K T C A P E I J N E N B U R G

C F A U V E L O T

S B J M E N K E N

Spatial and temporal genetic structure of the planktonic Sagitta setosa (Chaetognatha) in European seas as revealed by mitochondrial and nuclear DNA markers

Keywords: chaetognaths, deep clades, European seas, marine holoplankton, microsatellites, mitochondrial DNA, population structure

Little is known about the spatial and temporal scales at which planktonic organisms are genetically structured. A previous study of mitochondrial DNA (mtDNA) in the holoplanktonic chaetognath Sagitta setosa revealed strong phylogeographic structuring suggesting that Northeast (NE) Atlantic, Mediterranean and Black Sea populations are genetically disjunct. The present study used a higher sampling intensity and a combination of mitochondrial and four microsatellite markers to reveal population structuring between and within basins. Between basins, both marker sets indicated significant differentiation confirming earlier results that gene flow is probably absent between the respective S. setosa populations. At the within-basin scale, we found no evidence of spatial or temporal structuring within the NE Atlantic. In the Mediterranean basin, both marker sets indicated significant structuring, but only the mtDNA data indicated a sharp genetic division between Adriatic and all other Mediterranean populations. Data were inconclusive about population structuring in the Black Sea. The levels of differentiation indicated by the two marker sets differed substantially, with far less pronounced structure detected by microsatellite than mtDNA data. This study also uncovered the presence of highly divergent mitochondrial lineages that were discordant with morphology, geography and nuclear DNA. We thus propose the hypothesis that highly divergent mitochondrial lineages may be present within interbreeding S. setosa populations.

Introduction

Genetic studies of open ocean species have begun to challenge the long-standing view of low species diversity and broad-scale homogeneity in the pelagic realm [START_REF] Norris | Pelagic species diversity, biogeography, and evolution[END_REF]. On the one hand, genetic studies have indicated that sibling species are far more common in marine biota than previously thought [START_REF] Knowlton | Sibling species in the sea[END_REF][START_REF] Knowlton | Molecular genetic analyses of species boundaries in the sea[END_REF]. On the other hand, broad-scale surveys of genetic variation within marine species have shown that many are more structured than would be expected based on their large population size and high potential for dispersal [START_REF] Avise | Phylogeography: The History and Formation of Species[END_REF][START_REF] Grosberg | Genetic structure in the sea; from populations to communities[END_REF][START_REF] Hellberg | Genetic assessment of connectivity among marine populations[END_REF]. Genetic studies of marine holoplankton, the organisms that drift with the currents during their entire life, are particularly rare considering their abundance in the world's oceans, ecological importance and wide taxonomic representation (but see for examples of foraminifers: De [START_REF] Vargas | Molecular evidence of cryptic speciation in planktonic foraminifers and their relation to oceanic provinces[END_REF][START_REF] Vargas | A molecular approach to biodiversity and biogeography in the planktonic foraminifer Globigerinella siphonifera (d'Orbigny)[END_REF][START_REF] Darling | Molecular evidence for genetic mixing of Arctic and Antarctic subpolar populations of planktonic foraminifers[END_REF]dinoflagellates: John et al . 2003;copepods: Bucklin et al . 1996copepods: Bucklin et al . , 2000;;[START_REF] Goetze | Cryptic speciation on the high seas; global phylogenetics of the copepod family Eucalanidae[END_REF][START_REF] Goetze | Global population genetic structure and biogeography of the oceanic copepods Eucalanus hyalinus and S. Spinifer[END_REF]; euphausiids: [START_REF] Bucklin | Gene flow patterns of the euphausiid, Meganyctiphanes norvegica, in the NW Atlantic based on mtDNA sequences for cytochrome b and cytochrome oxidase I[END_REF]Zane et al . 1998Zane et al . , 2000;;cnidarians: Schroth et al . 2002;chaetognaths: Thuesen et al . 1993;[START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]). Yet, if we are to better understand how circulation (Fig. 1C, Zenkevitch 1963). The Mediterranean basin has sometimes been referred to as a 'sea of seas' because of its division into different subbasins, each with its own distinct characteristics, including partially enclosed gyral current systems (Fig. 1C; [START_REF] Pinardi | Variability of the large scale general circulation of the Mediterranean Sea from observations and modelling: a review[END_REF] resulting in distinct planktonic faunas [START_REF] Furnestin | Aspects of the zoogeography of the Mediterranean plankton[END_REF]). Hence, we would expect a higher degree of genetic structuring of S. setosa populations within the Mediterranean than within the NE Atlantic or Black Sea basins. Earlier results from mitochondrial cytochrome oxidase II (COII) sequences have indeed suggested that this may be the case [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]). However, a larger sampling intensity of individuals is needed to reveal such smallscale structuring.

The present study used a more intensive sampling of S. setosa from three European seas and employed both mitochondrial and microsatellite DNA markers. Furthermore, for the NE Atlantic, additional sampling localities were included to obtain a fine-scale spatial coverage of the continental shelf, which was repeated 2 years later. We used restriction fragment length polymorphism (RFLP) analysis to efficiently screen a large number of individuals for variation within the mitochondrial COII region and we developed four nuclear-encoded microsatellite loci. First, at the between-basin scale, we tested whether phylogeographic patterns were congruent between marker sets, as well as with earlier work based on a more limited sampling of S. setosa . Second, we examined patterns of genetic variation at within-basin scales: in the NE Atlantic we tested for small-scale spatial and temporal genetic structuring, whereas in the Mediterranean and Black Sea we tested the prediction that distinct hydrographic features limit gene flow. Finally, we compared the patterns of differentiation within basins to test the hypothesis of more genetic heterogeneity and population structuring in the Mediterranean than the NE Atlantic.

Materials and methods

Sampling and DNA extraction

We examined Sagitta setosa collected over a period of 4 years (1999)(2000)(2001)(2002) from three European seas. Analyses were based on 39 samples as well as 17 samples pooled per region and year (Table 1; Fig. 1). A thorough sampling scheme was employed for the NE Atlantic with a fine-scale spatial coverage of the continental shelf region in 1999 and 2000, and an approximate repetition of this sampling scheme in 2001 (Table 1; Fig. 1A,B). To avoid sampling the same population multiple times, samples were collected over a short period and against the general circulation pattern. Additionally, for some locations (99skag1, 01skag2, 01sb1 and 01sb3) we collected multiple samples: (i) from separate hauls (denoted A and B); or (ii) as juveniles, when both juveniles and adults were present (denoted juv, see sample codes Table 1; Fig. 1A,B). Samples from the Mediterranean were collected from the Ligurian Sea (1999and 2001), Tyrrhenian Sea (2002), Gulf of Gabès (2002) and Adriatic Sea (2000; Table 1; Fig. 1C). Black Sea samples were collected in 2000 (00black1 and 00black2) and 2002 (02black1). Further sampling, preservation and DNA extraction protocols were described in [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF].

Mitochondrial DNA

A section of the mitochondrial COII gene was screened for polymorphism using three restriction enzymes: Bst EII (G ∼ GTNACC, Live Technologies), Ita I (GC ∼ NGC, Roche Diagnostics) and Hap II (C ∼ CGG, Live Technologies). A 551-bp COII fragment was amplified using the primers and conditions described in [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]. Restriction digestions were carried out in 20-µ L total volumes containing 1 U of a single enzyme, 4 µ L of the PCR (polymerase chain reaction) product and enzyme specific buffers according to manufacturer's recommendations. Resulting fragments were run on 1.5% agarose gels with TBE buffer, stained with ethidium bromide and viewed under ultra violet light. Fragment sizes were estimated against a 100-bp sizing ladder.

Composite PCR-RFLP haplotypes for each individual were defined based on restriction-site data and coded into binary (1,0) format. In order to resolve restriction-site data for new haplotypes and to reveal the presence of deep mitochondrial lineages, each PCR-RFLP haplotype, from different basins where possible, was sequenced (methods described in [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF][START_REF] Papadopoulos | Phylogeography of the calanoid copepods Calanus helgolandicus and C. euxinus suggests Pleistocene divergences between Atlantic, Mediterranean, and Black Sea populations[END_REF]. These data were added to an existing data set of 86 unique sequences published in [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF], totalling 148 unique COII sequences (GenBank accession numbers AY585565 -AY585650 and DQ486064 -DQ486126). The Tamura-Nei model (1993) with site-specific rates was selected using modeltest [START_REF] Posada | modeltest: testing the model of DNA substitution[END_REF] and used in distance calculations between sequence haplotypes. From this distance matrix, we constructed a neighbourjoining (NJ) tree in paup * 4.0b 10 (Swofford 1998) and assessed clade support by NJ bootstrap analysis with 1000 replicates.

After excluding all individuals belonging to deep mitochondrial clades (see Results), the number of nucleotide 1990) weighted by the employed enzyme classes [START_REF] Nei | Maximum likelihood estimation of the number of nucleotide substitutions from restriction sites data[END_REF] between PCR-RFLP haplotypes was calculated using the program d in reap [START_REF] Mcelroy | reap: an integrated environment for the manipulation and phylogenetic analysis of restriction data[END_REF]. reap was also used to estimate haplotype diversity (h; Nei 1987) and nucleotide diversity (π; [START_REF] Nei | DNA polymorphism detectable by restriction endonucleases[END_REF] within samples as well as nucleotide divergence [START_REF] Nei | DNA polymorphism detectable by restriction endonucleases[END_REF] between samples (program da).

To test for population structuring, we estimated differentiation between samples as F ST incorporating distance between haplotypes (as estimated by d in reap) and tested for divergence from the null distribution of no differentiation by 10 000 permutations, as implemented in arlequin version 2.000 [START_REF] Schneider | ARLEQUIN, version 2.000: A software for population genetics data analysis genetics and biometry laboratory[END_REF]. For comparison, we also tested for differentiation between samples using exact tests of haplotype frequencies (not incorporating distance between haplotypes) using a Markov chain procedure (100 000 steps). For these, and all further analyses involving multiple simultaneous tests, we adjusted significance levels by applying a sequential Bonferroni correction with an initial α of 0.05 [START_REF] Rice | Analysing tables of statistical tests[END_REF].

