, population samples of Ca’ Roman sea side were not included in the analysis because no information about habitat typology and level of ecological risk was available. b No differences were found in 2007.

recruited cohorts is expected to produce clines in genetic polymorphisms correlated with varying environmental factors such as temperature or salinity, indeed observed in several cases (Koehn et al., 1980; Larson and Julian, 1999). Following this hypothesis, the larval pool of Carcinus aestuarii could be completely mixed and genetically homogeneous in the sea in front of the Venice Lagoon, and then spatially or temporally diverse selective forces acting during the recruitment inside the lagoon, might produce genetic differences among local samples. In this study, we did not measured directly the environmental factors potentially implicated in selection. However, the analysis of Carcinus aestuarii samples showed significant genetic differentiation with respect to habitat typology (largely reflecting salinity,

Gue´lorget and Perthuisot, 1983), ecological risk class (indicative of a different impact of pollution, Critto et al., 2005), and patterns of hemocyanin expression. With this regard, it is important to notice that due to our limited sampling we were not able to reject the hypothesis of non independence between these parameters, but the possibility that they are correlated cannot be ruled out. This is especially true when considering that habitat classification reflects the degree of confinement and this latter is potentially important in determining the level of pollution and changes in hemocyanin expression profiles. From this perspective, the possibility that genetic differentiation inside the Venice Lagoon is also correlated with one, or with a combination of these factors, is extremely interesting and should be addressed in the future with a specific

Table 5 Exact test of population differentiation for all 15 population samples (for acronyms, see Table 1). Significant values after the permutation (10,000 permutations) and q-value tests are indicated in bold. Venice Lagoon

CZ06 SAL06 PC06 CA07 SC07 FU05 FU06 FU07 CI05 CI06 CI07 CE05 CE06 GO06

MA06

CZ06

SAL06

PC06

CA07

SC07

FU05

FU06

FU07

CI05

CI06

CI07

CE05

CE06

0.001 <0.001 0.002 0.003 0.024 <0.001 0.124 0.001 <0.001 0.008 0.099 0.070 <0.001 0.007

0.211 0.006 0.009 0.068 0.034 0.194 0.217 0.129 0.066 0.203 0.138 0.006 0.029

0.083 0.260 0.149 0.093 0.264 0.591 0.011 0.546 0.389 0.030 0.197 0.100

<0.001 0.006 <0.001 0.004 0.004 0.009 0.194 0.003 0.005 0.010 <0.001

0.489 0.052 0.069 0.449 0.093 0.917 0.553 0.002 0.479 0.349

0.046 0.451 0.295 0.124 0.687 0.377 0.047 0.624 0.401

0.337 0.005 0.001 0.107 0.061 0.004 0.008 0.045

0.378 0.001 0.542 0.311 0.042 0.204 0.419

0.006 0.559 0.438 0.161 0.324 0.698

0.149 0.017 0.002 0.094 <0.001

0.973 0.537 0.473 0.810

0.105 0.822 0.764

0.097 0.063

0.150

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141

Fig. 2. Analyses of hemocyanin subunit expression. Panels A and B: Examples of hemocyanin electrophoretic pattern on one individual hemolymph sample and its densitometric profile; numbers identify the different bands in the PAGE. Panel C: Relative amount of hemocyanin band 5 among sampling sites collected in 2005, expressed in percentage of the total hemocyanin.

sampling plan. With the present data, we found significant differences that were not consistent between years, with differences showing up mainly in 2005 and 2006 and not significant in 2007 (Table 4). This result should not be overlooked taking into account that environmental conditions may change from one year to the other and there might be a lag between such change and the next event of recruitment influenced by the new state. An indirect indication of the dynamic behavior of environmental parameters in the Venice Lagoon is provided itself by hemocyanin data. The molecular heterogeneity of hemocyanin subunits is based on the existence of different genes, encoding single polypeptide chains, that constitute the genetic basis for the inter- and intraspecific polymorphism. The possibility to regulate the expression of subunits provides an efficient intrinsic mechanism of modulation of the functional activity of the protein, the oxygen transport (Giomi and Beltramini, 2007). A significant difference in expression of hemocyanin band 5 was found in this study for C. aestuarii samples collected in 2005 and 2006, with a lower expression mainly found in the areas closest to the sea (Table 1) but not in 2007. This result is in agreement with the observation that salinity is a major environmental factor capable to influence the hemocyanin subunits pattern of expression in crabs, as it was found in related species such as Callinectes sapidus (Mason et al., 1983). On the other hand, the fact that no significant differences were

