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All issues No 418 (2017) Knowl. Manag. Aquat. Ecosyst., 418 (2017) 55 Full HTML
Open Access
Issue
Knowl. Manag. Aquat. Ecosyst.
Number 418, 2017
Article Number 55
Number of page(s) 9
DOI https://doi.org/10.1051/kmae/2017048
Published online 22 November 2017

© T. Schmidt et al., Published by EDP Sciences 2017

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License CC-BY-ND (http://creativecommons.org/licenses/by-nd/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. If you remix, transform, or build upon the material, you may not distribute the modified material.

1 Introduction

Numerous fish populations are threatened by different environmental factors and human activities (e.g. Freyhof and Brooks, 2011), and diverse measures are taken to conserve such populations. Populations involved in conservation programs should be genetically characterized and compared to other populations of the species to set up appropriate conservation strategies (e.g. Attard et al., 2016; Luck et al., 2003; Palsbøll et al., 2007). Such information may help to prioritize conservation strategies (Araguas et al., 2007; Fraser and Bernatchez, 2001; Luck et al., 2003; Nunney and Campbell, 1993).

Supportive breeding is a common ex situ conservation strategy, often used after or along with in situ strategies like habitat restoration (e.g. Anderson et al., 2013; Black et al., 2016; Hundt et al., 2015; Saura and Faria, 2011), which aims at directly increasing population size. In the ideal case, specimen for supportive breeding originate from the threatened population itself to take into account possible local adaptions and best conserve the global genetic diversity of species (e.g. George et al., 2009). However, the number of spawners in breeding programs is often restricted. Thus, typically the effective population size Ne of brood stocks is low, which may lead to adverse genetic effects, such as inbreeding depressions (e.g. Fraser, 2008; Naish et al., 2013). Genetic analyses of brood stocks (spawners or descendants) may indicate such issues early (e.g. Naish et al., 2013).

Brown trout (Salmo trutta) is a species with a high level of genetic diversity and complicated spatial patterns of genetic variability indicate a complex evolutionary history (e.g. Bernatchez, 2001; Cortey et al., 2009; Laikre, 1999; Lerceteau-Kohler et al., 2013; McKeown et al., 2010). Based on analyses of mitochondrial DNA (mtDNA) at least six major genetic lineages have been detected in different regions of the Eurasian native range of the species (Bernatchez, 2001; Cortey et al., 2009; Suarez et al., 2001; Susnik et al., 2005). The “(Northern-) Atlantic lineage” is the most widespread lineage and natively distributed from western to northern Europe. Beside this large scale variability, brown trout may show considerable regional or even local differentiation (e.g. Lehtonen et al., 2009; Palmé et al., 2013), which may indicate local adaption (Meier et al., 2011), and plays an increasingly important role in conservation (e.g. Fruciano et al., 2014; Vilas et al., 2010). Additionally, natural patterns of diversity have been altered by various human activities (Kohout et al., 2012 and citations therein) more recently, which adds further complexity. Nevertheless, in several regions and certain populations human influence on the genetic diversity of brown trout may be still absent or negligible (Lerceteau-Kohler et al., 2013; Van Houdt et al., 2005). Although genetic diversity of brown trout has been studied frequently, genetic data on this species are still lacking for important regions within its natural distribution range, like the Baltic Daugava river basin.

Against this background, we have analyzed genetically a brood stock of brown trout to enhance a local conservation initiative in Latvia. This brood stock is used to restock and support a threatened population of resident brown trout in Virgulica creek in the Daugava river basin. Virgulica creek brown trout are mainly threatened by the loss of suitable spawning grounds caused by extensively increased dam buildings of beavers (Castor fiber). This had led to a considerable decline in population size, and several stretches of the creek have been totally without trout. To protect the fish population of Virgulica creek, spawning grounds have been restored and offspring of a locally derived brood stock of brown trout was used to repopulate the creek afterwards. These efforts were undertaken by local, private initiatives, which may be considered a common situation for conservation efforts of single, specific fish stocks. Typically, comprehensive genetic analyses, including populations outside the focal area, are far beyond the capabilities of such initiatives. However, local breeding programs may benefit from genetic analyses of the population under consideration itself and further comparisons with data from other regions − if available (cp. George et al., 2009).