Hierarchical analyses of molecular variance (amova; [START_REF] Excoffier | Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data[END_REF] were performed using arlequin to examine the partitioning of the total variance between various groups of samples. First, using the complete RFLP data set, we examined the partitioning of mitochondrial variation among the three basins, among samples within each basin and within samples. Second, we conducted single-level amovas (one group of samples) to look at the partitioning of variation within basins, and among unpooled and pooled samples. Third, we carried out multilevel amovas (several groups of samples) to examine the proportion of within-basin genetic variance distributed among regions (for NE Atlantic and Mediterranean Sea only) and among years (for the NE Atlantic only). Significance of Φ-statistics (and associated variance components) was tested using 10 000 permutations of: (i) haplotypes among samples (for single-level amovas) and among groups (for multilevel amovas) for Φ ST ; (ii) haplotypes among samples within groups for Φ SC ; and (iii) samples among groups for Φ CT [START_REF] Schneider | ARLEQUIN, version 2.000: A software for population genetics data analysis genetics and biometry laboratory[END_REF].

Microsatellite DNA

We used two protocols to obtain microsatellite markers for S. setosa, namely random amplified polymorphic DNA (RAPD) enrichment combined with repeat-specific PCR isolation of microsatellite arrays (PIMA method; [START_REF] Lunt | An efficient method for PCR-based isolation of microsatellite arrays (PIMA)[END_REF] and enrichment with biotin-labelled repeat sequences (see [START_REF] Boneh | Microsatellites in the bromeliads Tillandsia fasciculata and Guzmania monostachya[END_REF]. Fourteen primer pairs (seven from each protocol) were designed using the primer 3 program (Rozen & Skaletsky 1998) and tested for optimal annealing temperatures and PCR conditions. Of these, only four primer pairs resulted in reliable and interpretable fragment patterns (Table 2).

Four microsatellite loci were PCR amplified in 10 µL total volumes containing 1.2 µL genomic DNA, 1 µL 10× PCR buffer (HT Biotechnology), 4 µL dNTPs (2 mm each), 0.16 µL of each of two primers (10 µm), 0.4 unit of Taq polymerase (HT Biotechnology), 0.4 µL MgCl 2 (25 mm) and 0.5 µL bovine serum albumin (BSA, 1 mg/mL) for loci Sset-1, Sset-2 and Sset-4, and 0.7 µL MgCl 2 and 0.7 µL BSA for locus Sset-3. Amplifications were carried out in a Biometra thermocycler (GmBH) with the following profile: 3 min at 94 °C, followed by 35 cycles of 25 s at 94 °C, 40 s at annealing temperature (Table 2), 25 s at 72 °C and a final extension step of 5 min at 72 °C. Amplified PCR products were diluted 10-15 times, electrophoresed and visualized on 6.5% denaturing polyacrylamide gels in a LI-COR DNA Sequencer 4200. To ensure accuracy and consistency of scoring across gels, PCR products of four individuals with known genotypes were run for every locus in the middle and at both sides of each gel, as well as commercially available sizing standards (50 -350 bp or 50 -700 bp, LI-COR) for the two most variable loci. In addition, all samples with non-stepwise alleles (incongruent with the repeat motif, observed at Sset-1, Sset-3 and Sset-4) or nonamplifications (observed at Sset-3 and Sset-4) along with approximately Each allele was given a unique three-digit code based on its length in base pairs, and nonamplifying individuals were coded as missing data. Microsatellite diversity within samples was estimated as the number of alleles per locus (N a ), observed heterozygosity (H O ) and unbiased gene diversity (expected heterozygosity, H E ; Nei 1987) using fstat version 2.9.3.2 [START_REF] Goudet | FSTAT: A program to estimate and test gene diversities and fixation indices, version 2.9[END_REF]. Genotypes at all pairs of loci were tested for genotypic disequilibrium, and deviations from Hardy-Weinberg equilibrium (HWE) were estimated using Weir & Cockerham's (1984) inbreeding coefficient ƒ (equivalent to Wright's F IS ) in genepop version 3.4 [START_REF] Raymond | genepop (version 1.2): population genetics software for exact tests and ecumenicism[END_REF]. Locus conformance to HWE (any departure) was assessed using exact tests with significance determined by a Markov chain method, also in genepop.

The presence of null alleles was detected at loci Sset-3 and Sset-4 using the program micro-checker [START_REF] Van Oosterhout | micro-checker: software for identifying and correcting genotyping errors in microsatellite data[END_REF]. For samples where observed genotype frequencies deviated significantly from HWE expectations, null allele frequencies were estimated using [START_REF] Brookfield | A simple new method for estimating null allele frequency from heterozygote deficiency[END_REF] null allele estimator 2 (taking into account the presence of null allele homozygotes) and an alternative data set was generated in which each null allele was recoded as missing data (using micro-checker).

Analyses to estimate differentiation between samples were carried out for both uncorrected data and data corrected for null alleles in genepop. To quantify the differentiation between samples, we used the infinite allele model (IAM; [START_REF] Kimura | The number of alleles that can be maintained in a finite population[END_REF] because our microsatellite loci did not conform to the strict expectations of the stepwise-mutation model [START_REF] Kimura | Stepwise mutation model and distribution of allelic frequencies in a finite population[END_REF]. Thus, IAM-based estimates of F ST (θ; following Weir & Cockerham 1984) were calculated between all sample pairs and visualized using metric multidimensional scaling analyses (MDS; [START_REF] Kruskal | Multidimensional Scaling[END_REF] in xlsstat version 7.5 (available from http://xlstat.com). To test the null hypothesis of identical allele frequency distributions across samples, an unbiased estimate of the significance was calculated as described in [START_REF] Raymond | genepop (version 1.2): population genetics software for exact tests and ecumenicism[END_REF].

We estimated the partitioning of total genetic variance based on allelic frequencies using amova as implemented in arlequin, with the same groupings as for mtDNA analyses.

Pairwise F ST estimates over all loci were used as distance measure for microsatellite data (Weir & Cockerham 1984;[START_REF] Michalakis | A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci[END_REF].

Results

Mitochondrial DNA variability

The COII fragment was successfully amplified and digested in 1739 individuals from three different basins. It is worth noting that a substantial proportion of the Black Sea samples (39%) did not amplify with our COII primers. This proportion was much smaller for NE Atlantic and Mediterranean samples (< 0.01% and 0.06%, respectively). Three restriction enzymes sampled 52 bp, representing 12 restriction sites that were spread along the 551-bp fragment (Fig. 2). All restriction sites were variable and resulted in 35 composite PCR-RFLP haplotypes (Table 3).

Sequence analysis of representatives of each PCR-RFLP haplotype produced a phylogenetic tree consisting of three highly divergent clades, denoted 'S. setosa', 'Sagitta sp. I' and 'Sagitta sp. II', with average maximum-likelihood distances of 14.49 -25.92%, as opposed to within-clade divergences of 0.20 -4.42% (Fig. 3). Sagitta sp. I is identified by haplotype H35 and is exclusive to the Black Sea (Sagitta sp. in [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]). Sagitta sp. II is identified by haplotype H34 and was found in the NE Atlantic at very low frequencies in different sampling locations (Table 3; Fig. 3). These highly divergent clades may represent different, probably cryptic, species and are therefore not included in population genetic analyses of S. setosa (but see Discussion).

A total of 1724 individuals were identified with haplotypes H1-H33 and were considered S. setosa (Table 3). Mean haplotype diversity (± SD) across all samples was 0.37 ± 0.11 and nucleotide diversity 0.92 ± 0.40%. Both measures of diversity (h and π, respectively) were highest in Mediterranean samples (0.50 ± 0.04 and 1.39 ± 0.17%), intermediate in Black Sea samples (0.40 ± 0.14 and 1.12 ± 0.66%) and lowest in NE Atlantic samples (0.30 ± 0.05 and 0.65 ± 0.12%). Estimates differed significantly for comparisons between Mediterranean and NE Atlantic samples only (two-tailed Student's t-test, P(h) = 0.0001 and P(π) = 0.0001). Haplotype frequencies were highly skewed for all Table 3 Percentage frequency of 35 composite mitochondrial PCR-RFLP DNA haplotypes (H1-H35) for 17 (pooled) samples of Sagitta setosa from the Northeast Atlantic, Mediterranean and Black Sea. The cytochrome oxidase II region was surveyed by RFLP analysis with restriction enzymes BstEII, ItaI and HapII (ones and zeros indicate the presence and absence, respectively, of restriction sites that are ordered by distance from the start of the forward primer; see Fig. 2 for restriction map). Samples from which at least one representative was sequenced for a particular haplotype (and included in the phylogenetic analysis; see Fig. 3) are underlined (see Material and methods). Haplotype diversity (h) and nucleotide diversity (π) for each sample are given at the bottom of the table (excluding highly divergent haplotypes H34 and H35; see text and legend Fig. 3 3). Frequency distributions for NE Atlantic samples were most skewed with a single common haplotype at very high frequencies (79.2 -87.4%) and 23 haplotypes at very low frequencies (0.4 -8.3%). Frequency distributions of Mediterranean samples were least skewed with two relatively common haplotypes (H12: 17.5 -70.5% and H13: 15.9 -67.5%) and 14 rare haplotypes (1.0 -5.2%).

