SEARCH

SEARCH BY CITATION

Abstract

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. References
  8. Appendix

Genetic variation in nine wild brown trout (Salmo trutta L.) populations was studied by means of allozyme and microsatellite markers. All brown trout populations were clearly separated into two clusters that represented the Sil and Duero basins. Although both markers revealed a strong genetic differentiation between basins, microsatellite loci resulted much more accurate when population structure at the intrabasin level was analysed. Also pairwise multilocus FST estimates and assignment tests of individual fish to the set of sampled populations demonstrated a much higher efficiency of microsatellites compared to allozymes. The analysis of both markers provides new insights in defining the conservation units at this local area and confirms the existence of a recognized sub-lineage in the Duero basin. The management implications of these findings are discussed and changes in trout release activity are recommended to avoid mixing of trout gene pools mainly in the Sil basin.

Conservation of biological diversity in species of ecological and economical interest has been, for the last decades, one of the main challenges to overcome for many countries which have invested an extensive effort directed towards these species. This becomes critical both in brown trout (Salmo trutta L.) and other salmonid species which have lost large parts of their intraspecific variability due to environmental degradation, harvest and stocking.

In order to halt and reverse this negative trend, different organizations have started supportive programs. However in most of them, the origin and genetic composition of both the added specimen and the natural populations have not been taken into account. Many studies based on the analysis of genetic markers have shown the risk of such practices on natural populations, causing the loss of intraspecific diversity, due to hybridization and/or introgression among autochthonous (individuals from a specific area that share some genic features) and allochthonous (foreign individuals) specimen (Hansen et al. 1993; Skaala et al. 1996; Blanco et al. 1998, García-Marín et al. 1998; Cagigas et al. 1999a; Poteaux et al. 1999). Managers must recognize the biological reality of existing genetic diversity and devise appropriate management strategies on the basis of conserving genetic variability effectively (Ryman 1991). Therefore, as a previous step to any release of fishes, managers should take into account the genetic status of the species in general and the populations that will be manipulated in particular.

The existence of a hierarchical populational genetic structure in brown trout has been confirmed (Ryman 1983), although the number of levels within this hierarchy and interactions among different lineages or groups still remain unsolved to a great extent (García-Marín et al. 1999; Machordom et al. 2000; Sanz et al. 2000). The influence of glaciarism in the current patterns of gene diversity has been largely recognized in brown trout (Laikre et al. 1999). Also the existence of a high level of genetic diversity has been shown among the Spanish populations, exhibiting in many cases, a close link between the genetic differentiation and the hydrographical pattern. Thus, in the Iberian Peninsula, at least two clearly distinguished lineages can be identified: populations from the Mediterranean drainage (lineage IV) and those from the Cantabric drainage (lineage II) (García-Marín and Pla 1996; Cagigas et al. 1999b; García-Marín et al. 1999). More recent studies based on the analysis of mitochondrial DNA have confirmed this dichotomy reported previously by enzymatic loci (Machordom et al. 2000; Bernatchez 2001). This high differentiation at a macrogeographic level is also kept at a microgeographic level (Estoup et al. 1998; Sanz et al. 2000; Bouza et al. 2001; Cagigas et al. 2002), and it seems to be connected with the genetic drift and the scarce gene flow that is established among natural populations.

During the past few decades the development of new and highly variable genetic markers, such as microsatellite DNA, has led to new research opportunities that were not possible using allozymes only. The higher level of microsatellite variability associated with its neutral character has turned them into the favourite technique for researchers when studying the genetic population structure of brown trout (Estoup et al. 1993; Cagigas et al. 1999b; Bernatchez 2001; Fritzner et al. 2001). Most of the surveys done with such markers have been focused on the evaluation of restocking on natural populations or the genetic characterization of brown trout populations at a macrogeographical level (Hansen et al. 2000; Fritzner et al. 2001; Ruzzante et al. 2001).

In this work, we studied a microgeographic region in the Province of Leon, where two hydrographic basins are located: the Sil and Duero basins. Both basins are managed by the Servicio Territorial de Medio Ambiente y Ordenación del Territorio de León and, for the last decades, have been subject to restocking programs, using the same hatchery stock for the whole Province. Previous surveys, based on the analysis of allozymic loci and mitochondrial DNA, have proved the existence of genetic differences among natural populations of brown trout in both basins (García-Marín and Pla 1996; Corujo 1999; Machordom et al. 2000; Sanz et al. 2000; Bouza et al. 2001). Nonetheless, the low level of genetic variability shown by allozymic loci makes difficult the analysis of the populational structure in this microgeographical region.

The main aim of this work was the genetic characterization of wild brown trout populations in the Province of León; for this purpose, both enzymatic and microsatellite loci were used. The high variability that the latter show could be useful as a tool in order to define different management units from a conservationist point of view.

MATERIAL AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. References
  8. Appendix

Samples

From 1993 to 2004, more than 40 populations from Duero and Sil basins were sampled. Domesticated trout from hatchery strains have been stocked into rivers for the last decades. In order to locate the autochthonous populations from both basins, we selected 8 populations without alleles previously reported as being exclusive to the above mentioned hatchery strains (LDH-C*90, MDH-A2*200 and G3PDH-2*50: Martínez et al. 1993; Blanco et al. 1998; Cagigas et al. 1999a). Five samples were from Duero basin and three samples from Sil basin. From Lérez river, a waterway discharging into the Atlantic Ocean near the Sil basin, another population was analyzed to use it as an external control.

From these 9 samples, 214 brown trout were sampled by electrofishing over not less than 350 m of the river from 1994 to 2001. Geographical locations of the 9 brown trout populations assayed in this study are represented in Fig. 1.

image

Figure 1. Geographical locations of the 9 samples of brown trout assayed in this study.