To improve the Virgulica creek trout restoration efforts, we have used nuclear and mitochondrial markers and compared the genetic diversity of the brood stock to other Baltic populations. Further, we have examined the allelic data for indications of inbreeding. Finally, our study provides first genetic details on resident brown trout from the Baltic Daugava river basin and contributes new phylogeographically relevant information on brown trout from an understudied region. Overall, this study may improve the regional management of the valuable genetic diversity of brown trout in the Daugava river basin.

2 Materials and methods

2.1 Location and brood stock

Virgulica creek is a small (length ca. 20 km) tributary of River Pededze, in the Daugava river basin in Latvia (57.44°N, 27.33°E) and a typical salmonid creek of the region (Fig. 1). The dominant land use around Virgulica creek is forestry, but also some agriculture. In the 1960/70s the creek was partly straightened for agricultural land reclamation.

In recent years the main threat to brown trout in Virgulica creek was loss of spawning grounds and habitat fragmentation caused by beaver dams. Licensed recreational angling takes place at the creek, but fishing pressure is generally low. However, illegal poaching has been observed.

Virgulica creek has a historic watermill dam 150 m before joining River Pededze, which presumably prevents fish upstream migrations. No stocking with foreign brown trout happened within the last 20 years and there is no indication for earlier introductions. Thus, we consider the Virgulica creek brown trout as autochthonous.

The Virgulica creek brood stock was derived from 50 wild brown trout, caught in the most downstream, largely unmodified stretch of the creek in 2009. Eggs of several randomly selected females were fertilized with the milt of the respective number of likewise randomly selected males. Thereby, all available specimen (50), regardless of phenotypic properties (e.g. size, early/late maturity), were used to conserve genetic diversity and avoid artificial selection.

thumbnail Fig. 1

Location of the Virgulica creek (red cross) in Latvia and approximate locations of non-hatchery samples from Carlsson and Carlsson (2002), Carlsson and Nilsson (2000), Lehtonen et al. (2009), Nilsson et al. (2008), Östergren et al. (2015), Samuiloviene et al. (2009), Was and Bernaś (2016), and Was and Wenne (2002) (Tab. 2). Locations for Östergren et al. (2015) are indicated at the respective river outlets.

2.2 Sampling and genetic analysis

In November 2011 adipose fin tissue of 25 specimens of the first generation offspring of the breeding program was clipped and tissue samples were preserved in 96% ethanol and stored at −20 °C in the lab. DNA was extracted using a modified (Wetjen et al., 2017) salt protocol (Aljanabi and Martinez, 1997).

Nuclear DNA of all 25 specimens was examined at twelve microsatellite loci in two multiplex-PCRs (Type-it Microsatellite PCR Kit, QIAGEN) and one single PCR (Tab. 1). The 5 µl reaction volumes contained different volumes of the primers (Tab. 1), 1x Type-it Multiplex PCR Master Mix, 0.5x Q-Solution (QIAGEN) and 10 ng DNA. Cycling parameters were: initial denaturation (95 °C, 5 min), 30 cycles at 94 °C (30 s), 57 °C (90 s), 72 °C (60 s), and final extension at 60 °C (30 min). The loci were analyzed on an automated sequencer (CEQ 8000, Beckman Coulter) using the GenomeLab DNA Size Standard Kit (400 and 600 respectively, Beckman Coulter).

Further, the control region (CR) of the mtDNA of 11 specimens was amplified with primers Str-L19 (5'-CCACTAGCTCCCAAAGCTA-3') and Str-H17 (5'-ACTTTCTAGGGTCCATC-3') (Bernatchez et al., 1992), as detailed in Wetjen et al. (2017). Bidirectional sequencing was done by SeqIT GmbH & Co KG.

Table 1

Primer concentrations, batches, size ranges and references of the microsatellite loci.