- - - - - - - - H5 00 011 - - 1.3 - - - - 0.4 - - - - - - - - - H6 00 011 - 0.6 - 2.1 - - - 0 . 4 1.0 - - - - - - - - H7 00 011 - 1.1 - - - - - - - - - - - - - - - H8 00 011 - - - - - 0.7 - - - - - - - - - - - H9 00 011 - 0.6 0.6 - - 1.4 - 0.9 - 2.0 - - - - - - - H10 00 011 - - - - - 0.7 - - - - - - - - - - - H11 00 011 - - - - - - - 0.4 - - - - - - - - - H12 00
- - - - - - - 0.5 - 1.0 1.1 1.5 - - 2.7 - H16 00 001 - - - - - - - - - - 2.1 - 1.5 - - 8.1 - H17 00 001 - - - - - - - - - - 2.1 1.1 - 1.4 3.8 - - H18 00 001 - - - - - - - - - - 2.1 3.4 - 1.4 2.5 - 3.3 H19 00 001 - - - - - - - - - - - - - - 1.3 - - © 2006 The Authors Journal compilation © 2006 Blackwell Publishing Ltd H20 00 001 - - - - - - - - - - - - - - 3.8 - - H21 00 111 0.6 0.6 - - - 0.7 2.0 0.4 0.5 2.0 - - - - - - - H22 00 101 - - - - - - - - - - 1.0 - - 1.4 - - - H23 00 101 - - - - - - - - - - - - - - 1.3 - - H24 00 101 - - - - - - - - - - - 1.1 - - - - - H25 00 010 0.6 - - - - - - - 0.5 - - - - - - - - H26 00 010 - - - - - - - 0.4 - - - - - - - - - H27 10 011 - - 0.6 - - - - 0.4 - - - - - - - - - H28 10 011 0.6 - - - - - - - - - - - - - - - - H29 10 101 - - - - - - - - - - - - 1.5 - - - - H30 01 011 - 1.1 0.6 - - - - - - - - - - - - - -

Microsatellite variability

The same individuals that were used in mtDNA analyses were screened for microsatellite variability. It is notable that individuals belonging to deep mitochondrial clades (Sagitta sp. I and Sagitta sp. II) and those that failed to amplify with the mitochondrial primers (mostly Black Sea samples) successfully amplified with all microsatellite primer pairs and produced common allelic patterns. This suggests that bad DNA quality is not a likely cause of the mitochondrial amplification failures. Nevertheless, microsatellite analyses of population structure only included the 1724 individuals that were identified by mitochondrial haplotypes H1-H33 (considered S. setosa).

The four microsatellite loci varied widely in number of alleles (2 -42, mean = 16.5) and expected heterozygosities (0.015-0.956, mean = 0.660) across samples (Table 4). Non-stepwise alleles were observed at low frequencies at Sset-1 (0.64%), Sset-3 (1.61%) and Sset-4 (2.65%). Tests of genotypic disequilibrium within samples between the four loci indicated no significant association of alleles (all P > 0.33), confirming that all loci can be treated as independent markers. Levels of genetic variability over all loci were similar across samples between the three basins, with no evidence against the null hypothesis of equal mean values of observed heterozygosities (two-tailed Student's t-test, P = 0.126 -0.437).

Heterozygote deficiencies were observed at Sset-3 and Sset-4 for all samples (F IS values ranged from 0.155 to 0.554 for Sset-3 and 0.100-0.435 for Sset-4) and were significant except for BLACK00 at Sset-3, and CELT00 and ATL01 at Sset-4 (Table 4). By contrast, all samples conformed to HWE expectations at Sset-1 (F IS = -0.047-0.182) and Sset-2 (F IS = -0.105 -0.187). Null alleles were the most likely cause of departures from HWE and estimated null allele frequencies (r) per sample were high (0.108 -0.411 for Sset-3 and 0.160 -0.311 for Sset-4; Table 4). Results for the unpooled data set of 39 samples were similar but are not shown here because of space limitations. Allele frequencies at each locus for each sample from original and adjusted data sets are available upon request. There was some bootstrap support for the grouping of NE Atlantic haplotypes H32 and H33 (59%) and Black Sea haplotypes H31 (69%) into two separate clades within Clade A (Fig. 3). Congruent with sequence data, frequency distributions of RFLP haplotypes showed a distinct division of samples into two major groups: a NE Atlantic group with H1 as most common haplotype and a Mediterranean/Black Sea group with H12 or H13 as most common haplotype (Table 3). Not surprisingly, differentiation between the three basins was highly significant (global exact test, P < 0.001). F ST estimates between basins were 0.71 (NE Atlantic-Mediterranean), 0.73 (NE Atlantic-Black Sea) and 0.15 (Mediterranean-Black Sea), and all differed significantly from zero (permutation tests, all P < < 0.001). Net nucleotide divergence between basins amounted to 2.15% (NE Atlantic-Mediterranean), 1.75% (NE Atlantic-Black Sea) and 0.29% (Mediterranean-Black Sea).

Within the NE Atlantic, no evidence of geographic or temporal structuring of samples was observed (global exact test for unpooled and pooled data sets, P > 0.16). Within the Mediterranean basin, however, significant differentiation was observed (global exact test for unpooled and pooled data sets, P < 0.001). This was mainly the result of a reversal in relative frequencies of H12 and H13 in samples from the Adriatic Sea compared to other Mediterranean samples (Table 3). F ST values between the Adriatic Sea and all other Mediterranean samples were very high (0.31-0.36) and highly significant (Table 5). Conversely, no differentiation was apparent among the Ligurian Sea, Tyrrhenian Sea and Gulf of Gabès samples. F ST estimates between three samples from the Black Sea ranged from 0.02 to 0.10 and did not significantly differ from zero. When pooled per year, F ST amounted to 0.09 and was marginally significant, suggesting that low sample sizes may have been the reason for nonsignificant differentiation within this basin [Table 5; global exact test, P (unpooled) = 0.14 and P (pooled) = 0.02].

Analysis of molecular variance (amova) indicated that a large proportion of the total mitochondrial variation resided among basins (69.81%, Table 6). Within the NE Atlantic, a small and nonsignificant part of the variance was distributed among regions (0.16%) or among years (0.19%). For both the NE Atlantic and Black Sea, the proportion of the variance distributed among pooled samples was higher than for unpooled samples, but not significant after Bonferroni corrections. Within the Mediterranean basin, however, large portions of the total mitochondrial variation were distributed among regions (20.0%) or among samples (15.47 and 17.10%), and all associated Φ ST values were highly significant (Table 6).

Microsatellite DNA. All four loci contributed to betweensample differentiation, although not equally (global single locus F ST values were 0.006 for Sset-1, 0.015 for Sset-2, 0.012 for Sset-3 and 0.011 for Sset-4 using data uncorrected for null alleles). Significant heterogeneity between sample pairs was most often detected between samples from different basins: 90.6% and 100% of all significant comparisons using uncorrected and data corrected for null alleles, respectively. Since levels of differentiation can be substantially overestimated when null alleles are present [START_REF] O'reilly | Inverse relationship between F ST and microsatellite polymorphism in the marine fish, walleye pollock (Theragra chalcogramma): implications for resolving weak population structure[END_REF], we present results for the corrected data set unless stated otherwise. Multilocus estimates of F ST between basins were 0.012 (NE Atlantic-Mediterranean), 0.016 (NE Atlantic-Black Sea) and 0.020 (Mediterranean-Black Sea). When pairwise F ST values were plotted using MDS between all unpooled and pooled samples (Fig. 4A andB, respectively), it is clear that pooling of samples increased the signal of differentiation between basins as opposed to the differentiation within basins. By pooling samples, and thus increasing sample sizes, the statistical power to reject homogeneity between basins also increased.

Pairwise multilocus F ST between pooled samples and the probability of genetic heterogeneity are given in Table 5. Within the NE Atlantic, F ST estimates were very low, with a maximum of 0.026 between 99skag1A and 01sb1B for the unpooled data set, and 0.012 between ATL01 and GB01 for the pooled data set. None of the pairwise exact tests within this basin were significant after Bonferroni corrections. Global exact tests over all NE Atlantic samples were significant when samples were unpooled (P = 0.001) but insignificant when samples were pooled (P = 0.089) although F ST estimates were negligible in both cases (-0.0006 and 0.0003, respectively). Similarly, the MDS plots show no association of samples according to different regions or years within the NE Atlantic, although 'ATL01' appears separate from other samples (Fig. 4). Possible reasons for this are the relatively low sample size of 'ATL01' (n = 50) because it was not pooled with other samples, and the fact that it was not corrected for null alleles at locus Sset-4. Within the Mediterranean Sea, global exact tests were significant for both unpooled and pooled data sets (P = 0.0001 and 0.0021, respectively). However, none of the pairwise tests indicated significant genic differentiation (Table 5). Pairwise F ST values were negative for all comparisons except between the Gulf of Gabès (GAB02) and other Mediterranean samples, which were very low (0.0005 -0.0041) and only marginally significant (Table 5). Contrary to results from mtDNA data, the Adriatic Sea was not significantly differentiated from other Mediterranean samples. Within the Black Sea, no evidence of sample differentiation was found (global exact test, P = 0.15 and 0.60 for unpooled and pooled data, respectively).

Consistent with these results, amovas based on allelic frequencies indicated that a tiny, but highly significant, fraction of the total nuclear variance was distributed among the three basins (0.93%) and that the vast majority was present within samples (99.03%, Table 6). For both the NE Atlantic and Black Sea, amovas did not detect any significant portion of the variance distributed among samples, regions or years within these basins. Within the Mediterranean basin, however, a small but significant portion was distributed among pooled samples (0.58%), which resulted from inclusion of the Gulf of Gabès sample only.

Discussion

Combined analysis of mitochondrial and nuclear data for 1724 Sagitta setosa individuals from three European Table 6 Hierarchical analyses of mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) molecular variance (amova) amongst three European basins (A), within the Northeast Atlantic (B), within the Mediterranean Sea (C) and within the Black Sea (D) showing the proportion of total variance distributed between various groups of samples. For within basin comparisons, the results of two single-level amovas (one group of samples) are given first for separate and pooled samples, respectively, and as the among-samples component only (Φ ST ). Associated with each hierarchical level are: degrees of freedom (d.f.), percentage of total variance explained (%), Φ-statistics incorporating [START_REF] Nei | DNA polymorphism detectable by restriction endonucleases[END_REF] distance among RFLP haplotypes for mtDNA and using allele frequencies (multilocus, corrected for null alleles) for nDNA. Estimates of statistical significance are given as probabilities of having, by chance alone, a more extreme variance component and Φ-statistic than the observed value (determined by 10 000 permutations): NS, not significant, *P < 0.05, **P < 0.01, ***P < 0.001; values in bold type remain significant after sequential Bonferroni corrections seas revealed qualitatively congruent patterns of differentiation on large (between-basin) and smaller (within-basin) spatial scales. Both data sets indicated significant differentiation between populations from the NE Atlantic, Mediterranean and Black Sea. Congruent patterns were also observed within basins with no evidence of spatial or temporal structuring in the NE Atlantic, evidence of differentiation in the Mediterranean Sea (although patterns of substructuring were different as indicated by the two marker sets), and inconclusive results about structuring in the Black Sea. Quantitatively, however, the levels of differentiation were different, with far less pronounced structure detected by nuclear than mitochondrial markers. Alongside such information on S. setosa population structure, our study uncovered the presence of highly divergent mitochondrial lineages that were discordant with morphology, geography and nuclear DNA.