Download figure to PowerPoint

Enzyme electrophoresis

Liver, muscle and eye tissue were subjected to horizontal starch (11%, Sigma, St Louis, MO, USA) gel electrophoresis followed by specific enzyme staining. The following 12 enzymes, encoded by 25 loci, were resolved (enzyme name and Enzyme Commission number in parentheses): ADH* (alcohol dehydrogenase, 1.1.1.1), sAAT-1*, -2*, -4* (aspartate amino transferase; 2.6.1.1), CK-A1*, -A2* (creatine kinase, 2.7.3.2), GPI-A1*, -A2*, -B* (glucose 6-phosphate isomerase, 5.3.1.9), G3PDH-2* (glycerol-3-phosphate dehydrogenase 1.1.1.8), sIDHP-1*, -2*, -3*, -4* (isocitrate dehydrogenase, 1.1.1.42), LDH-C* (L-lactate dehydrogenase, 1.1.1.27), sMDH-A1*, -A2*, -B1*, -B2* (malate dehydrogenase, 1.1.1.37), MEP-1*, -2*, -3* (malic enzyme-NADP, 1.1.1.40), PGM-2* (phosphoglucomutase, 5.4.2.2), PGDH-2* (6-phosphogluconate dehydrognase, 1.1.1.44), and SOD-2* (superoxide dismutase, 1.15.1.1).

Recipes for staining solutions were derived from Guyomard and Krieg (1983) and Aebersold et al. (1987). Nomenclature for locus and allele designations followed Guyomard and Krieg (1983) and Shaklee et al. (1990). The alleles occurring at each locus were identified according to the relative electrophoretic mobility system, the most frequent allele being *100. In this work, the alternate allele at each allozyme locus was considered as a “variant”.

Microsatellite analysis

DNA was extracted from muscle tissue following the chelex resin/proteinase K procedure (Walsh et al. 1991) and stored at 4°C until PCR amplifications. Six microsatellite loci were analysed: Str-60* and Str-15* (Estoup et al. 1993), Str-543* (Presa and Guyomard 1996), SsoSL 311* (Slettan et al. 1995), BFRO 002* (Sušnik et al. 1997) and OKI 10* (Smith et al. 1998). PCR reactions (30 μl) contained approximately 20 ng of total DNA, 1 unit of Ecogen Taq polymerase, 60 ng of each unlabelled primer, 1, 5 mM MgCl2, 200 μM of each dNTP and 10×buffer. PCR aliquots were electrophoresed on 6% denaturing polyacrylamide sequencing gels and visualized by silver staining (Promega Silver Sequence DNA Staining). Genotypes were scored by using known sequences of Pgem-3Zf+ vector (Promega Sequencing kit).

Statistical analysis for enzyme and microsatellite loci

For both loci, allele frequencies were estimated by direct counts from observed genotypic frequencies. Variability observed at the isoloci sMDH-B1*, -B2* and sAAT-1*, -2* was first allocated to sMDH-B1* and sAAT-1* and then to sMDH-B2* and sAAT-2* respectively. Departures from expected Hardy-Weinberg proportions were tested using either the exact test described by Louis and Dempster (1987) when four alleles or fewer per locus were found (enzyme loci), or the exact test described by Guo and Thompson (1992) when more than four alleles per locus were found (microsatellite loci). Levels of genetic variation were based on observed heterozygosity (Ho), expected unbiased heterozygosity (He), percentage of polymorphic loci (P0.95), and mean number of alleles per locus (Na). These analyses were performed using the BIOSYS-2 program (Swofford and Selander 1989).

Allele frequency differences among samples or between pair of samples were analyzed by Fisher's exact test using the GENEPOP 3.3 program (Raymond and Rousset 1995). The sequential Bonferroni correction (Rice 1989) was employed over the multiple tests carried out.

Total genetic variation (HT) was evaluated by a hierarchical gene diversity analysis using the NEGST program (Chakraborty et al. 1982). As suggested by Nei (1973) the statistical average of absolute gene diversity was obtained over all loci assayed to clarify a general mode of differentiation among populations. Relative gene diversity (GST) was divided into two components: between samples within basin (GSB) and between basins (GBT).

For classifying individuals into populations we used assignment tests as defined by Paetkau et al. (1995); however, probabilities of individuals belonging to populations were calculated using a Bayesian approach following Rannala and Mountain (1997). These analyses were performed using the GENECLASS 1.0 program (Cornuet et al. 1999). This program allowed us to generate a number of 10 000 genotypes per population based on the estimates of allele frequencies of the specific population. For each individual multilocus genotype, the probability of belonging to each of the samples was calculated, based on the allele frequencies of the samples. The individual was then assigned to the sample in which it had the highest probability of belonging. An individual was rejected from a sample if its multilocus genotype was among the 5% most unlikely simulated genotypes.

Nei (1972) genetic distance was calculated between all pairs of samples analysed. Genetic relationships among populations were estimated from genetic distances by generating an UPGMA dendrogram using the PHYLIP 3.5 package (Felsenstein 1993). Confidence of the topology was assessed by bootstrapping over loci using 1000 replicates. Genetic relationships among samples were also examined using principal co-ordinates analysis (PCoA; Gower 1966) of the Nei (1972) genetic distance matrix.

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. References
  8. Appendix

Enzymatic loci

Allelic variation was detected at 9 of the 25 protein coding loci examined (36%). The allele frequencies are summarised in Appendix 1. Some of the data reported here include 13 allelic variants. Three of them were found only in the Sil basin and two alleles (sAAT-1*40;sAAT-2*40) appear not to have been described previously. Three other variants (sIDHP-3*200; GPI-B*90 and *103) were specific from Duero basin; however none of them appeared in all samples of this basin (Appendix 1). Variant CK-A1*115 which characterize Spanish rivers flowing into the Atlantic drainage, showed high frequencies in all samples (average:0.986±0.028). sMDH-B1*80 and sMDH-B2*80 alleles reached a higher frequency in the Duero basin samples (ranging from 0.868 to 0.968 and from 0.158 to 0.565 respectively) than in the Sil basin samples (ranging from 0.075 to 0.400 and from 0 to 0.05 respectively; Appendix 1).

Different parameters of genetic variability are summarised in Table 1. Genetic variation measured as He and Na turned out to be slightly higher in the Sil (0.037±0.027 and 1.2±0.1) than in the Duero basin (0.033±0.01 and 1.16±0.054, respectively), although these differences were not significant (two tailed Mann-Whitney U-Test, P>0.500 in both cases).

Table 1.  Paramaters of genetic variability in the 9 samples at 25 enzymatic loci and 6 microsatellite loci (see Fig. 1 for codes).
SamplesEnzymatic lociMicrosatellite loci
 HoHeNaP0.95HoHeNaP0.95
  1. Observed and expected average heterozygosity (Ho and He, respectively), mean number of alleles per locus (Na), percentage of polymorphic loci (P0.95).