2.3 Data analysis

Prior to further analyses we checked completeness of allelic data and determined polymorphism of loci to reject uncomplete and monomorphic loci. MICRO-CHECKER Version 2.2.3 (Van Oosterhout et al., 2004) was used to test for null alleles. We determined the size range of alleles [base pairs (bp)], the number of alleles and genotypes, the allele frequencies (Supplemental Information Tab. S1), the allelic richness (AR) and observed and expected heterozygosity (Hobs and Hexp) for each locus and calculated the difference Hexp−Hobs and the fixation index FIS [(Hexp−Hobs)/Hexp]. Further, we tested conformity to the Hardy–Weinberg equilibrium (HWE) per locus. At population level we calculated the expected heterozygosity Hs and the means of the above genetic diversity values per locus and specimen respectively with standard errors (SEM). For comparison of our results on n = 25 specimens from Virgulica creek we acquired genetic diversity values for 77 Baltic population samples from Carlsson and Carlsson (2002) Carlsson and Nilsson (2000) Lehtonen et al. (2009) Nilsson et al. (2008) Östergren et al. (2015) Samuiloviene et al. (2009) Was and Bernaś (2016), and Was and Wenne (2002) (Fig. 1, Tab. 2). We obtained the mean number of alleles (n = 34), the mean AR (n = 55), Hobs (n = 35) and Hexp (n = 77) per sample.

For each specimen we derived a likelihood function of the individual inbreeding coefficient F and estimated a mean F by randomly sampling 1000 F-values from the distribution of the probability density from this function. Further, we estimated the pairwise relatedness over all loci Mxy (Blouin et al., 1996) between all specimens. We used ‘adegenet’ v. 1.3-9.2 (Jombart, 2008), ‘hierfstat’ v. 0.04-10 (Goudet, 2013) and ‘Demerelate’ v. 0.9-3 (Kraemer and Gerlach, 2017) in R v. 3.0.2 (R Core Team, 2013).

MtDNA CR sequences were aligned and assigned to previously published haplotypes (Bernatchez, 2001; Bernatchez et al., 1992; Cortey and Garcia-Marin, 2002; Duftner et al., 2003; Kohout et al., 2012; Weiss et al., 2001) and major mtDNA lineages (Bernatchez, 2001; Bernatchez et al., 1992) using the Geneious 6.0 software (Biomatters). Haplotype diversity h was estimated as h = n/(n−1)(1−∑xi2), with sample size n and frequency of haplotypei xi (Nei and Tajima, 1981). For comparison we obtained or calculated haplotype diversities from Cortey and Garcia-Marin (2002; n = 10), Duftner et al. (2003; n = 5), and Kohout et al. (2012; n = 29) for 44 populations with at least 10 specimens genotyped.

Table 2

Number of population samples, mean number of specimens per sample, total number of specimens, number of analyzed microsatellites (NMS), river and sea basin of sample origin, and years of sampling from eight studies from which genetic diversity data were acquired for comparison with our results on the Virgulica creek brood stock sample. Comparisons of diversity values per sample are shown in Figure 2.

3 Results

The locus Ssa 417 UOS could not be amplified in 17 samples (68%), while for all other loci percentage of missing data was within an acceptable range (≤12%). Loci Ssa 417 UOS and OMM 1323 were 100% monomorphic, and thus rejected, so that further analyses included allelic data from 25 individuals at 10 polymorphic loci. No evidence for null alleles was found.

For the Virgulica stock the mean number of alleles was 3.40 (SEM 0.43) and the mean AR was 3.80 (SEM 0.04). The mean Hobs was 0.52 (SEM 0.02) and the mean Hexp was 0.53 (SEM 0.05). Table 3 shows detailed characteristics per locus. All four diversity values were in the lower quartile of the respective values obtained from other studies (Fig. 2). The mean number of alleles in the reference samples ranged from 3.2 to 8.0 (median 4.72, mean 4.75, SEM 0.23) and the mean AR from 3.26 to 8.57 (median 4.61, mean 5.28, SEM 0.20). The range of Hobs was 0.39 to 0.80 (median 0.64, mean 0.62, SEM 0.02) and 0.47 to 0.75 (median 0.66, mean 0.65, SEM 0.01) for Hexp.

The mean difference of Hexp−Hobs was 0.01 (SEM 0.43), mean FIS was 0.04 (SEM 0.56) and significant deviation from HWE was observed at locus Ssa410UOS (Tab. 3). The genetic diversity within the population Hs was 0.53.

Estimates of mean F ranged from 0.147 (sample ID: F1459) to 0.53 (F1464). The mean F of 20 specimens were below 0.33, slightly exceeded 0.4 for two specimens (0.41; F1456, F1461) and were higher than 0.5 for another three (0.53; F1464, F1467, F1473) (Supplemental Information Tab. S2). Figure 3 shows a graphical representation of the likelihood functions of F. At population level, the mean of individual F–values was 0.26 (SEM 0.03). Pairwise relatedness Mxy ranged from 0.25 (F1461–F1468) to 0.83 (F1464–F1470) with a mean of 0.53 (SEM 0.0065) (Fig. 4).