Between-basin genetic structure

Estimates of divergence based on COII PCR-RFLPs were high, with similar F ST estimates between NE Atlantic and Mediterranean, and NE Atlantic and Black Sea populations, and the lowest estimate between Mediterranean and Black Sea populations. These results are congruent with sequence data from the same fragment, although estimates of net nucleotide divergence from RFLPs were lower compared to sequence data (a factor of 1.5-3.3; [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]). This may be explained by the fact that sequence data were corrected for saturation whereas RFLP data were not. Moreover, restriction enzymes sampled only a subset of the variation present within the COII fragment. This also explains why some RFLP haplotypes (e.g. H1 and H12-H15) were shared across basins while sequences of representatives of these haplotypes were grouped into monophyletic geographic clades (Fig. 3). Taken together, RFLP analysis provided an efficient method for screening large numbers of individuals for mtDNA variation and corroborate earlier conclusions that Atlantic, Mediterranean and Black Sea populations of S. setosa are probably disjunct. Furthermore, the pattern of population divergences is consistent with deep splits between Atlantic and Mediterranean populations and colonization of the Black Sea from the Mediterranean as proposed by [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]. Similar patterns were observed for mussels [START_REF] Ladoukakis | Mitochondrial DNA variation in a species with two mitochondrial genomes: the case of Mytilus galloprovincialis from the Atlantic, the Mediterranean and the Black Sea[END_REF] and copepods [START_REF] Papadopoulos | Phylogeography of the calanoid copepods Calanus helgolandicus and C. euxinus suggests Pleistocene divergences between Atlantic, Mediterranean, and Black Sea populations[END_REF].

Microsatellite data also indicated significant structuring among basins. However, F ST estimates were much lower than RFLP-based estimates (a factor of 7.6 -62.0). Contrary to mtDNA data, the lowest levels of differentiation were between NE Atlantic and Mediterranean, and NE Atlantic and Black Sea populations, and the highest estimate between Mediterranean and Black Sea populations. This discrepancy is most likely the result of higher levels of homoplasy for microsatellites than mtDNA, although other explanations are also possible (see below). Microsatellite data, however, provide independent support that phylogeographic breaks (sensu category I of [START_REF] Avise | Intraspecific phylogeography: the mitochondrial DNA bridge between population genetics and systematics[END_REF]) exist between S. setosa populations from the three European seas.

Within-basin genetic structure

Genetic homogeneity among samples from different regions and years suggests that S. setosa constitutes a single interbreeding population within the NE Atlantic. This result is not surprising for a holoplanktonic species, considering that there is a single anticlockwise circulation in this region, and that the relevant time scales for mixing of waters from the entire shelf is of the order of years [START_REF] Prandle | A modelling study of the mixing of 137Cs in the seas of the European continental shelf[END_REF]. Similar results were obtained for NE Atlantic populations of flatfish [START_REF] Borsa | Genetic structure of the flounders Platichthys flesus and P. stellatus at different geographic scales[END_REF][START_REF] Hoarau | Population structure of plaice (Pleuronectes platessa L.) in northern Europe: microsatellites revealed large-scale spatial and temporal homogeneity[END_REF][START_REF] Hoarau | Population structure of plaice (Pleuronectes platessa L.) in northern Europe: a comparison of resolving power between microsatellites and mitochondrial DNA data[END_REF] and squid [START_REF] Shaw | Subtle population structuring within a highly vagile marine invertebrate, the veined squid Loligo forbesi, demonstrated with microsatellite DNA markers[END_REF]. Two other studies, however, examining mitochondrial variation in a goby [START_REF] Gysels | Phylogeography of the common goby, Pomatoschistus microps, with particular emphasis on the colonization of the Mediterranean and the North Sea[END_REF]) and crab [START_REF] Roman | A global invader at home: population structure of the green crab, Carcinus maenas, in Europe[END_REF], reported a barrier separating North Sea populations from more southern populations in the English Channel and Celtic Sea. An alternative explanation for the observed homogeneity of S. setosa is that populations have not yet reached an equilibrium between gene flow and genetic drift, so that significant structuring is not revealed by our genetic markers. Even though we cannot exclude this explanation, it is unlikely because of our extensive sampling of mitochondrial and nuclear diversity and the fact that we found significant structuring within the Mediterranean basin using the same markers.

Another pattern contained in our NE Atlantic data set was that of genetic patchiness, i.e. samples separated by 0-100 km were sometimes as genetically different as samples separated by 100 -1000 km (Fig. 4). Some pairwise F ST estimates were indeed quite high for a planktonic organism (max. 0.037 for mtDNA and 0.026 for microsatellites) and some indicated marginally significant differentiation. However, such patterns were not consistent across markers nor across years, and are therefore not interpreted in terms of population structuring. One explanation for these 'chaotic' patterns may be that insufficient sample sizes resulted in overestimates of genetic differentiation, especially since levels of variability of our markers were very high and levels of differentiation within this basin were very low [START_REF] Ruzzante | A comparison of several measures of genetic distance and population structure with microsatellite data: bias and sampling variance[END_REF][START_REF] Waples | Separating the wheat from the chaff: patterns of genetic differentiation in high gene flow species[END_REF][START_REF] Kalinowski | Do polymorphic loci require large sample sizes to estimate genetic distances?[END_REF]. Support for this comes from the fact that our estimates of F ST and significance of exact tests were generally reduced when NE Atlantic samples were pooled per region and year to increase sample size.

Significant heterogeneity within the Mediterranean basin was detected by both mtDNA and microsatellite markers, and for both unpooled and pooled data sets. This © 2006 The Authors Journal compilation © 2006 Blackwell Publishing Ltd result is consistent with our hypothesis that higher levels of genetic structuring were expected for the Mediterranean than the NE Atlantic because of the division into several subbasins and the presence of fragmented neritic habitats in the Mediterranean basin. Also consistent with this hypothesis is the higher level of mitochondrial diversity in Mediterranean than NE Atlantic samples, though this pattern was not apparent for microsatellites. Frequency distributions of RFLP haplotypes were also least skewed, and genealogical structure was deepest for Mediterranean samples. These results suggest that Mediterranean S. setosa populations are genetically structured because gene flow between sub-basins is limited, or alternatively, that populations are presently connected by gene flow but diverged in the past (e.g. because of survival in different Pleistocene refugia). Mediterranean populations may also have had an effective population size larger than the NE Atlantic population, possibly resulting from less severe bottlenecks and/or selective sweeps. A combination of these possibilities is also conceivable.

Mitochondrial DNA data provided strong evidence that the Adriatic population of S. setosa is genetically differentiated from Ligurian, Tyrrhenian and Gulf of Gabès populations. Microsatellites, however, did not detect any differentiation between Adriatic and other Mediterranean samples, but instead suggested slight and marginally significant differentiation of the Gulf of Gabès sample. It should be stressed, though, that this differentiation was minimal, and based on allelic frequency differences at a single microsatellite locus (Sset-4) and a single sample from the Gulf of Gabès, and should therefore be interpreted cautiously. Differentiation between Adriatic and western Mediterranean populations is consistent with earlier results for S. setosa [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF], several marine fish [START_REF] Borsa | Genetic structure of the flounders Platichthys flesus and P. stellatus at different geographic scales[END_REF][START_REF] Bahri-Sfar | Fragmentation of sea bass populations in the western and eastern Mediterranean as revealed by microsatellite polymorphism[END_REF][START_REF] Stefanni | Mitochondrial DNA phylogeography reveals the existence of an Evolutionarily Significant Unit of the sand goby Pomatoschistus minutus in the Adriatic (Eastern Mediterranean)[END_REF]) and a bivalve [START_REF] Mariani | Genetic structuring and gene flow in Cerastoderma glaucum (Bivalvia: Cardiidae): evidence from allozyme variation at different geographic scales[END_REF].

There are several, mutually nonexclusive explanations for the discrepancy between mitochondrial and nuclear data in detecting the differentiation of the Adriatic Sea population, and for the higher estimates of population divergence detected by mtDNA in general. First, mitochondrial markers are generally more sensitive in detecting differentiation because of a lower effective population size than nuclear markers (e.g. [START_REF] Buonaccorsi | Reconciling patterns of inter-ocean molecular variance from four classes of molecular markers in blue marlin (Makaira nigricans)[END_REF][START_REF] Shaw | Genetic structuring of Patagonian toothfish populations in the Southwest Atlantic Ocean: the effect of the Antarctic Polar Front and deepwater troughs as barriers to genetic exchange[END_REF]). Second, sex-biased dispersal or survival is often cited to explain discrepancies between mtDNA and nDNA, but this seems implausible for a hermaphroditic species (but see Diaz-Almela et al. 2004 for a possible example). Third, selection may have differential effects on different genetic markers. Most examples, however, involve selection acting on certain allozyme loci (e.g. [START_REF] Lemaire | Do discrepancies between microsatellite and allozyme variation reveal differential selection between sea and lagoon in the sea bass (Dicentrarchus labrax)?[END_REF][START_REF] De Innocentiis | Allozyme and microsatellite loci provide discordant estimates of population differentiation in the endangered dusky grouper (Epinephelus marginatus) within the Mediterranean Sea[END_REF][START_REF] Arnaud-Haond | Large discrepancies in differentiation of allozymes, nuclear and mitochondrial DNA loci in recently founded Pacific populations of the pearl oyster Pinctada margaritifera[END_REF]. A dominant role for purifying selection in the evolution of mtDNA has also been suggested [START_REF] Ballard | The incomplete natural history of mitochondria[END_REF]. We believe that selection on mtDNA may accelerate the coalescence of lineages, and thus increase levels of differentiation observed between populations. Furthermore, the effect of (slight) selection pressures may be more important in large (pelagic) populations of marine organisms because the effect of drift is diminished. In particular, we reported indirect evidence of selection acting on the mitochondrial COII region in chaetognaths [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF][START_REF] Peijnenburg | Presentday genetic composition suggests contrasting demographic histories of two dominant chaetognaths of the North-East Atlantic, Sagitta elegans and S. setosa[END_REF]. Fourth, various technical problems may have reduced the signal of differentiation detected by our microsatellite markers. Microsatellites probably suffered from higher levels of homoplasy than mtDNA because of high mutation rates (especially when combined with allelic size constraints) and larger effective population size, inflating the apparent similarity between populations [START_REF] Nauta | Constraints on allele size at microsatellite loci: implications for genetic differentiation[END_REF][START_REF] Balloux | Microsatellites can be misleading: an empirical and simulation study[END_REF][START_REF] Estoup | Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis[END_REF]. Also, two out of four microsatellite loci were hypervariable (Sset-3 and Sset-4 had 54 and 89 alleles, respectively) and suffered from a high incidence of null alleles. Our results indicated that correcting for null alleles reduced the signal as well as the power to detect differentiation. Similarly, O' Reilly et al. (2004) comparing 14 microsatellite loci with varying levels of polymorphism in a marine fish, showed that estimates of differentiation and the probability to detect genetic differences among populations both diminished when locus heterozygosities increased and data were corrected for null alleles. Finally, a potential problem with the microsatellite data set is related to sample size. Even though sample sizes of at least 50 are generally considered sufficient to produce precise estimates of F ST with highly variable loci [START_REF] Ruzzante | A comparison of several measures of genetic distance and population structure with microsatellite data: bias and sampling variance[END_REF], this may not be the case when F ST values are low [START_REF] Kalinowski | Do polymorphic loci require large sample sizes to estimate genetic distances?[END_REF]. We found that the signal of differentiation between basins and within the Mediterranean basin increased when samples were pooled (Fig. 4; Table 6), suggesting that (much) larger samples may be necessary to detect differentiation of S. setosa populations in the Mediterranean Sea using highly variable nuclear markers. Summarizing, our data are inconclusive about whether Adriatic populations are presently connected by gene flow with other Mediterranean populations; however, mitochondrial data indicate that they have diverged in the past. Likewise, we cannot conclude whether populations within the Black Sea are genetically structured because sampling intensity was probably too low to detect subtle levels of differentiation with our highly variable markers.