L10.0400.0551.2200.5830.5876.2100
S30.0530.0621.3200.5740.6547.3100
S10.0040.0081.140.7110.6365.0100
S20.0390.0411.2120.7130.7086.7100
D30.0180.0241.140.5420.5705.2100
D50.0230.0201.180.4560.3852.866.7
D10.0280.0371.280.5480.5425.783.3
D20.0450.0431.2160.6020.6947.8100
D40.0210.0391.2120.6170.5845.5100

The gene differentiation test showed a highly significant genetic heterogeneity among all the samples (P<0.00001). The same result was observed when comparisons, on the one hand, were made among the samples from the Sil basin and, on the other hand, between the 5 ones from Duero basin (P<0.00001 in both cases). Only in 6 (16.67%) out of the 36 multilocus pairwise FST values significant differences were not observed and all of them involving samples from Duero basin (Table 2).

Table 2.  Below diagonal: pairwise multi-locus FST signifcance from enzymatic loci. Above diagonal pairwise multi-loci FST significance from microsatellite loci (see Fig. 1 for codes).
 L1S3S1S2D3D5D1D2D4
  1. ***: Significant at the 0.1% level; **: Significant at the 1% level; NS: not significant.

  2. For both kind of loci was employed the sequential Bonferroni correction (Rice 1989).

L1----************************
S3***----*********************
S1******----******************
S2********----***************
D3************----************
D5**************----*********
D1************NS***----******
D2************NSNSNS----***
D4************NS******NS----

Hierarchical analyses of gene diversity, among all samples and without the sample from Lérez river, demonstrated that approximately 67% of the gene diversity (HT≈0.05) was the result of variation within samples, the remaining 33% being distributed among samples (column A and B in Table 3). In both cases, genetic variation at two of the nine polymorphic loci accounted for the largest proportion of differences among basins (sMDH-B1* and sMDH-B2*; Table 3). The GST division into two variability components revealed that the largest differentiation occurred among basins (GBT:26.52% and 24.22%), while variation among samples from the same basin was significantly lower (GSB:6.29% and 7.65% respectively, column A and B in Table 3).

Table 3.  Hierarchical analysis of genetic diversity for 25 allozyme loci. A: in all (9) samples assayed; B: excluding Lérez sample. C: only in all (3) samples from Sil river; D: only in all (5) samples from Duero river.
 ABCD
 HTGSTGSBGBTHTGSTGSBGBTHTGSTHTGST
  1. HT, total genetic variation; GST: relative genetic variation among samples, which was divided into GSB (sample within basin) and GBT (between basins).

  2. Bold GST value denotes significant differentiation based on Workman and Niswander (1970) test.

sAAT-1*0.071513.426.446.980.0813.046.466.580.19956.92-------
sAAT-2*0.00381.511.130.380.00421.491.130.360.01121.13-------
sAAT-4*0.06926.590.905.690.04233.481.651.830.08961.470.01272.57
CK-A1*0.02785.042.222.820.01465.204.750.45-------0.02334.77
GPI-A2*0.13416.675.2511.420.09517.108.338.770.23119.13-------
GPI-B*0.067912.219.542.670.076111.849.602.24-------0.1199.80
sIDHP-3*0.042217.6315.861.770.047317.4015.901.50-------0.074616.14
sMDH-B1*0.541252.776.7346.040.48151.878.5243.350.479821.550.14792.38
sMDH-B2*0.36825.985.6820.300.38624.706.0918.610.03283.380.4837.65
            
Average0.05332.816.2926.520.04931.877.6524.220.041813.500.03447.63

Likewise, only a few loci were the primary contributors to the gene differentiation, sMDH-B1* (46.04%) and sMDH-B2 *(20.30%) for GBT; whereas sIDHP-3* (15.86%) and GPI-B *(9.54%) for GSB. In the Sil basin, as GST decreased considerably (13.5%, column C in Table 3), only three loci were found to be significant. On the other hand, in the Duero basin, GST diminution (7.63%) reflected some level of homogeneity, with only four significant loci (column D in Table 3).

The UPGMA cluster and the principal co-ordinates analyses revealed a strong geographical pattern at the basin level but little geographical pattern within each one (2a and 3a). Duero basin samples were located in the first cluster whereas the Sil and Lérez river samples were grouped in the second one.

image

Figure 2. Consensus UPGMA tree illustrating the genetic relationships among 9 samples of brown trout (see Fig. 1 for codes). (a) from 25 allozyme loci; (b) from 6 microsatellite loci; (c) from both markers altogether. Bootstrap values calculated from 1000 replicate trees are given at branch points. Only bootstrap values above 500 are considered satisfactory.

Download figure to PowerPoint

image

Figure 3. Principal co-ordinates analyses (PCoA) of 9 brown trout populations based on Nei (1972) index of genetic distance (see Fig. 1 for codes). The populations have been projected onto the first two principal co-ordinates. (a) from 25 allozyme loci; (b) from 6 microsatellite loci; (c) from both markers altogether.

Download figure to PowerPoint

These results were also reflected in the assignment tests (Table 5). The greatest differences observed among basins allowed the correct allocation to the basin of origin of a high percentage of individuals (68.18%, 60% and 68.47% for Lérez river, Sil and Duero basins respectively). However, within each basin, the diminution in the interpopulational diversity led to a diminution in the percentage of individuals well allocated to their sample of origin (47.5% and 18.92% for Sil and Duero basins respectively).

Table 5.  Results of assignment tests, using the Bayesian approach included in the GeneClass 1.0 program (Cornuet et al. 1999). See Fig. 1 for codes.
Sample  SilDueroRejected from all
 L1S3S1S2D3D5D1D2D4 
  1. Note. Numbers represents the percentage of individuals assigned to or rejected (in parentheses) from the corresponding population. A denotes percentages obtained from 25 allozyme loci and M from 6 microsatellite loci. Individuals are rejected from one population when their multilocus genotypes were among the 5% genotypes most unlikely to appear in each sample, based on a simulation approach. “Total” denotes the percentage of individuals assigned to their population of origin from the total of individuals assigned to this population. Bold values denote the percentage of individuals correctly assigned in the sample of origin.