Based on a comparison of 247 bp of haplotype At-s1 (310 bp; GenBank accession number M97969; Bernatchez, 2001; Bernatchez et al., 1992) and based on 401 bp to haplotype At1 (464 bp; AF321990; Weiss et al., 2001) all mtDNA CR sequences were identical. A comparison of the full 946 bp segments assigned eight specimens to haplotype H2 (1012 bp; AF273087) and two specimens to haplotype H3 (1012 bp; AF274574) in Cortey and Garcia-Marin (2002). The haplotypes H2 and H3 are identical to the haplotypes At1b and At1d in Duftner et al. (2003). A third haplotype, represented by one specimen, was not found in any previous study. It differs from haplotypes At1b and At1d by one mutation at nucleotide position 527 (Tab. 4). This sequence was named At1q, following the attempt of Duftner et al. (2003) to standardize haplotype nomenclature, and deposited in GenBank (KT360957).

Additionally, we compared haplotypes At1b, At1d, and At1q to a 285 bp segment at the 3' end of the CR associated to haplotype At1 (328 bp; M97968; Bernatchez et al., 1992). Haplotype At1d differs at three positions from At1, while haplotypes At1b and At1q, which are identical in this segment, differ at four positions. Differences include two insertions/deletions in either of these cases (Tab. 4).

The estimated haplotype diversity h was 0.47, and thus within a medium range: It is between the lower quartile and the median of haplotype diversities found in populations from earlier studies (n = 44, range 0.00 to 0.90, mean 0.53, SEM 0.04; Fig. 5).

Table 3

Genetic diversity values of the n = 25 specimens determined per locus. Bold numbers indicate significant (p < 0.0001) deviations from Hardy–Weinberg equilibrium.

thumbnail Fig. 2

Frequency (bars) and distribution (boxplots) of the genetic diversity values a) mean number of alleles, b) mean allelic richness, c) observed heterozygosity, and d) expected heterozygosity of 77 samples of Baltic brown trout from Carlsson and Carlsson (2002), Carlsson and Nilsson (2000), Lehtonen et al. (2009), Nilsson et al. (2008), Östergren et al. (2015), Samuiloviene et al. (2009), Was and Bernaś (2016), and Was and Wenne (2002) (Tab. 2). Asterisks and dashed lines mark the respective values of the Virgulica creek brood stock of which n = 25 specimens were examined in the present study (a: 3.40, b: 3.80, c: 0.52, d: 0.53).
thumbnail Fig. 3

Likelihood functions of the inbreeding coefficient F of the n = 25 specimens. Dashed lines indicate specimen with mean F-values >0.4 and solid lines specimen with mean F-values <0.4. Note that the maxima of the probability density for the specimens F1456 and F1461 are in the same order of magnitude (∼0.4), while they are higher for specimens F1464, F1467, and F1473 (∼0.6) compared to mean F (Supplemental Information Tab. S2).
thumbnail Fig. 4

Frequency of pairwise relatedness Mxy between all analyzed specimens from Virgulica creek brood stock. Dashed line indicates the mean (0.53, SEM 0.0065) of all Mxy.
Table 4

Variable base positions among the three haplotypes of the n = 11 specimens based upon 946 bp of the mtDNA CR and additionally a 285 bp segment of the 3' end of haplotype At1 (328 bp; acc. no. M97968; Bernatchez et al., 1992). Nucleotide positions are numbered according to the reference sequence ‘haplotype 2’ (AF273087; Cortey and Garcia-Marin, 2002). Identity with the reference sequence is indicated with . and indels are marked with −. Number (N) and relative frequency (Freq.) of each haplotype is given.

thumbnail Fig. 5

Frequency (bars) and distribution (boxplot) of haplotype diversity h of 44 samples of brown trout from Cortey and Garcia-Marin (2002; n = 10), Duftner et al. (2003; n = 5) and Kohout et al. (2012; n = 29). Asterisks and dashed line mark the haplotype diversity of the Virgulica creek brood stock (0.47).