Discordant mitochondrial lineages

Using RFLPs to screen for polymorphism within the COII fragment and subsequent sequencing of new haplotypes, we uncovered the presence of three groups of divergent mitochondrial lineages that were discordant with geography (Fig. 3). These groups were denoted Clade A, Sagitta sp. I and Sagitta sp. II and were detected in samples that were morphologically identified as S. setosa. Interestingly, all samples with divergent mtDNA showed common nuclear DNA patterns. Furthermore, mitochondrial amplification failures of Black Sea samples (39%) may represent additional, highly divergent mitochondrial lineages where point mutations have occurred in the primer regions of the COII fragment, but not at any of the four nuclear loci.

Clade A grouped with the majority of S. setosa haplotypes and appeared intermediate between NE Atlantic and Mediterranean/Black Sea clades (Fig. 3). Nine individuals were found to belong to Clade A, seven from widespread locations in the NE Atlantic and two from the Black Sea. These results indicate that Clade A individuals shared a more recent common ancestor than with the majority of S. setosa individuals (see also [START_REF] Peijnenburg | Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European seas[END_REF]). It may be that Clade A represents remnants of ancestral lineages (before the splitting of major phylogeographic groups) that have not been lost from present populations by the process of lineage sorting (or selection). The fact that Atlantic and Black Sea haplotypes within this clade are separated into two subclades suggests that they have diverged (average maximum-likelihood sequence divergence is 1.60% and net divergence is 0.62%), and are thus unlikely to represent recent dispersal between the NE Atlantic and Black Sea basins (e.g. by ballast water).

Sagitta sp. I and Sagitta sp. II were highly differentiated from S. setosa with average corrected sequence divergences of 14.49% and 25.92%, respectively, as opposed to average within-clade divergences of 0.20 -4.42%. This pattern corresponds to phylogeographic category II (sensu [START_REF] Avise | Intraspecific phylogeography: the mitochondrial DNA bridge between population genetics and systematics[END_REF] in which highly distinct mitochondrial lineages lack geographic localization. These lineages may originate from either unidentified sympatric sibling species or from previously isolated populations that presently interbreed. Such extremely high levels of divergence for a mitochondrial protein-coding gene are generally considered indicative of at least species-level status. For example, divergences of ∼5-25% at mitochondrial protein-coding genes in several marine species were considered as evidence of cryptic species [START_REF] Lee | Global phylogeography of a cryptic copepod species complex and reproductive isolation between genetically proximate 'populations[END_REF][START_REF] Rocha-Olivares | Decoupling of molecular and morphological evolution in deep lineages of a meiobenthic harpacticoid copepod[END_REF][START_REF] Lee | Morphological stasis in the Eurytemora affinis species complex (Copepoda: Temoridae)[END_REF][START_REF] Goetze | Cryptic speciation on the high seas; global phylogenetics of the copepod family Eucalanidae[END_REF][START_REF] Lee | Hidden Floridian biodiversity: mitochondrial and nuclear gene trees reveal four cryptic species within the scorched mussel, Brachidontes exustus, species complex[END_REF]. However, these studies also reported congruent patterns of divergence at nuclear loci confirming that divergent lineages were probably reproductively isolated. We, however, did not find concordant patterns at nuclear loci. Even sequence data of microsatellite alleles did not show any fixed differences between any of the deep mitochondrial clades at the nucleotide level (K. T. C. A. Peijnenburg et al., unpublished results). This suggests that highly divergent mitochondrial lineages may be present in interbreeding populations of S. setosa. In very large populations it would theoretically be possible that anciently separated lineages are retained by chance whereas intermediate haplotypes have been lost by gradual lineage sorting, although examples are lacking [START_REF] Avise | Phylogeography: The History and Formation of Species[END_REF]. Hybridization and introgression of mtDNA across chaetognath species may be another possibility. To test these hypotheses, we need more data of other nuclear markers and a molecular species phylogeny of chaetognaths.

©

Fig. 1

 1 Fig. 1 Sampling locations of Sagitta setosa in the Northeast Atlantic from 1999 and 2000 (A), 2001 (B) and in the Mediterranean and BlackSea from 1999 to 2002 (C). Samples that were pooled per region and year are encircled (see also Table1). Dominating surface currents (grey arrows) and approximate scales are indicated.

  20% of genotyped individuals were re-amplified and scored, a second time to confirm repeatability.

Fig. 2

 2 Fig. 2 Restriction map of a 551-bp fragment of the mitochondrial cytochrome oxidase II region (COII) of Sagitta setosa. Each restriction site is indicated by the enzyme by which it is recognized and the distance in base pairs (in parentheses) from the beginning of the forward primer.

Fig. 3

 3 Fig.3Unrooted neighbour-joining tree of 148 cytochrome oxidase II sequence haplotypes (GenBank accession numbers AY585565-AY585650 and DQ486064 -DQ486126) sampled in morphologically identified samples of Sagitta setosa from the Northeast Atlantic, Mediterranean and Black Sea. Supported groupings are encircled and bootstrap support is indicated on major branches. PCR-RFLP haplotypes (H1-H35, see Table3) present within a clade as well as their geographic origin are indicated (see legend). Two highly divergent clades, denoted Sagitta sp. I (PCR-RFLP haplotype H35) and Sagitta sp. II (PCR-RFLP haplotype H34) may represent different (cryptic) species and were therefore not included in population genetic analyses (see text).

©

  2006 The Authors Journal compilation © 2006 Blackwell Publishing Ltd

Fig. 4

 4 Fig. 4 Multidimensional scaling plots of pairwise multilocus F ST (Weir & Cockerham 1984) based on four microsatellites (of which Sset-3 and Sset-4 were corrected for null alleles, see text) among 39 samples (A) and 17 pooled samples (B) of Sagitta setosa from the Northeast Atlantic, Mediterranean Sea and Black Sea (see legend; seeTable 1 for sample codes).

Table

Table 1

 1 Sample codes, sampling information and number of individuals per sample (n). Sample codes refer to the year of sampling, geographic region, sample number, separate hauls (A or B, if applicable) and juveniles (juv, if applicable). Pooled sample codes are in capital letters and denote the region and year of sampling (for sampling locations see Fig.1)

					Sampling		Pooled
	Sample code	Region	Latitude	Longitude	date	n	sample code
	NE Atlantic
	99skag1A	Skagerrak	58°10.1′N	0 8 °44.6′E	05.09.99	59	SKAG99
	99skag1B	Skagerrak	58°10.1′N	0 8 °44.5′E	05.09.99	49	SKAG99
	99skag3	Skagerrak	57°42.1′N	0 9 °46.0′E	06.09.99	55	SKAG99
	99gb1	German Bight	55°23.4′N	0 7 °09.3′E	08.09.99	41	GB99
	99gb2	German Bight	55°01.4′N	0 6 °24.9′E	08.09.99	48	GB99
	99gb3	German Bight	54°05.9′N	0 8 °13.3′E	09.09.99	41	GB99
	99gb4	German Bight	54°06.0′N	0 6 °58.6′E	09.09.99	47	GB99
	99sb1	Southern Bight	52°59.2′N	0 2 °01.8′E	14.09.99	57	SB99
	99sb2	Southern Bight	53°06.7′N	0 1 °48.0′E	14.09.99	48	SB99
	99sb3	Southern Bight	51°44.4′N	0 1 °54.2′E	15.09.99	50	SB99
	00celt1	Celtic Sea	51°49.9′N	0 5 °46.0′W	29.09.00	48	CELT00
	00atl1	Atlantic inflow	58°41.2′N	0 4 °01.5′W	16.10.00	57	ATL00
	01skag1	Skagerrak	58°00.0′N	0 8 °29.9′E	24.08.01	59	SKAG01
	01skag2	Skagerrak	58°00.0′N	1 0 °24.1′E	25.08.01	57	SKAG01
	01skag2juv	Skagerrak	58°00.0′N	1 0 °24.1′E	25.08.01	25	SKAG01
	01atl1	Atlantic inflow	57°00.0′N	0 0 °29.9′W	27.08.01	50	ATL01
	01gb1	German Bight	56°00.0′N	0 7 °29.9′E	29.08.01	55	GB01
	01gb2	German Bight	55°00.0′N	0 7 °30.0′E	01.09.01	60	GB01
	01gb3	German Bight	54°24.0′N	0 7 °30.0′E	01.09.01	60	GB01
	01gb4	German Bight	53°58.5′N	0 7 °30.0′E	01.09.01	57	GB01
	01sb1A	Southern Bight	53°12.0′N	0 2 °30.0′E	03.09.01	24	SB01
	01sb1B	Southern Bight	53°12.0′N	0 2 °30.0′E	03.09.01	19	SB01
	01sb1Bjuv	Southern Bight	53°12.0′N	0 2 °30.0′E	03.09.01	19	SB01
	01sb2	Southern Bight	52°35.9′N	0 2 °30.1′E	03.09.01	57	SB01
	01sb3	Southern Bight	51°30.0′N	0 1 °59.5′E	04.09.01	59	SB01
	01sb3juv	Southern Bight	51°30.0′N	0 1 °59.5′E	04.09.01	21	SB01
	01chan1	Channel	50°20.0′N	0 0 °30.0′W	04.09.01	50	CHAN01
	Mediterranean Sea
	99lig1	Ligurian Sea	43°30′N	0 7 °12′E	19.08.99	44	LIG99
	99lig2	Ligurian Sea	43°30′N	0 7 °12′E	20.08.99	52	LIG99
	01lig1	Ligurian Sea	43°30′N	0 7 °12′E	22.08.01	35	LIG01
	01lig2	Ligurian Sea	43°30′N	0 7 °12′E	22.08.01	53	LIG01
	02tyr1	Tyrrhenian Sea	40°36′N	1 4 °30′E	22.08.02	41	TYR02
	02tyr2	Tyrrhenian Sea	40°36′N	1 4 °30′E	22.08.02	26	TYR02
	02gab1	Gulf of Gabès	33°42′N	1 0 °24′E	19.06.02	69	GAB02
	00adr1	Adriatic Sea	45°24′N	1 3 °30′E	19.07.00	62	ADR00
	00adr2	Adriatic Sea	42°18′N	1 8 °42′E	12.07.00	18	ADR00
	Black Sea
	00black1	Black Sea	44°32′N	3 6 °37′E	14.04.00	20	BLACK00
	00black2	Black Sea	43°22′N	3 8 °24′E	17.04.00	17	BLACK00
	02black1	Black Sea	44°30′N	3 3 °30′E	10.08.02	30	BLACK02