L1A68(13)4(27)14(68)0(36)0(100)0(100)0(100)0(100)0(100)14
 m59(41)0(100)0(100)0(100)0(100)0(100)0(100)0(100)0(100)41
S3A20(73)67(13)0(87)0(40)0(93)0(90)3(90)0(77)0(80)10
 m0(95)54(27)3(83)23(53)0(100)0(100)0(100)0(100)0(100)20
S1A85(10)10(0)5(5)0(5)0(100)0(95)0(95)0(95)0(95)0
 m0(100)25(45)55(25)5(65)0(95)0(100)0(100)0(100)0(100)15
S2A17(57)23(3)0(63)57(7)0(97)0(90)0(97)0(87)0(87)3
 m0(100)13(67)0(100)74(17)0(100)0(100)0(100)0(93)0(100)13
D3A0(100)29(29)0(92)0(67)0(8)3(29)0(8)33(0)35(0)0
 m0(100)0(100)0(100)0(100)71(12)0(100)0(92)21(50)0(96)8
D5A0(100)21(0)0(74)5(32)0(26)42(0)0(26)0(0)32(0)0
 m0(100)0(100)0(100)0(100)5(84)74(21)0(87)21(53)0(100)0
D1A0(100)22(55)0(94)0(77)0(39)0(58)16(17)32(10)20(32)10
 m0(100)0(100)0(100)0(97)3(84)0(100)52(20)42(20)0(68)3
D2A0(100)27(44)0(89)0(78)0(44)0(50)17(33)17(17)28(28)11
 m0(100)0(100)0(100)0(94)0(94)0(100)0(78)50(44)11(83)39
D3A0(100)21(42)0(100)0(68)0(32)5(47)0(32)37(26)26(16)11
 m0(100)0(100)0(100)0(100)5(85)0(100)5(85)45(15)30(30)15
TotalA35352594080561117 
 m10064927385100942375 

Microsatellite loci

In the 9 samples analyzed, variation at all 6 microsatellite loci was observed (Appendix 2). The data reported here include 75 allelic variants which were distributed as follows: BFRO 002* locus presented 3 alleles; 6 in Str-60*; 8 in Str-15*; 14 in SsoSL 311*; 16 in Str-543*; whereas the OKI 10* tetranucleotid locus was the most variable with 28 alleles. The allele frequencies in the 9 populations analyzed are summarised in Appendix 2. The average value of gene diversity and the average number of alleles per locus (Table 1) were significantly higher for microsatellite loci than for allozymes (two tailed Mann-Whitney U-Test, P:0.001 in both cases).

As for the allozymes, both parameters turned out to be slightly higher in the samples from the Sil basin in relation to the Duero basin, although differences were not significant (two tailed Mann-Whitney U-Test, P:0.001 for both parameters). The allelic distribution did not show any diagnostic microsatellite allele distinguishing between populations from Duero and Sil basins. However inversions of allele frequencies could be observed at the Str-15* and BFRO 002* loci (Appendix 2). Also allele differences in the other loci were observed although they were not as strong as in the previously mentioned. As regards to the presence of private alleles, 7 specific variants were found in the Lérez river, 8 in the Sil basin (Str-60*106; Str-543*130;*144 and *150 present in all the three analyzed samples) and 10 in the Duero basin. Although all of these alleles showed low frequencies they also contributed to distinguish each basin from each other (Appendix 2).

Genotypic frequencies fitted into the expected Hardy-Weinberg proportions in all the 53 tests. When comparing the allele frequencies among all the samples and within each basin a highly significant genetic heterogeneity was observed (P<0.00001 in the three cases). The multilocus pairwise FST values were always significant even after a Bonferroni correction, which supported the high genetic differentiation observed among populations (Table 2).

Although the total diversity of microsatellite loci was 15 times higher than for allozymic loci (0.737 and 0.053 respectively) it was distributed in a similar way for both markers, increasing slightly the relative intrapopulational diversity component when microsatellite loci were analyzed (HS/HT:79.16%, column A in Table 4). The remaining 20.84% being distributed among samples and these differences turned out to be significant both when including and excluding the sample from Lérez river (FST:0.1544; P<0.001 and FST:0.1286; P<0.001 respectively). Genetic variation among samples was distributed among all the loci, all of them showing significant differences.

Table 4.  Hierarchical analysis of genetic diversity for 6 microsatellite loci. A: in all (9) samples assayed; B: excluding Lérez sample. C: only in all (3) samples from Sil river; D: only in all (5) samples from Duero river.
 ABCD
 HTGSTGSBGBTHTGSTGSBGBTHTGSTHTGST
  1. HT, total genetic variation; GST: relative genetic variation among samples, which was divided into GSB (sample within basin) and GBT (between basins).

  2. Bold GST value denotes significant differentiation based on Workman and Niswander (1970) test.

μStr-15*0.519439.241.8937.350.415521.832.6519.180.6432.590.15155.05
BFRO 002*0.655237.998.729.290.638838.0610.0328.030.470215.990.453512.67
μStr-60*0.66169.943.746.20.6678.784.164.620.66924.250.61644.44
μStr-543*0.811819.8613.266.60.810818.4114.933.480.82173.30.759223.37
OKI 10*0.94449.596.952.640.93899.427.861.560.90317.620.9378.2
SsoSL 311*0.8318.84.8313.970.802917.625.62120.66365.620.73236.8
            
Average0.73720.946.8914.050.712318.058.0210.030.69516.080.608310.86

The distribution of gene diversity among samples considerably varied when Lérez river sample was included or excluded from the analyses (column A and B in Table 4). In the first case, differences among basins were twice than within basins, whereas when Lérez sample was excluded, differences within and among basins scored similar values (column A and B in Table 4). This change was mainly due to Str-15* locus, which showed an allele distribution that clearly allowed to differentiate both basins from Lérez river (Appendix 2). Despite the GST value decreased inside each basin (column C and D in Table 4), these differences became significant in both cases (FST:0.066; P<0.001 and FST:0.0423; P<0.001 in the Sil and Duero basins respectively).

Genetic affinity among populations (Fig. 2b and 3b) displayed the formation of two clearly differentiated clusters that represent the Duero and the Sil basins respectively, and the population from Lérez river moved away from the others.

When both markers were represented together (Fig. 2c and 3c), both basin group of populations clearly came apart and both figures only differed in the relative position that the Lérez river sample occupied.

The percentage of individuals accurately assigned to their sample of origin (Table 5), reached an average value of 55.36% in the Duero basin (ranging from 30% for D4 to 74% for D5) and 61.25% in the Sil basin (ranging from 54% for S3 to 74% for S2). When individuals were assigned to the basin of origin, microsatellites showed a greater resolution power than allozymes, with values of 88.39% and 83.75% in the Duero and Sil basins respectively.

DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. References
  8. Appendix

The main result of the present work is the confirmation, by means of microsatellite loci analysis, of two genetically divergent groups of brown trout populations corresponding to the Duero and Sil hydrographical basins in the Province of Leon.

The combination of different molecular markers has been demonstrated to be of great value in studying population structure and dynamics of different species in greater depth (Estoup et al. 1998; Aurelle et al. 2002). Despite the scarce variability that allozymes show in relation to other more polymorphic molecular markers, such as microsatellites, they have proved their usefulness to draw the evolution that this species has gone through and differentiating the lineages and sub-lineages that brown trout have shown through its wide geographical distribution (Krieg and Guyomard 1985; Ferguson 1989; Apostolidis et al. 1996; García-Marín et al. 1999). The genetic characteristics that populations have shown in this work are in accordance with their geographic location in the south part of the Atlantic drainage (CK-A1*115 and LDH-C*100 allele fixation; García-Marín and P 1996).

Also the high frequency that sMDH-B1*80 showed (average: 0.928) and the presence of sMDH-B2*80 (average: 0.428) in the Duero basin agree with results previously reported (García-Marín and P 1996; Cagigas et al. 1999b; Corujo 1999; Bouza et al. 2001). Nonetheless, the presence of both alleles in the Sil basin, although with frequencies significantly lower than those in Duero basin, contrasts with published data. Excluding Sanz et al. (2000), these variants have never been reported in populations out of the Duero basin (Morán et al. 1995; García-Marín and P 1996; Blanco et al. 1998; Bouza et al. 1999, 2001; Cagigas et al. 1999b, 2002). Moreover, Sanz et al. (2000) found both alleles in 3 out of 6 populations from Miño drainage but all of them were sampled in the Sil basin.

Enhancement of populations through the release into the wild of fishes bred in hatcheries has been a common management practice in the Duero and Sil basins. However these hatchery strains have been founded with individuals from central Europe that have genetic characteristics completely different of the “autochthonous” populations from Sil and Duero basins (García-Marín et al. 1991; Morán et al. 1991; Martínez et al. 1993; Blanco et al. 1998; Corujo 1999). Currently the policy is to construct hybrid stocks (German stock×local populations) where central Europe domestic females are crossed with wild males from Duero basin. Then females from the hybrid offspring were again crossed with wild males for several generations (pers. comm.). These stocks are likely to be more successful in surviving than pure exotic stocks but there is an obvious risk that this initiative will increase introgression rates, and thus genetic contamination of natural populations. As these hatchery strains have been used to restock all rivers in the province of Leon, the sMDH-B1*80 and sMDH-B2*80 origin in the Sil basin would be a consequence of the restocking policy.

In order to clarify the situation our group proceeded to use microsatellite loci since they have demonstrated their usefulness for assessing whether or not present populations are indigenous or have been founded by straying or stocked fish (Nielsen et al. 1997; Hansen et al. 2000). From the analysis carried out with microsatellite loci of restocked samples from the Duero basin and classic hatchery strains (data non publ.), some microsatellite alleles have been proposed as diagnostic markers:Str-15*220;*222;*224;*226 and Str-60*98. The presence of these markers along with sMDH-B1*80 and sMDH-B2*80 alleles in the Sil basin supports therefore the domestic origin of such alleles. From a conservationist point of view, the introgression of foreign genes means a high risk as genetic identity, which is the result of years of adaptation to a concrete environment, could be missing in the Sil basin. Nevertheless the lack of surveys that use microsatellite loci in Spain makes difficult the lighting up about the natural or artificial origin of such variants in the Sil basin.

Microsatellites have also demonstrated their usefulness for differentiating populations. Duero basin populations showed their own genetic characteristics, mainly high frequencies of Str-15*210 and BFRO 002*124, which allow to distinguish them from Sil basin populations. Thus, the theory about the existence of a glacial refuge in the Duero basin, proposed from the analysis of allozymes and mitochondrial DNA, becomes more consistent (García-Marín et al. 1999; Bernatchez 2001; Bouza et al. 2001). Regarding to Sil basin samples, four specific alleles (Str-60*106, Str-543*130,*144 and *150) that are not found in the Duero basin were observed and also showed low frequencies of the two alleles (Str-15*210 and BFRO 002*124) previously mentioned.

Although the total amount of variation resulted significantly higher for microsatellites however it was similarly distributed for both kinds of markers. The GST values for the complete set of loci indicated a strong differentiation between samples. Nonetheless while allozymic gene differentiation at the intrabasin level was basically due to a few loci, microsatellite differences were significant in all the analyzed loci (Tables 3 and 4). On the other hand, allozyme pairwise FST estimates also showed a strong differentiation between all samples excepting between the most of Duero basin samples (Table 1). The observed homogeneity inside the Duero basin could lead to consider the whole basin as a unique management unit. Nonetheless, microsatellites revealed a strong populational subdivision structure in Duero basin showing highly significant FST values between all pair of samples. Similar results where allozymes suggest homogeneity whereas nuclear markers, such as RFLPS, suggest a high populational subdivision have been described in other species (Karl and Avise 1992; Zhang et al. 1993; Pogson et al. 1995). The balanced selection in loci that are coding for proteins has been proposed in such surveys to explain the heterogeneous polymorphism patterns observed. Nonetheless, we cannot reject the action of the genetic drift associated with the scarce existing gene flow among populations of brown trout when explaining this situation, especially if we take into account that several populations analyzed in the present work have been gathered in river headwaters, places that represent habitats ecologically unstable due to variations in river flows, which can provoke significant ups and downs in the populational size (Estoup et al. 1998).

The high genetic variability that microsatellite markers show means an increasing amount of allelic variants on a microgeographical level, without losing for that reason, its discriminatory power in higher levels. This higher discriminatory level is reflected in the high assignment percentages obtained with microsatellite loci in relation to allozymic loci, especially in the Duero basin, where we could observe a correct individual assignment to the population of origin of 18.92% for allozymic loci and 55.36% for microsatellite loci. In the same way, the higher resolution power obtained in UPGMA trees and principal co-ordinates analysis also showed the higher discriminatory capacity of microsatellites.