observed in 2007 could reflect a temporal change in this, or other relevant, environmental parameters. Further analyses are needed to fully disentangle the effect of environment on genetic differentiation of the Mediterranean shore crab. We underline, however, that selection requires a strong genetic linkage between the markers used and some of the fitness component (Arnaud-Haond et al., 2008) and therefore acts in a locus specific way, whereas we found that more loci consistently support the significant differences observed in C. aestuarii (Table 4). Immigration could be another explanation for temporal variation in allele frequencies. A variable regime of gene flow of larvae from different spawning populations with different allelic composition might generate temporal genetic heterogeneity (Kordos and Burton, 1993; Ruzzante et al., 1996; Larson and Julian, 1999) and the stochastic nature of dispersal in nearshore environments could enhance genetic patchiness (Siegel et al., 2003). Genetic patchiness in this case is expected only if a divergent, un-sampled, source population exists, fact that contrasts with the finding that this unpatterned variation has been found in many noticeable cases in species with very weak differentiation on the large scale (i.e. Planes and Lenfant, 2002; Hedgecock et al., 2007). However, this possibility is not very likely considering that no differentiation has been observed between samples separated by several hundred kilometres in its sibling Carcinus maenas using both cytochrome c

Table 6 Details of pairwise Fst estimates of the genetic differentiation (for acronyms, see Table 1). Significant values for the uncorrected 0.05 threshold are shown in italic. Venice Lagoon

CZ06 SAL06 PC06 CA07 SC07 FU05 FU06 FU07 CI05 CI06 CI07 CE05 CE06 GO06

MA06

CZ06

SAL06

PC06

CA07

SC07

FU05

FU06

FU07

CI05

CI06

CI07

CE05

CE06

0.002 0.001 0.004 0.005 0.000 0.005 0.001 0.004 0.006 0.001 0.001 0.000 0.005 0.002

0.001 0.009 0.004 0.000 0.003 0.000 0.000 0.001 0.000 0.002 0.000 0.006 0.001

0.004 0.002 0.002 0.001 0.003 0.002 0.001 0.004 0.003 0.002 0.000 0.002

0.015 0.008 0.008 0.008 0.014 0.006 0.003 0.010 0.004 0.009 0.009

0.002 0.000 0.001 0.002 0.000 0.004 0.002 0.002 0.002 0.000

0.001 0.003 0.001 0.002 0.002 0.003 0.001 0.001 0.001

0.001 0.004 0.002 0.001 0.001 0.001 0.002 0.001

0.001 0.001 0.002 0.002 0.001 0.001 0.002

0.003 0.001 0.000 0.001 0.003 0.001

0.002 0.002 0.000 0.001 0.001

0.004 0.003 0.001 0.004

0.000 0.000 0.002

0.001 0.001

0.001

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oxidase I gene (Roman and Palumbi, 2004) and microsatellites (Darling et al., 2008). In our study, exact test detected significant differences between samples collected in the Venice Lagoon and those from Marano, separated by approximately 100 km (Table 5). Importantly, these differences do not allow to explain the pattern of genetic variation within the Venice Lagoon with migration events. First, higher (though insignificant) Fst values have been recorded among Venice Lagoon samples than between Marano and Venice. Second, immigration should result in a higher genetic homogeneity between source and sink populations. However, our results showed that the most differentiated samples in the Venice Lagoon (CI05, FU05, PC06) were significantly different from Marano (Table 5), ruling out the possibility that they are affected by strong immigration from this source. Similarly, if the samples collected in 2005 represent a stable ‘‘native’’ population of the Venice Lagoon subsequently replaced by immigrants, we would expect to record a higher homogeneity between samples collected from the Venice Lagoon in 2006 and 2007 and the one from Marano, which was not the case (Tables 5 and 6). As mentioned before, fine-scale genetic patchiness may result from temporal variation of numbers and genotypes of recruits delivered locally (Johnson and Black, 1982). In this case the genetic pool of recruits can change from time to time due to random effects (‘‘instantaneous drift effect’’ David et al., 1997) instead than because of differentiated populations. Thus, temporal variance in allelic frequencies may result from the consideration of stochasticity in reproductive activity and oceanographic conditions conducive to fertilization, larval development, retention and recruitment (Hedgecock, 1994). Under such hypothesis, also called the ‘‘hypothesis of sweepstakes reproductive success’’, many individuals fail to contribute to recruitment, which in turn reduces the effective population size. This variance may then result in changes in allelic frequencies when differences in allelic composition among spawning groups are present. Hedgecock (1994) attributed the variation in reproductive success of adults to spatio-temporal variation in oceanographic conditions, occurring within and among seasons. The random variation in parental contribution to the next generation leads to the variation in genetic composition of recruits observed in genetic patchiness (Pujolar et al., 2006). Considering the lack of consistent correlation with classification based on environmental factors, the support to differentiation provided by multiple loci, the likely absence of differentiated source populations and the temporal variability found in our study, we propose that genetic drift linked to large variation in the reproductive success of individuals is the most probable scenario to explain the local genetic patterns of differentiation in the Mediterranean shore crab. This hypothesis can also explain the general deficit of heterozygotes found in the present study that might be due to a Wahlund effect resulting from the mixing of different pools of recruits in the sampled populations (Wahlund, 1928; Hoarau et al., 2002; but see Nielsen et al., 2003). Moreover, the global fixation indices obtained in this study were extremely small and insignificant (range 0.00005 to 0.00098) and pairwise comparison reached, in few cases, values of above 0.01 and were significant at the 0.05 threshold (Table 6). Waples and Gaggiotti (2006), extending the classical argument of Wright (1978) about the number of migrants sufficient to prevent population differentiation, suggested that an exchange of less than 5 or 25 effective migrants per generation is a convenient threshold to define a population. Considering that, assuming an infinite island model (Wright, 1978), these migrants correspond to Fst values of 0.05 and 0.01 respectively, the Fst observed in Carcinus aestuarii can hardly be interpreted as indicative of separated populations at equilibrium at least under a simplified model of migration.