Table 2

 2 Characteristics of four microsatellite loci developed for Sagitta setosa including primer sequences, PCR annealing temperatures (T a ), allele sizes, number of alleles (N a ), repeat motifs and GenBank accession numbers of the original sequenced clones. Allelic information is based on a screening of 1724 individuals (see text). Labelled primers are indicated by †IRD700 or ‡IRD800

	Locus	Primer sequences (5′-3′)	T a (°C)	Sizes (bp)	N a	Repeat motif	GenBank
	Sset-1	F: GGGCAAGGACGAGAAGTAAA ‡	5 8	8 5 -121	18	(AAC) 4	DQ463218
		R: TCGGTCCTAATGATAAGAATGG
	Sset-2	F: CATGTCGGTACATCGCTGTTT †	5 8	1 0 9 -136	10	(AGG) 7	DQ463219
		R: GTGCTTCGGTTTCGCCTACT
	Sset-3	F: CACTTTAGTGACACTCGTCTTGC †	5 7	8 4 -279	54	(CAA) 25	DQ463220
		R: GTTGGCGAAACCGGATGT
	Sset-4	F: TGCGTCTCTGTCTCAGAATCC †	5 8	1 1 8 -434	89	(CTAT) 19	DQ463221
		R: GGACAAATTGATGGGCAAAG

Table 3 Continued

 3

				Northeast Atlantic								Mediterranean Sea			Black Sea
	Hap.	Bst		SKAG	GB	SB	CELT	ATL	SKAG	ATL	GB	SB	CHAN	LIG	LIG	TYR	GAB	ADR	BLACK	BLACK
	no.	EII	ItaI HapII	99	99	99	00	00	01	01	01	01	01	99	01	02	02	00	00	02
	H31	10	011 3.7	4.0	1.3	4.2	5.3	4.3	-	3.0	1.5	8.0	-	-	-	-	-	-	6.7
	H32	10	001 0.6	0.6	-	-	3.5	-	2.0	-	0.5	-	4.2	1.1	-	1.4	1.3	-	-
	H33	10	001 -	-	0.6	-	-	-	-	-	-	-	-	2 . 3	1.5	1.4	1.3	-	-
	H34	00	101 0.6	-	-	-	1 . 8	-	-	0.9	-	-	-	-	-	-	-	-	-
	H35	00	101 -	-	-	-	-	-	-	-	-	-	-	-	-	-	-	18.9	13.3
	Excl. H34 and H35:	n	162	177	155	48	56	141	50	230	199	50	96	88	67	69	80	30	26
			h	0.35	0.33 0.25 0.37	0.32	0.27	0.26	0.23 0.23 0.32	0.56	0.48	0.46	0.48	0.52	0.30	0.50
			π	0.76	0.77 0.56 0.75	0.83	0.61	0.58	0.48 0.50 0.63	1.61	1.48	1.19	1.27	1.40	0.65	1.59

n, number of individuals screened; h, haplotype diversity; π, nucleotide diversity in percentages. For sample codes see Table

1

.

samples (Table

Table 3

 3

	) present

Table 4

 4 Levels of genetic variability at four microsatellite loci for 17 (pooled) samples of Sagitta setosa. n, sample size; N a , number of alleles; H E , expected heterozygosity; H O , observed heterozygosity; and r, null allele frequency as estimated by[START_REF] Brookfield | A simple new method for estimating null allele frequency from heterozygote deficiency[END_REF] estimator 2 using micro-checker. Significant deviations from Hardy-Weinberg equilibrium are in bold (null allele frequencies are only listed if a significant deviation was observed)

	NE Atlantic

Table 5

 5 Estimates of genetic differentiation (F ST ) between samples of Sagitta setosa (sample codes as in Table1). Above diagonal: F ST incorporating genetic distances between haplotypes from RFLP analysis of the mitochondrial COII region. Below diagonal: multilocus F ST(Weir & Cockerham 1984) based on variation at four microsatellite loci (loci Sset-3 and Sset-4 were corrected for null alleles; see text). Significant deviations from genetic homogeneity (exact test: below diagonal; permutation test: above diagonal) are shown in italics when P < 0.01, and in bold type when remaining significant after sequential Bonferroni correction for multiple tests

							NE Atlantic			Mediterranean Sea	Black Sea
	n		SKAG99 GB99	SB99	CELT00 ATL00 SKAG01 ATL01 GB01	SB01	CHAN01 LIG99 LIG01 TYR02 GAB02 ADR00 BLACK00 BLACK02
	162 SKAG99		-0.0018 0.0046 -0.0096	0.0171 -0.0033 0.0028	0.0036 0.0015 0.0003	0.6476 0.6674 0.6970 0.6988 0.7829	0.7185	0.6646
	177 GB99	0.0006		0.0072 -0.0114	0.0120 -0.0031 0.0121	0.0038 0.0069 0.0001	0.6431 0.6623 0.6910 0.6930 0.7795	0.7111	0.6565
	155 SB99	-0.0009 -0.0015		0.0066	0.0251 0.0052 0.0018	0.0005 0.0068 0.0007	0.6722 0.6940 0.7288 0.7282 0.7999	0.7688	0.7170
	48	CELT00	-0.0010 -0.0012 -0.0014		0.0134 -0.0082 0.0108	0.0021 0.0014 0.0001	0.5952 0.6210 0.6671 0.6646 0.7507	0.7248	0.6164
	56	ATL00	-0.0030 -0.0022 -0.0019 -0.0020	0.0121 0.0150	0.0219 0.0289 0.0043	0.5541 0.5801 0.6225 0.6214 0.7247	0.6753	0.5767
	141 SKAG01 -0.0011 -0.0018 -0.0025 -0.0025 -0.0022	0.0034 -0.0016 -0.0008 -0.0005	0.6538 0.6761 0.7123 0.7128 0.7937	0.7476	0.6876
	50	ATL01	0.0090	0.0047 0.0043 0.0058	0.0043 0.0043		0.0052 -0.0039 0.0112	0.5983 0.6260 0.6740 0.6707 0.7562	0.7475	0.6483
	230 GB01	0.0007	0.0021 0.0008 0.0000 -0.0004 0.0006 0.0119		0.0019 -0.0009	0.7061 0.7261 0.7572 0.7579 0.8272	0.7886	0.7461
	199 SB01	-0.0004	0.0011 -0.0013 -0.0015 -0.0009 -0.0008 0.0094 -0.0002	0.0154	0.6928 0.7140 0.7475 0.7484 0.8218	0.7798	0.7334
	50	CHAN01 -0.0009	0.0006 -0.0001 -0.0034 -0.0025 -0.0016 0.0037	0.0001 -0.0005	0.6074 0.6339 0.6807 0.6767 0.7552	0.7524	0.6501
	96	LIG99	0.0048	0.0137 0.0085 0.0062	0.0058 0.0086 0.0250	0.0056 0.0059 0.0081	-0.0106 -0.0063 0.0031 0.3450	0.0685	0.0741
	88	LIG01	0.0111	0.

0223 0.0180 0.0135 0.0115 0.0178 0.0372 0.0139 0.0128 0.0169

							-0.0004	-0.0076 0.0003 0.3494	0.0838	0.0852
	67	TYR02	0.0073	0.0131 0.0086 0.0066	0.0047 0.0106 0.0272	0.0074 0.0070 0.0114	-0.0015 -0.0006	-0.0083 0.3610	0.1100	0.1368
	69	GAB02	0.0077	0.

0138 0.0093 0.0094 0.0044 0.0110 0.0273 0.0116 0.0092 0.0108

				0.0005 0.0037 -0.0002	0.3069	0.1574	0.1655
	80	ADR00	0.0089	0.

0177 0.0139 0.0103 0.0093 0.0139 0.0311 0.0087 0.0089 0.0125

						-0.0018 -0.0022 -0.0032 0.0041	0.5569	0.5160
	30	BLACK00 0.0146	0.0119 0.0155 0.0153	0.0069 0.0149 0.0291	0.0141 0.0191 0.0152	0.0223 0.0249 0.0150 0.0214 0.0251		0.0880
	26	BLACK02 0.0141	0.0110 0.0154 0.0105	0.0070 0.0122 0.0287	0.0106 0.0165 0.0131	0.0154 0.0181 0.0107 0.0141 0.0172 -0.0056
	© 2006 The Authors
	Journal compilation © 2006 Blackwell Publishing Ltd

  Table 1 for sample codes).