The use of microsatellite markers has also shown to have a greater usefulness when determining the populational structure of this species on a microgeographical scale, proving a higher populational subdivision structure in the Duero basin that isozymes are unable to show. Nonetheless, it is necessary to extend the number of analyzed samples, especially in the Sil basin, in order to determine with more precision, the origin of several variants whose presence might be a consequence of the restocking made with individuals from the Duero basin and that might be endangering the genetic identity of this species in the Sil basin. Besides, the use of another type of molecular markers, such as RFLPs of mitochondrial DNA, might in the future helps to illuminate the status of natural brown trout populations in this basin.

The natural populations of brown trout in León represent a unique and valuable resource which must be protected. Based on this study we recommend to preserve existing known natural populations and protect their habitats. Also existing hatchery populations should be replaced with local natural populations in the interest of more effective protection of natural populations. When releases for improving fisheries was made monitoring the impact is crucial, particularly if there are no barriers between wild populations and stocking places. This requires collection of data both before and after releases.

Acknowledgements

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. References
  8. Appendix

We gratefully acknowledge Servicio Territorial de Medio Ambiente y Ordenación del Territorio de León for supplying biological samples. This research was partially supported by funds from Servicio Territorial de Medio Ambiente y Ordenación del Territorio de León.

References

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. References
  8. Appendix
  • Aebersold P. B., Winans G. A., Teel D. J. et al., (1987). Manual for starch gel electrophoresis: a method for the detection of genetic variation. NOAA Tech. Rep. NMFS. 61: 119.
  • Apostolidis A., Karakousis Y. and Triantaphyllidis C., (1996). Genetic divergence and phylogenetic relationships among Salmo trutta L. (brown trout) populations from Greece and other European countries. Heredity 76: 551560.
  • Aurelle D., Cattaneo-Berrebi G. and Berrebi P., (2002). Natural and artificial secondary contact in brown trout (Salmo trutta, L.) in the French western Pyrenees assessed by allozymes and microsatellites. Heredity 89: 171183.DOI: 10.1038/sj.hdy.6800120
  • Bernatchez L., (2001). The evolutionary history of brown trout (Salmo trutta L.) inferred from phylogeographic, nested clade, and mismatch analyses of mitochondrial DNA variation. Evolution 55: 351379.
  • Blanco G., Cagiga E., Vázquez E. et al., (1998). Genetic impact of introduced domesticated strain on Spanish native populations of brown trout (Salmo trutta). In: Stocking and introduction of fish, (ed. I.Cow), Blackwell, pp. 371379.
  • Bouza C., Arias J., Castro J. et al., (1999). Genetic structure of brown trout, Salmo trutta L., at the southern limit of the distribution range of anadromous form. Mol. Ecol. 8: 19912001.
  • Bouza C., Castro J., Sánchez L. et al., (2001). Allozymic evidence of parapatric differentiation of brown trout (Salmo trutta L.) within an Atlantic river basin of the Iberian Peninsula. Mol. Ecol. 10: 14551469.
  • Cagigas M. E., Vázquez E., Blanco G. et al., (1999a). Genetic effects of introduced hatchery stocks on indigenous brown trout (Salmo trutta L.) populations in Spain. Ecol. Freshw. Fish. 8: 141150.
  • Cagigas M. E., Vázquez E., Blanco G. et al., (1999b). Combined assessment of genetic variability in populations of brown trout (Salmo trutta L.). Based on allozymes, microsatellites, and RAPD markers. Mar. Biotechnol. 1: 286296.
  • Cagigas M. E, Vázquez E., Blanco G. et al., (2002). Phylogeographical lineages in brown trout (Salmo trutta): investigating microgeographical differentiation between native populations from northern Spain. Freshw. Biol. 47: 18791892.
  • Chakraborty R., Haag M., Ryman N. et al., (1982). Hierarchical gene diversity analysis and its application to brown trout population data. Hereditas 97: 1721.
  • Cornuet J. M., Piry S., Luikart G. et al., (1999). Comparison of methods employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics 144: 20012014.
  • Corujo M., (1999). Nuevos datos de la estructura genética de las poblaciones de trucha común (Salmo trutta L.) en la provincia de León; su integración en la descripción de la península Ibérica. Seminario de Investigación. Área de Genética Depto de Biología Funcional. Univ. de Oviedo.
  • Estoup A., Presa P., Krieg F. et al., (1993). (CT)n and (GT)n microsatellites: a new class of genetic markers for Salmo trutta L. (brown trout). Heredity 71: 488496.
  • Estoup A., Rousset F., Michalakis Y. et al., (1998). Comparative analysis of microsatellite and allozyme markers: a case study investigating microgeographic differentiation in brown trout (Salmo trutta). Mol. Ecol. 7: 339353.
  • Felsenstein J., (1993). PHYLIP (Phylogenetic Inference Package, Version 3.5c), Dept of Genetics, SK, Univ. of Washington, Seattle, WA.
  • Ferguson A., (1989). Genetic differences among brown trout, Salmo trutta, stocks and their importance for the conservation and management of the species. Freshw. Biol. 21: 3546.
  • Fritzner N. G., Hansen M. M., Madsen S. S. et al., (2001). Use of microsatellite markers for identification of indigenous brown trout in a geographical region heavily influenced by stocked domesticated trout. J. Fish Biol. 58: 11971210.
  • García-Marín J. L. and Pla C., (1996). Origins and relationships of native populations of Salmo trutta (brown trout) in Spain. Heredity 77: 313323.
  • García-Marín J. L., Jorde P. E., Ryman N. et al., (1991). Management implications of genetic differentiation between native and hatchery populations of brown trout (Salmo trutta) in Spain. Aquaculture 95: 235249.
  • García-Marín J. L., Sanz N. and Pla C., (1998). Proportions of native and introduced brown trout in adjacent fished and unfished Spanish rivers. Conserv. Biol. 12: 313319.