5. Conclusion Random genetic drift might be an important microevolutionary force affecting the genetic patterns of Carcinus aestuarii and, within the Venice Lagoon, this species might be structured by chaotic genetic patchiness (Avise, 2000). Similar patterns have already been found in several organisms including cod (Ruzzante et al., 1996), sea urchins (Edmands et al., 1996; Moberg and Burton, 2000) and oysters (Hedgecock, 1994; Li and Hedgecock, 1998) and appear to be quite general for marine species with a high dispersal capacity. Our study, by providing the first evidence for the existence of genetic patchiness in the Mediterranean shore crab at the micro-geographic scale, extends these findings to organisms inhabiting coastal lagoons and suggests that a better comprehension of the link between reproduction, recruitment and oceanography is critical to understand how colonization and maintenance of genetic variation is achieved in these ephemeral and vulnerable environments. Acknowledgements This work was supported by a CORILA (Consortium for Coordination of Research Activities Concerning the Venice Lagoon System) grant, ‘‘Scientific Research and Safeguarding of Venice, Research programme 2004–2006, Line 3.11’’. The authors would like to thank Dr. Davide Tagliapietra and Dr. Marco Sigovini of the CNR-ISMAR of Venice for providing habitat classifications for our stations and for their kind help, and Dr. Stefano Cannicci (University of Firenze) for his valuable comments on the manuscript. Appendix 1. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ecss.2010.01.003. References Amos, W., Hoffman, J.I., Frodsham, A., Zhang, L., Best, S., Hill, A.V.S., 2007. Automated binning of microsatellite alleles: problems and solutions. Molecular Ecology Notes 7, 10–14. Arnaud-Haond, S., Vonau, V., Bonhomme, F., Boudry, P., Blanc, F., Prou, J., Seaman, T., Goyard, E., 2004. Spatio-temporal variation in the genetic composition of wild populations of pearl oyster (Pinctada margaritifera cumingii) in French Polynesia following 10 years of juvenile translocation. Molecular Ecology 13, 2001–2007. Arnaud-Haond, S., Vonau, V., Rouxel, C., Bonhomme, F., Prou, J., Goyard, E., Boudry, P., 2008. Genetic structure at different spatial scales in the pearl oyster (Pinctada margaritifera cumingii) in French Polynesian lagoons: beware of sampling strategy and genetic patchiness. Marine Biology 155, 147–157. Avise, J.C., 2000. Phylogeography: the History and Formation of Species. Harvard University Press, Cambridge, MA, 447 pp. Behrens Yamada, S., 2001. Global Invader: the European Green Crab. Oregon Sea Grant, Corvallis, 123 pp. Behrens Yamada, S., Hauck, L., 2001. Field identification of the European green crab species: Carcinus maenas and Carcinus aestuarii. Journal of Shellfish Research 20, 905–912. Belkir, K., Borsa, P., Goudet, J., Bonhomme, F., 2005. GENETIX v. 4.05, logiciel sous Windows pour la ge´ne´tique des populations. Laboratoire Ge´nome et Populations. CNRS UPR 9060, Universite´ Montpellier II. Bellucci, L.G., Frignani, M., Paolucci, D., Ravanelli, M., 2002. Distribution of heavy metals in sediments of the Venice Lagoon: the role of the industrial area. The Science of the Total Environment 295, 35–49. Benzie, J.A.H., 2000. The detection of spatial variation in widespread marine species: methods and bias in the analysis of population structure in the crown of thorns starfish (Echinodermata: Asteroidea). Hydrobiologia 420, 1–14. Bulnheim, H.P., Bahns, S., 1996. Genetic variation and divergence in the genus Carcinus (Crustacea, Decapoda). Internationale Revue der gesamten Hydrobiologie 81, 611–619. Caley, M.J., Carr, M.H., Hixon, M.A., Hughes, T.P., Jones, G.P., Menge, B.A., 1996. Recruitment and local dynamics of open marine populations. Annual Reviews of Ecology, Evolution and Systematics 27, 477–500. Carlton, J.T., Cohen, A.N., 2003. Episodic global dispersal in shallow water marine organisms: the case history of the European shore crabs Carcinus maenas and C. aestuarii. Journal of Biogeography 30, 1809–1820.

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