4 Discussion

The comparison of genetic diversity values of Baltic brown trout populations based on neutral nuclear markers overall revealed that the diversity of the Virgulica creek brood stock is rather low. Low genetic diversity is often regarded as a warning signal that a population might be or become threatened by increased inbreeding or deleterious genetic drift (e.g. Naish et al., 2013 and citations therein). Thus, maintenance or establishment of high levels of genetic diversity is a common aim of conservation efforts (Saura and Faria, 2011). Nevertheless, low genetic diversity may occur in small, wild salmonid populations without preventing survival and adaption (Pujolar et al., 2016) and citations therein), so that the comparatively low genetic diversity of the Virgulica brood stock itself is not necessarily a major concern.

Direct comparisons of genetic diversity between microsatellite based studies might be affected e.g. by the selection of different markers (Ryman et al., 2006). However, this effect should be reduced at population level by averaging over a number of loci with different levels of polymorphism. The number of loci in the studies used here for comparison ranged from 5 to 14 (mean 8.1, SEM 0.25; Tab. 2). However, the necessary number of loci is disputable (Selkoe and Toonen, 2006). Further, rare alleles might be missed because of low sampling sizes, which also makes comparisons between studies difficult. Our sample of the Virgulica creek brood stock is at the lower range of sampling sizes per population used for comparison (Tab. 2). This might partly explain the comparatively low diversity found here. However, measured by the low number of 50 specimens in the parental generation, we believe that our sample is representative for the brood stock. The range of genetic diversity values compared here, might in part reflect the ecological range of Baltic brown trout (e.g. effective population sizes, life history traits, isolation, or population history, like bottlenecks), so that overall, despite methodological difficulties, we believe that this approach is helpful in providing a context for the further assessment of the Virgulica creek population.

The mean difference of Hexp−Hobs, the mean FIS, and deviations from HWE, provide better evidence for inbreeding than above genetic diversity values. Both, mean Hexp−Hobs and FIS, were nearly zero. Significant deviation from HWE was found only at a single locus. Thus, all three values did not indicate significant inbreeding at the population level. In contrast we have found certain indications for inbreeding at the individual level, i.e. mean F > 0.4, in 5 specimens (20%). We consider this indication as weak (0.4 < F ≤ 0.5) for two (8%) specimens and as reasonable (F > 0.5) for three (12%) specimens. Higher values of F in first generation offspring may be explained by kinship within the sample or the parental generation. The mean, range and frequency distribution of Mxy in the brood stock sample match the expectations for full sibs (or parent–offspring pairs). Our results come very close to the findings of Blouin et al. (1996) for a breed of full siblings from wild parents in mice (Mus musculus). Thus, our sample apparently includes mostly closely related specimens. The spawners of the brood stock were mainly sampled from just several stretches of a rather small section of Virgulica creek. Thus, already this sample may have contained (half) siblings. By mixing sperm and eggs of several spawners the risk of producing offspring exclusively from one pair of siblings was minimized. However, mating of (half) siblings may also occur in the wild, especially in headwaters where effective population sizes are typically low (Hansen and Jensen, 2005). Also, at the population level, mean F appears uncritically low. Thus, we believe that the proportion of specimens with indications of reasonable inbreeding of less than 15% is acceptable in a local breeding program (cp. Ruzzante et al., 2001). However, in a future perspective the Virgulica creek breeding program could benefit from conducting sib-avoidance matings to further delay inbreeding. Overall, our analyses of microsatellite data confirm that low genetic diversity itself is a rather insufficient indicator of inbreeding. Thus, aiming at increasing genetic diversity in conservation programs may not be simply justifiable by avoidance of inbreeding depressions.

Our analyses of the mtDNA CR have demonstrated that the brown trout population of Virgulica creek shows − as expected for the Daugava river basin − exclusively (northern) “Atlantic” haplotypes. Haplotypes At1b and At1d are the most common haplotypes in central and northern Europe. This is well reflected in our results: 91% of the specimens were assigned to either of them. One out of just eleven analyzed specimens, however, revealed a previously undescribed haplotype. This suggests that the Daugava river basin may hold undetected genetic diversity of brown trout. This assumption is supported by the variation at the 3' end of the CR compared to haplotype At1 (sensu Bernatchez et al., 1992). Such previously undetected diversity in understudied regions is potentially phylogeographically relevant and lastly important to establish appropriate conservation strategies at a larger scale (e.g. Cortey and Garcia-Marin, 2002; Schenekar et al., 2014). Our study provides a first basis towards future conservation strategies for brown trout in the Daugava river basin.