  • García-Marín J. L., Utter F. M. and Pla C., (1999). Postglacial colonization of brown trout in Europe based on distribution of allozyme variants. Heredity 82: 4656.
  • Gower J. C., (1966). Some distances properties of latent roof and vector methods in multivariate analysis. Biometrika 53: 315328.
  • Guo S. W. and Thompson E. A., (1992). Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48: 361372.
  • Guyomard R. and Krieg F., (1983). Electrophoretic variation in six populations of brown trout (Salmo trutta L.). Can. J. Genet. Cytol. 25: 403413.
  • Hansen M. M., Loeschcke V., Rasmussen G. et al., (1993). Genetic differentiation among Danish brown trout (Salmo trutta) populations. Hereditas 118: 177185.
  • Hansen M. M., Ruzzante D. E., Nielsen E. E. et al., (2000). Microsatellite and mitochondrial DNA polymorphism reveals life-history dependent interbreeding between hatchery and wild brown trout (Salmo trutta L). Mol. Ecol. 9: 583594.
  • Karl S. A. and Avise J. C., (1992). Balancing selection at allozyme loci in oysters: implications from nuclear RFLPS. Science 256: 100102.
  • Krieg F. and Guyomard R., (1985). Population genetics of French brown trout (Salmo trutta L.): large geographical differentiation of wild populations and high similarity of domesticated stocks. Génét. Sélection Evol. 17: 225242.
  • Laikre L., Antunes A., Alexandrino P. et al., (1999). Conservation genetic management of brown trout (Salmo trutta) in Europe. In: (“Troutconcert”: EU FAIR CT97-3882), (ed. L.Laikre), Stockholm Univ., Sweden.
  • Louis E. J. and Dempster E. R., (1987). An exact test for Hardy-Weinberg proportions for multiple alleles. Biometrics 43: 805811.
  • Machordom A., Suárez J., Almodóvar A. et al., (2000). Mitochondrial haplotype variation and phylogeography of Iberian brown trout populations. Mol. Ecol. 9: 13251338.
  • Martínez P., Arias J., Castro J. et al., (1993). Differential stocking incidence in brown trout (Salmo trutta) populations from northwestern Spain. Aquaculture 114: 203216.
  • Morán P., Pendás A. M., García-Vázquez E. et al., (1991). Failure of stocking policy, of hatchery reared brown trout, Salmo trutta L., in Asturias, Spain, detected using LDH-5* as a genetic marker. J. Fish Biol. 39: 117122.
  • Morán P., Pendás A. M., García-Vázquez E. et al., (1995). Estimates of gene flow among neighbouring populations of brown trout. J. Fish Biol. 46: 593602.
  • Nei M., (1972). Genetic distance between populations. Am. Nat. 106: 283292.
  • Nei M., (1973). Analysis of gene diversity in subdivided populations. Proc. Natl Acad. Sci. USA 70: 33213323.
  • Nielsen E. E., Hansen M. M. and Loeschcke V., (1997). Analysis of microsatellite DNA from old scale samples of Atlantic salmon: a comparison of genetic composition over sixty years. Mol. Ecol. 6: 487492.
  • Paetkau D., Calvert W., Stirling I. et al., (1995). Microsatellite analysis of population structure in Canadian polar bears. Mol. Ecol. 4: 347354.
  • Pogson G. H., Mesa K. A. and Boutillier R. G., (1995). Genetic population structure and gene flow in the atlantic cod Gadus mohuas: a comparison of allozyme loci and nuclear RFLP loci. Genetics 139: 375385.
  • Poteaux C., Bonhomme F. and Berrebi P., (1999). Microsatellite polymorphism and genetic impact of restocking in mediterranean brown trout (Salmo trutta L.). Heredity 82: 645653.
  • Presa P. and Guyomard G., (1996). Conservation of microsatellites in three species of salmonids. J. Fish Biol. 49: 13261329.
  • Rannala B. and Mountain J. L., (1997). Detecting immigration by using multilocus genotypes. Proc. Natl Acad. Sci. USA 94: 91979201.
  • Raymond M. and Rousset F., (1995). GENEPOP (Version.3.3): a population genetics software for exact tests and ecumenicism. J. Heredity 86: 248249.
  • Rice W. R., (1989). Analyzing table of statistical test. Evolution 43: 223225.
  • Ruzzante D. E., Hansen M. M. and Meldrup D., (2001). Distribution of individual inbreeding coefficients, relatedness and influence of stocking on native anadromous brown trout (Salmo trutta) population structure. Mol. Ecol. 10: 21072128.
  • Ryman N., (1983). Patterns of distribution of biochemical genetic variation in salmonids: differences between species. Aquaculture 33: 121.
  • Ryman N., (1991). Conservation genetics considerations in fishery management. J. Fish Biol. (suppl. A) 39: 211224.
  • Sanz N., García-Marín J. L. and Pla C., (2000). Divergence of brown trout (Salmo trutta) within glacial refugia. Can. J. Fish. Aquat. Sci. 57: 22012210.
  • Shaklee J. B., Allendorf F. W., Morizot D. C. et al., (1990). Gene nomenclature for protein-coding loci in fish. Trans. Am. Fish. Soc. 119: 215.
  • Skaala Ø., Jørstad K. E. and Borgstrøm R., (1996). Genetic impact on two wild brown trout (Salmo trutta L.) populations after released of non-indigenous hatchery spawners. Can. J. Fish. Aquat. Sci. 53: 20272035.
  • Slettan A., Olsaker I. and Lie O., (1995). Atlantic salmon, Salmo salar L., microsatellites at the SSOSL311, SSOSL417, SSOSL85, SSOSL25 loci. Anim. Genet. 26: 277285.
  • Smith C., Koop B. and Nelson R. J., (1998). Isolation and characterization of coho salmon (Oncorhynchus kisutch) microsatellites and their use in other salmonids. Mol. Ecol. 7: 16141615.
  • Sušnik S., Snoj A., Pohar J. et al., (1997). The microsatellite marker (BRFO 002) characteristic for different geographically remote brown trout, Salmo trutta L., populations. Anim. Genet. 28: 370383.
  • Swofford D. L. and Selander R. B., (1989). BIOSYS-2. A computer program for the analysis of allelic variants in population genetics and biochemical systematics Release 1.7, Illinois Nat. Hist. Survey.
  • Walsh P. S., Metzger D. A. and Higuchi R., (1991). ChelexR 100 as a medium for simple extraction of DNA for PCR–based typing from forensic material. Biotechnics 10: 506510.
  • Workman P. L. and Niswander J. D., (1970). Population studies on southwestern Indian tribes. II. Local genetic differentiation in the Papago. Am. J. Human Genet. 22: 2429.
  • Zhang Q., Marof M. A. S. and Kelinhofs A., (1993). Comparative diversity analysis of RFLPS and allozymes within and among populations of Hordeum vulgare ssp. Spontaneum. Genetics 134: 909916.