  2006 The Authors Journal compilation © 2006 Blackwell Publishing Ltd Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution, 38, 1358 -1370. Zane L, Ostellari L, Maccatrozzo L et al. (1998) Molecular evidence for genetic subdivision of Antarctic krill (Euphausia superba Dana) populations. Proceedings of the Royal Society of London. Series B, Biological Sciences, 265, 2387-2391. Zane L, Ostellari L, Maccatrozzo L et al. (2000) Genetic differentiation in a pelagic crustacean (Meganyctiphanes norvegica: Euhausiacea) from the North East Atlantic and the Mediterranean Sea. Marine Biology, 136, 191-199. Zenkevitch L (1963) Biology of the Seas of the U.S.S.R. George Allen and Unwin, London. This paper was part of the PhD work of Katja Peijnenburg, which aimed at unravelling spatial and temporal scales of marine plankton populations. Katja Peijnenburg worked in the Evolutionary Biology group at the University of Amsterdam and was supervised by Hans Breeuwer and Steph Menken. Cécile Fauvelot shares an interest in genetic population structures of marine organisms and contributed to this work during her postdoctoral position in Amsterdam. Katja Peijnenburg is currently working as a Postdoctoral Researcher at University College Dublin and pursues interests in stock structure and local adaptation of common whelk (Buccinum undatum).

© 2006 The Authors Journal compilation © 2006 Blackwell Publishing Ltd

© 2006 The Authors

Acknowledgements

We would like to thank the many people who helped us to obtain samples from various areas: M. Tuerkay, J. Fives, G. Duineveld, C. ten Hallers-Tjabes, H. van Aken, H. Thomas, all crew of Research Vessel 'Pelagia', S. Dallot, E. Ghirardelli, E. Vinzi, N. Bajkoviç, M. Flint, V. Velikova, V. Mihneva, H. Harrath, M.-G. Mazzocchi, S. Piontkovsky, A. Pierrot-Bults, H. Pierrot, R. ter Hofstede, J.-H. Schotveld and E. van Haastrecht. We also especially thank M. Flint (P. P. Shirshov Institute of Oceanology, Moscow, Russia) and S. Piontkovsky (Institute of Biology of the Southern Seas, Sevastopol, Ukraine) for sending us the Black Sea samples from 2000 and 2002, respectively. We also acknowledge B. Voetdijk, W. van Ginkel, P. Kuperus and S. van Heiningen for laboratory assistance, Westburg for technical advice about LI-COR systems, J. van Arkel for producing Figs 1, 2 and 3, M. van Ballegooijen, G. Hoarau, J. Olsen, W. Stam, M. Canino, C. van Oosterhout and many people at the Institute for Biodiversity and Ecosystem Dynamics of the University of Amsterdam for advice and discussion. Comments on the manuscript from two anonymous referees, P. Luttikhuizen and S. Mariani were also greatly appreciated. This work was part of the priority program 'Sustainable use and conservation of marine living resources' and was funded by the Netherlands Organization for Scientific Research (NWO project 885.100.02).


Josina Tiavouane

Théa Jacob

Paul Pascal

Dumas

Cécile Fauvelot
email: cecile.fauvelot@ird.fr

Isolation and characterization of fifteen microsatellite loci for the giant clam Hippopus hippopus (family Tridacnidae)

Keywords: Hippopus hippopus, Tridacnidae, microsatellite loci, Marine Protected Area, paternity analyses, New Caledonia

Fifteen polymorphic microsatellite markers were developed for Hippopus hippopus in order to assess the effectiveness of population replenishment within MPAs in New Caledonia. Number of alleles varied from 2 to 11 per locus, observed and expected heterozygosities ranged from 0.300 to 0.866 and 0.495 to 0.858 respectively. Significant deviations from HWE were detected in two loci. Cross-amplifications were tested in four other species of Tridacnidae.

event of Hippopus hippopus in September 2009 within the co-managed Marine Protected Areas (MPAs) of the North-East Lagoon: Hyabé/LeJao (Pouébo) and Yeega (Hienghène). Microsatellite loci were developed to provide genetic tools for assessing the effectiveness of this replenishment and estimating spatial scales of larvae dispersal of this species from these MPAs.

Approximately 20 ng of genomic DNA was isolated from muscle tissue of one individual conserved in 80% EtOH. Size-selected fragments from genomic DNA were enriched for SSR content by using magnetic streptavidin beads and biotin-labeled CT and GT repeat oligonucleotides. The SSR-enriched library was analyzed on a Roche 454 platform using the GS FLX Titanium reagents. From the 28'526 reads, 1'689 contained a microsatellite insert with a tetra-or a trinucleotide of at least 6 repeat units or a dinucleotide of at least 10 repeat units. Suitable primer design was possible in 4141 reads and 72 were tested for polymorphism on 8 individuals. Genomic DNA was isolated from mantel biopsies using DNeasy Blood and Tissue Kit (Qiagen).

PCR reactions were performed using Type-It Microsatellite (Qiagen) in two distinct multiplexes of 5µl final volume containing 1X Master Mix, 0.5X of Q-solution, 0.1µM of each primer (fluorescent-labeled forward primer 6-FAM, PET, NED or VIC) and 50 to 150ng of DNA template (Table 1). PCRs were conducted in GeneAMP PCR System 9700 (Applied Biosystems) with 5min at 94°C, 28 cycles at 95°C for 30s, 57°C for 90s and 72°C for 30s, and a final step at 60°C for 30min. Fluorescent PCR fragments were visualized on an ABI 3130XL Genetic Analyser (Applied Biosystems) with GS-500-LIZ (Applied Biosystems). Alleles were sized using the program GeneMapper® (Applied Biosystems).

Using GENETIX [START_REF] Belkhir | GENETIX 4.02, Logiciel sous Windows TM pour la génétique des populations. Laboratoire Génome, Populations, Interactions[END_REF], between 2 and 11 alleles (mean = 7.54) were observed per locus among 30 individuals from Hyabé/LéJao (Pouébo) MPA, with expected heterozygosity values ranging from 0.495 to 0.858 (Table 1). No significant genotypic linkage disequilibrium among loci was found using GENEPOP [START_REF] Raymond | GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism[END_REF]. Significant deviations from Hardy-Weinberg equilibrium were observed at Hiphip_13326 and Hiphip_14220 after Bonferroni corrections. Null alleles were suggested at these two loci by MICRO-CHECKER (Van Oosterhout et al. 2004) and confirmed by the presence of null homozygotes.

Cross-priming was tested on 8 individuals of Tridacna crocea (from New Caledonia), T. maxima (from La Réunion, Juan de Nova and Glorieuses, Indian Ocean), T. squamosa (from Juan de Nova and Glorieuses) and T. derasa (from New Caledonia). Using the same PCR conditions as above, but an annealing temperature of 52°C, the test resulted in 12 loci amplifying in T. crocea; 5 loci amplifying in T. maxima; 2 loci amplifying in T. squamosa; and 8 loci amplifying in T.derasa (Table 2).

The 15 new microsatellite loci will be helpful tools for connectivity studies on H. hippopus populations and to investigate genetic stocks for fisheries management of this endangered species.

Acknowledgements

This work was funded by World Wildlife Fund France, within the framework of the project entitled "Connectivity of Hippopus hippopus populations in the North-Eastern lagoon of New Caledonia" with the support of Northern Province of New Caledonia. Thanks to Pouébo and Hienghène associations of co-managed

Alerted by recent stock declines in New Caledonia, in agreement and following local communities wish, World Wildlife Fund with the support of the Department of Fisheries and Aquaculture


E E V A N L O O N

D F R C L E A R Y

C F A U V E L O T

CBPG/IBED E E Van Loon
email: vanloon@uva.nl

ARES : software to compare allelic richness between uneven samples

Keywords: allelic richness, extrapolation, rarefaction, uneven sample size

Allelic richness is one of the most basic measures of genetic diversity. Its calculation is, however, still problematic because estimates depend on sample size. This paper describes an r library that calculates mean allelic richness with confidence bounds for a range of sample sizes. It takes a file in GENEPOP format as input, or alternatively a binary data matrix with columns representing different individuals and rows representing different alleles. The output is tabular as well as graphical. Unlike existing tools, ARES does extrapolate beyond the sample size and provides confidence bounds for these predictions.

The preservation of genetic diversity within populations appears to be critical to species survival under changing environmental conditions [START_REF] Allendorf | Genetic drift and the loss of alleles versus heterozygosity[END_REF][START_REF] Pease | A model of population growth, dispersal and evolution in a changing environment[END_REF][START_REF] Petit | Identifying populations for conservation on the basis of genetic markers[END_REF][START_REF] Saccheri | Inbreeding and extinction in a butterfly metapopulation[END_REF][START_REF] Spielman | Most species are not driven to extinction before genetic factors impact them[END_REF][START_REF] Reusch | Ecosystem recovery after climatic extremes enhanced by genotypic diversity[END_REF][START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF]. Allelic richness (the number of alleles) is increasingly used as a means to estimate levels of genetic diversity in evolutionary and conservation biology, in order to identify endangered breeding units, populations or species. The primary disadvantage of using allelic richness for this purpose is that its value is highly dependent on sample size, making comparisons between samples of different sizes difficult (e.g. [START_REF] Stout | Comparison of species richness for stream-inhabiting insects in tropical and midlatitude streams[END_REF]. One way to avoid this problem is to use rarefaction [START_REF] El Mousadik | High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L.) Skeels] endemic of Morocco[END_REF][START_REF] Kalinowski | Counting alleles with rarefaction: private alleles and hierarchical sampling design[END_REF]). With rarefaction, the sample size at which allelic richness among samples is compared is that of the smallest sample. This, however, can lead to relatively inaccurate results and leaves valuable information unused [START_REF] Gotelli | Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness[END_REF]). In the context of species richness estimation, it has been suggested to extrapolate small samples rather than rarefying larger samples in order to avoid this problem [START_REF] Colwell | Estimating terrestrial biodiversity through extrapolation[END_REF][START_REF] Melo | Comparing species richness among assemblages using sample units: why not use extrapolation methods to standardize different sample sizes?[END_REF]. Nevertheless, present user-friendly software for estimating allelic richness that extrapolates and provides confidence intervals around estimates is lacking. This is an important hiatus because obtaining reliable estimates of allelic richness may be both time-consuming and expensive (especially at large scales and for rare species), and it may not always be feasible to provide well-replicated samples that can be used for subsequent hypothesis testing with, for example, t -tests or anova s. Moreover, confidence intervals are more informative than the simple result of hypothesis tests (reject or do not reject H 0 ) since they provide a range of plausible values for the unknown parameter.