From an applied perspective, our study shows the importance of comparing genetic diversity data between studies to better evaluate values measured in a single population of interest. This comparison revealed, that the genetic diversity of the Virgulica creek brood stock is relatively low. Analyses of inbreeding showed, that − despite overall low diversity − the supportive breeding procedures applied in the Virgulica creek program appear appropriate to conserve the valuable genetic diversity of brown trout at a local scale.

Supplementary Material

Supplementary tables. Access here

Acknowledgements

We are grateful to B. Wahl-Ermel, K. Mäck, and M. Wetjen for their support in the lab work and to an anonymous reviewer for comments on an earlier version of this manuscript.

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Cite this article as: Schmidt T, Zagars M, Roze A, Schulz R. 2017. Genetic diversity of a Daugava basin brown trout (Salmo trutta) brood stock. Knowl. Manag. Aquat. Ecosyst., 418, 55.

All Tables

Table 1

Primer concentrations, batches, size ranges and references of the microsatellite loci.

In the text
Table 2

Number of population samples, mean number of specimens per sample, total number of specimens, number of analyzed microsatellites (NMS), river and sea basin of sample origin, and years of sampling from eight studies from which genetic diversity data were acquired for comparison with our results on the Virgulica creek brood stock sample. Comparisons of diversity values per sample are shown in Figure 2.

In the text
Table 3

Genetic diversity values of the n = 25 specimens determined per locus. Bold numbers indicate significant (p < 0.0001) deviations from Hardy–Weinberg equilibrium.

In the text
Table 4

Variable base positions among the three haplotypes of the n = 11 specimens based upon 946 bp of the mtDNA CR and additionally a 285 bp segment of the 3' end of haplotype At1 (328 bp; acc. no. M97968; Bernatchez et al., 1992). Nucleotide positions are numbered according to the reference sequence ‘haplotype 2’ (AF273087; Cortey and Garcia-Marin, 2002). Identity with the reference sequence is indicated with . and indels are marked with −. Number (N) and relative frequency (Freq.) of each haplotype is given.

In the text

All Figures

thumbnail Fig. 1

Location of the Virgulica creek (red cross) in Latvia and approximate locations of non-hatchery samples from Carlsson and Carlsson (2002), Carlsson and Nilsson (2000), Lehtonen et al. (2009), Nilsson et al. (2008), Östergren et al. (2015), Samuiloviene et al. (2009), Was and Bernaś (2016), and Was and Wenne (2002) (Tab. 2). Locations for Östergren et al. (2015) are indicated at the respective river outlets.
In the text
thumbnail Fig. 2

Frequency (bars) and distribution (boxplots) of the genetic diversity values a) mean number of alleles, b) mean allelic richness, c) observed heterozygosity, and d) expected heterozygosity of 77 samples of Baltic brown trout from Carlsson and Carlsson (2002), Carlsson and Nilsson (2000), Lehtonen et al. (2009), Nilsson et al. (2008), Östergren et al. (2015), Samuiloviene et al. (2009), Was and Bernaś (2016), and Was and Wenne (2002) (Tab. 2). Asterisks and dashed lines mark the respective values of the Virgulica creek brood stock of which n = 25 specimens were examined in the present study (a: 3.40, b: 3.80, c: 0.52, d: 0.53).
In the text
thumbnail Fig. 3

Likelihood functions of the inbreeding coefficient F of the n = 25 specimens. Dashed lines indicate specimen with mean F-values >0.4 and solid lines specimen with mean F-values <0.4. Note that the maxima of the probability density for the specimens F1456 and F1461 are in the same order of magnitude (∼0.4), while they are higher for specimens F1464, F1467, and F1473 (∼0.6) compared to mean F (Supplemental Information Tab. S2).
In the text
thumbnail Fig. 4

Frequency of pairwise relatedness Mxy between all analyzed specimens from Virgulica creek brood stock. Dashed line indicates the mean (0.53, SEM 0.0065) of all Mxy.
In the text
thumbnail Fig. 5

Frequency (bars) and distribution (boxplot) of haplotype diversity h of 44 samples of brown trout from Cortey and Garcia-Marin (2002; n = 10), Duftner et al. (2003; n = 5) and Kohout et al. (2012; n = 29). Asterisks and dashed line mark the haplotype diversity of the Virgulica creek brood stock (0.47).
In the text

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