Appendix

  1. Top of page
  2. Abstract
  3. MATERIAL AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. References
  8. Appendix

Appendix A

Appendix 1. Allelic frequencies observed at 9 polymorphic enzymatic loci in 9 brown trout samples; (n), sample size (see Fig. 1 for codes).Text Table 6Appendix 2. Allelic frequencies observed at 6 microsatellite loci in 9 brown trout samples; (n), sample size (see Fig. 1 for codes).Text Table 7

Table Text Table 6. . 
SampleL1S1S3S2D3D5D1D2D4
sAAT-1*(22)(20)(30)(30)(24)(19)(31)(18)(19)
*400.1000.200
*450.033
*1001.0001.0000.8670.8001.0001.0001.0001.0001.000
sAAT-2*(22)(20)(30)(30)(24)(19)(31)(18)(19)
*400.017
*1001.0001.0001.0000.9831.0001.0001.0001.0001.000
sAAT-4*(10)(20)(30)(30)(23)(19)(31)(18)(19)
*650.1500.0250.0830.0330.032
*1000.8500.9750.9170.9671.0001.0000.9681.0001.000
CK-A1*(22)(19)(30)(30)(24)(19)(31)(18)(19)
*1000.0680.059
*1150.9321.0001.0001.0001.0001.0001.0000.9411.000
GPI-A2*(22)(19)(30)(30)(24)(19)(31)(18)(19)
*1000.7501.0000.7500.8501.0001.0001.0001.0001.000
*2000.2500.2500.150
GPI-B*(22)(19)(30)(30)(24)(19)(31)(18)(19)
*900.1770.111
*1001.0001.0001.0001.0001.0001.0000.8230.8611.000
*1030.028
sIDHP-3*(10)(20)(30)(30)(24)(19)(31)(18)(18)
*1001.0001.0001.0001.0001.0001.0001.0001.0000.806
*2000.194
sMDH-B1*(22)(20)(30)(30)(24)(19)(31)(18)(19)
*800.0750.4000.1670.9580.8680.9680.8890.921
*1000.6590.9250.3170.8000.0420.1320.0320.0830.026
*1100.3410.2830.0330.0280.053
sMDH-B2*(22)(20)(30)(30)(24)(19)(31)(18)(19)
*800.0500.4790.1580.5650.4170.421
*1000.9321.0000.9501.0000.5210.8420.4350.5830.579
*1100.068
Table Text Table 7. . 
SampleL1S1S3S2D3D5D1D2D4
μStr-15*(22)(20)(29)(30)(24)(19)(31)(18)(20)
*2080.0230.5000.3280.2830.083---0.0480.1110.050
*210---0.3750.4830.4830.9171.0000.9520.7770.950
*2160.114------------------------
*2180.8630.0500.1380.217---------------
*220---0.0750.0340.017---------0.028---
*222---------------------0.028---
*224------0.017------------0.028---
*226---------------------0.028---
BFRO 002*(22)(20)(30)(29)(24)(19)(31)(18)(20)
*1220.6370.475---0.1380.2080.2110.0650.5830.300
*1240.0450.0250.0830.2070.7920.7890.7580.3890.700
*1260.3180.5000.9170.655------0.1770.028---
μStr-60*(22)(20)(30)(29)(24)(19)(31)(18)(20)
*980.250---0.0670.155---------0.056---
*1020.6590.5000.4660.3970.4580.5260.5970.5000.650
*1040.091---------0.188---0.0480.0560.050
*106---0.2000.2830.431---------------
*108---0.3000.1670.0170.2710.4740.2100.1660.175
*112------0.017---0.083---0.1450.2220.125
μStr-543*(22)(19)(29)(27)(24)(19)(31)(18)(20)
*118------------------0.0160.0830.050
*120------0.017---0.083---------0.100
*124---0.026------0.104---0.1130.1110.125
*1260.0230.0530.2420.2590.1040.0260.5490.4160.350
*1280.4770.2890.1560.2960.4380.9740.2740.083---
*130---0.0530.0340.037---------------
*132---------------------0.028---
*1340.455---0.0860.074---------0.056---
*136------0.017------------------
*1380.045------------------0.0280.150
*142------0.017------------------
*144---0.2640.0690.185---------------
*146---0.0260.1030.0190.271---0.0480.1670.225
*150---0.2890.2250.130---------------
μStr-543*(22)(19)(29)(27)(24)(19)(31)(18)(20)
*152------0.034------------------
*154---------------------0.028---
OKI 10*(21)(19)(30)(30)(24)(19)(31)(18)(20)
*1000.213---0.017------------------
*1080.048------------------------
*144---------------------0.028---
*1600.024------------------------
*164---------------------0.028---
*1720.048---------------0.194------
*1760.048---------0.063------------
*1800.024------0.050------0.0160.028---
*1840.1660.026------------0.0160.1110.075
*188---0.0530.0670.0670.063------0.083---
*192---0.1580.2000.0330.0830.211---------
*196---0.4440.1000.0330.206------0.028---
*200---0.1050.050------0.105------0.100
*2040.024---0.0500.0670.021---0.0160.028---
*208------0.1000.1170.083---0.0810.0560.025
*2120.0710.0260.0500.3000.042---0.0480.1100.075
*2160.048---0.0330.0500.063---0.0320.0280.125
*220------0.0170.033---0.474---0.0560.050
*224---0.0530.0330.0830.0630.0260.0650.0830.050
*2280.095---0.0170.033---0.1050.0970.0830.075
*2320.1430.026---0.0830.0630.0530.0480.0830.075
*2360.0240.0530.033---0.063---0.2260.0830.225
*240---0.0260.1830.0170.0420.0260.016---0.050
*244---0.026---0.0170.103---0.048------
*248------0.0500.0170.042---0.0160.0560.025
*252------------------0.081---0.025
*2560.024------------------------
*284---------------------0.0280.025
SsoSL 311*(22)(19)(30)(30)(24)(19)(31)(18)(20)
*134---------------------0.056---
*138------0.017------------------
*1400.2270.3680.3670.150---0.3420.0650.250---
*1420.363------0.050---------------
*1440.023---0.0330.1000.5620.3690.3230.1940.275
*146---0.6320.4160.3830.125---0.1130.0830.075
*1480.023---0.0170.0170.2920.2890.4670.2780.400
*1500.023---------0.021---0.0320.1110.225
*152------0.1500.267------------0.025
*1540.068------0.033---------0.028---
*1560.023------------------------
*1580.023------------------------
*1620.182------------------------
*1720.045------------------------