Recently, [START_REF] Colwell | Interpolating, extrapolating and comparing incidence-based species accumulation curves[END_REF] and [START_REF] Mao | Estimating the species accumulation curve using mixtures[END_REF] have introduced a new model to extrapolate an incidence-based accumulation curve: a combination of a moment-based richness estimator for calculating richness up to the number of individuals present in the sample and a truncated binomial mixture model for extrapolating beyond that. Using this model, allelic richness estimates can be obtained with confidence bounds for any sample size, and tests can be confidently performed to investigate whether two samples of differing size differ with respect to allelic richness. We call our implementation of the aforementioned model ares (Allelic Richness EStimation), a model that closely follows the notation and definitions given by [START_REF] Colwell | Interpolating, extrapolating and comparing incidence-based species accumulation curves[END_REF]. Input for ares core functions is a binary data matrix with the individuals columnwise, and the alleles row-wise. Briefly, the data for h individuals are expressed as an S -byh allele-individual incidence matrix consisting of the presence indicators Z ij , which is 1 if the i th allele is present in the j th individual; and 0 if the i th allele is absent in the j th individual. The model is based on two statistical assumptions: (i) an allele has the same probability of being present in each individual ( φ i ), and (ii) the Z ij are independent, given the probability of encountering allele i , over all i and j .

The allelic accumulation function, which gives the expected number of alleles observed in h individuals, is the sum of the probabilities, across the alleles, that each allele is not absent from all h individuals:

(1) where φ i is the probability that an allele is present in an individual i . Alleles with identical presence probabilities can be grouped. For G of these groups eqn 1 becomes

(2)
where ψ k is the presence probability of the k th group of alleles, and π k is the number of individuals in the k th group divided by the total number of individuals.

In any realistic situation, one takes a random sample of H individuals from the population (hence an S -by-H allele-individual incidence matrix). The observed data matrix consists of alleles that have at least one Z ij > 0. Let s j be defined as the number of alleles found in exactly j individuals: s 0 is the number of alleles present in the population but not in the observed sample, s 1 is the number of alleles found in only one individual, etc. s j are called empirical counts. The observed allelic richness in the sample set is therefore (3) and the total number of alleles in the population is (4)

We use the following form of the Chao 2 richness estimator [START_REF] Chao | Estimating population size for sparse data in capture-recapture experiments[END_REF]) to estimate S (5) where s 1 is the number of alleles that occur in a single individual and s 2 is the number of alleles that occur in exactly two individuals.

Assuming that H individuals have been observed, the expected number of alleles for a sample of size h (i.e. % ( h ) for h < H ) is given by ( 6)

The variance of this estimator is given by ( 7) Eqns 6 and 7 allow calculating approximately 95% confidence intervals for τ ( h ) by using % ( h ) ± 1.96 # ( h ).

To estimate the number of alleles within samples of the population, larger than the observed sample (i.e. % ( h ) for h > H ), a formulation based on eqn 2 is used

(8)
where ψ k is the presence probability of the k th group, and π k is the number of individuals in the k th group. In eqn 8, the parameters ψ k , π k and G need to be estimated. This is achieved by maximizing the log conditional likelihood of the observed empirical counts s 1 , s 2 , ... , s H (given the observed allelic richness S obs ), using an expectation maximization algorithm [START_REF] Mao | Estimating the species accumulation curve using mixtures[END_REF]. Confidence intervals for τ ( h ) are estimated by taking bootstrap resamples (typically between 100 and 1000) from the likelihood of the empirical counts ( s 1 , s 2 , ... , s H ) given a random S obs generated as a binomial variable with size % ( ∞ ) and probability (9) This algorithm allows computing reliable allelic richness estimates for sample sizes ranging from one up to any number of individuals [START_REF] Colwell | Interpolating, extrapolating and comparing incidence-based species accumulation curves[END_REF]. The software provides estimates of mean allelic richness, along with confidence bounds in order to test whether two populations differ with respect to allelic richness.

Two examples of uses for ares are illustrated in this paper: a spatial and a temporal comparison of allelic richness in different populations. Our data consist of microsatellite data from the tropical butterfly Drupadia theda (Felder), a rainforest lycaenid species typical of pristine and moderately disturbed forest. The data were originally collected to investigate the recently introduced speciesgenetic diversity correlation [START_REF] Vellend | Connections between species diversity and genetic diversity[END_REF] in disturbed landscapes. Butterflies were collected in four

τ φ ( ) ( ( ) ) h i h i S = -- = ∑ 1 1 1 τ π ψ ( ) ( ( ) ) h S k k h k G = -- = ∑ 1 1 1 S s obs j j H = = ∑ 1 S s S obs = + 0 % ( ) = - + H s Hs S obs 1 2 1 2 2 %( ) , , , ( )!( )! ( )! ! ( ) ( )

h S s h H H h H j H h j H j h H j h H obs jh j j

H jh = - = = - - -- + ≤ + > ⎧ ⎨ ⎪ ⎩ ⎪ = ∑ α α 1 1 2 0 with for for K # % 2 2 1 2 1 ( ) ( ) ( ) h s h jh j j H = - - = ∑ α % %( ) ( ) ( ) ( ) ( ( ) ) ( ( ) )
, , ,

h S S k G obs obs k k G k H k h k H k k k H m m G m H = + - - -- = -- -- = = = ∑ ∑ ω ψ ψ ψ ω π ψ π ψ 1 1 1 1 1 1 1 1 1 1 1 2 with K p k k G k H k H ( ) ( ) ( ) ∞ = + - -- ⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟ = - ∑ 2 1 1 1 1 1 ω ψ ψ © 2007
The Authors Journal compilation © 2007 Blackwell Publishing Ltd differentially disturbed landscapes (I1, I2, B1 and B2) in East Kalimantan, Indonesia Borneo. Also a time series of samples were taken in one landscape (B2) before and after the 1997-1998 El Niño Southern Oscillation (ENSO) event.

The species was sufficiently abundant in all environments to enable reliable sample size for robust estimates of allelic richness. However, sample sizes differed among landscapes (sample sizes were much lower in burned than in unburned forests). A more elaborate description of landscapes and data is available in [START_REF] Cleary | Parallel responses of species and genetic diversity to El Niño Southern Oscillation-induced environmental destruction[END_REF]. An analysis of allelic richness with ares yielded the results shown in Figs 1 and2. Figure 1 describes the allelic richness of the four sites as observed in the year 2000. The four graphs at the right show the mean allelic richness curves with 95% confidence bounds shaded. In these graphs, a vertical dotted line denotes the sample size. andI2 (B1 andB2) is rejected with a 0.05 confidence level. (c-f) mean allelic richness curves with 95% confidence bounds as shaded area. Vertical dotted line denotes the sample size (for I2 and B1 at 25 and 41 individuals, respectively, for I1 and B2 the samples were larger than 60 individuals).

To the left of these vertical lines the mean and 95% confidence bounds are calculated analytically and to the right of the line they are calculated with a bootstrap analysis. The mean and 95% confidence bounds at the left of these vertical lines are calculated analytically (namely eqns 6 and 7) and at the right of the lines, they are calculated with a bootstrap analysis (eqns 8 and 9). The divergences between confidence bounds are a direct consequence of the change from interpolation to extrapolation. Mean allelic richness accumulation curves at the four sites are shown in a single graph (upper left graph) to facilitate comparison. Here it can be seen that allelic richness was highest in populations sampled from areas in the large unburned isolate (I1) and once-burned forest (B1). For the comparison of I1 and I2, it can furthermore be seen that small sample sizes can lead to unreliable results, since for sample sizes of less than 18 individuals, allelic richness in I2 (small unburned isolate) is higher than in I1. The graph at the lower left shows, for a given sample size, the result of a one-sided t-test comparing allelic richness between I1 and I2, and between B1 and B2. The circles show the minimum sample size for which one can conclude that allelic richness of I1 > I2 and B1 > B2 (using a P level of 0.05 for rejecting H 0 ).

Figure 2 shows an analysis similar to that presented in Fig. 1. Here, the allelic richness is compared among years for site B2 (the twice-burned forest). Importantly, allelic richness was significantly higher pre-ENSO (1997) than post-ENSO (1999, 2000 and 2004). Moreover, the allelic richness seems to increase over time since burning (namely the increasing curves for 1999, 2000 and 2004. ares is available at CRAN (http://cran.r-project.org/). The archive contains the source code in r (www.r-project.org/), a brief text describing how the software can be used and the data that were used in this study.

Fig. 1

 1 Fig.1Allelic richness at four sites sampled in the year 2000: a large unburned isolate (I1), a small unburned isolate (I2), onceburned forest (B1) and twice-burned forest (B2). (a) Comparison of mean allelic richness among all four landscapes. (b) P values when testing with a t-test the differences in mean allelic richness between I1 and I2 as well as B1 and B2; the circles indicate the minimum sample size where the the null hypothesis of equal allelic richness for I1 and I2 (B1 and B2) is rejected with a 0.05 confidence level. (c-f) mean allelic richness curves with 95% confidence bounds as shaded area. Vertical dotted line denotes the sample size (for I2 and B1 at 25 and 41 individuals, respectively, for I1 and B2 the samples were larger than 60 individuals).To the left of these vertical lines the mean and 95% confidence bounds are calculated analytically and to the right of the line they are calculated with a bootstrap analysis.

Fig. 2

 2 Fig.2Allelic richness at B2 (a twiceburned forest) in the years1997, 1999, 2000 and 2004. For an explanation of the various graphs, see Fig.1.

Acknowledgements

Chang Xuan Mao is kindly acknowledged for providing help in applying his algorithm. This study was conducted within the Virtual Laboratory for e-Science project (http://www.vl-e.nl), supported by a BSIK grant from the Dutch ministry of education, culture and science and the ICT innovation program of the ministry of economic affairs.