Long-term and seasonal genetic differentiation in wild and enhanced stocks of sea trout (Salmo trutta m. trutta L.) from the Vistula River, in the southern Baltic—Management implications

Fisheries Research 175 (2016) 57–65

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Long-term and seasonal genetic differentiation in wild and enhanced stocks of sea trout (Salmo trutta m. trutta L.) from the Vistula River, in the southern Baltic—Management implications ˛ a,∗ , Rafał Berna´s b Anna Was a b

˛ National Marine Fisheries Research Institute, Department of Fisheries Resources, Kołłataja 1, 81-332 Gdynia, Poland Inland Fisheries Institute, Department of Migratory Fishes, Synów Pułku 37, 80-298 Gda´ nsk, Olsztyn, Poland

a r t i c l e

i n f o

Article history: Received 25 May 2015 Received in revised form 28 October 2015 Accepted 5 November 2015 Keywords: Temporal genetic structure Sea trout Microsatellites Vistula River

a b s t r a c t The Vistula River, rich with sea trout (Salmo trutta m. trutta L.) is the longest river in Poland, flowing into the southern Baltic Sea, with its population sustained mainly by enhancement programmes. The population source was declined and the proportion of Vistula winter and summer sea trout stocks existing in the river was drastically changed over a few decades. This paper is the first detailed genetic study of stocks of the species in the river. Five samples representing wild winter (1971), enhanced winter (2010/11) and summer (2003), and hatchery winter (2003) sea trout stocks were analysed. Twelve microsatellite loci were employed to investigate long-term and seasonal genetic differentiation affected by strong anthropogenic activities and thence to assist fish stock management in the Vistula River. The genetic structure of past and current stocks was observed. The substructure of the sea trout stocks, showing different spawning behaviour, was also exhibited. The drop of private alleles and bottleneck effect were noted, pointing the decrease of Vistula sea trout genetic sources in last 40 years. The pronounced differentiation (pairwise Fst values from 0.024 to 0.057) and high full-siblings share (68%) recorded for the hatchery stock, being the only source for supplementary stocking of winter sea trout stock, imply that this source should be used with caution and action to improve, and subsequently maintain, conditions for natural spawning should be prioritized. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Sea trout (Salmo trutta m. trutta L.) is a salmonid fish, an anadromous form of brown trout. Fish spawn in freshwater streams and rivers, with juveniles generally staying 0–6 years in their natal river, although only 1–3 years for those in Polish rivers. After smoltification, fish migrate into the sea for feeding where adults reach maturity at 2–4 years and are ready to return to the river for spawning, following homing patterns (Fahy, 1978; L’Abee-Lund et al., 1989; Elliott, 1994). Sea trout has great economic importance not only for fisheries but also for anglers and communities. Over five hundred self-sustaining populations already exist in the Baltic region, mainly in small and medium sized rivers (HELCOM, 2011a). The occurrence of sea trout in Poland is limited to some 25

∗ Corresponding author. Fax: +48 587 356 110. ˛ rber@infish.com.pl E-mail addresses: [email protected] (A. Was), (R. Berna´s). http://dx.doi.org/10.1016/j.fishres.2015.11.006 0165-7836/© 2015 Elsevier B.V. All rights reserved.

rivers with spawning habitats located mainly in the coastal areas (HELCOM, 2011b). The Vistula River is the longest river flowing into the Baltic Sea and has the second highest run-off after Neva River (BACC, 2015). The river contains both resident and sea-run forms of the species, and is the most abundant with sea trout in this area. During the last fifty years catches from this river have varied between 10 and 35 tonnes of sea trout annually. Sea trout from the Vistula is known for growing faster and reaching a larger size than other sea trout in the Baltic Sea (Bartel, 1988; Woznicki et al., 1999). In the river two seasonal stocks of the sea trout exist, summer and winter. Summer stock enters the river in late summer with spawning migration reaching a peak in September and October in lower ˛ River. Winter stock starts its jourtributaries as well as the Drweca ney later, from November to the following Spring trying to enter upper Vistula parts. Summer stock specimens show partial breeding colours, with enlarged or mature gonads and spend only a short time in freshwater, spawning the same year that they ascend the

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river. In contrast, winter stock individuals ascend the Vistula with immature gonads, have silver colouring and stay several months in ˙ the river before they spawn in the following year (Zarnecki, 1964; Bartel et al., 2010). Before the 1960s winter stock was more abundant than summer, and reached spawning grounds in the Carpathian Mountains, the upper reaches of the Vistula River system. Nowadays, the summer stock is dominant in the Vistula River basin. The winter stock seems to be in regress and exists only through compensatory stock˛ ing. There is evidence of the existence of winter stock in the Drweca, an important tributary in the middle run of the Vistula, where winter spent fish were caught (Bartel and Bontemps, 1995). Moreover, ˛ Borzecka (2003) confirmed the presence of winter sea trout by ˛ from investigating scales from individuals collected in the Drweca 1988 to 1992. Generally Salmo trutta L. reproduces in autumn or winter; the earlier the higher the latitude and altitude because of lower water temperature and longer egg incubation period (Klemetsen et al., 2003). However, the effect of environmental factors on salmonid migration seems also to vary with the size of the river and geomorphology (Trépanier et al., 1996; Thorstad et al., 2008). In the past, the Vistula had a large population of sea trout which collapsed after the Włocławek dam was constructed in 1968. From that time matured, migrating fish have been observed above the dam only occasionally and natural spawning occurs mainly in the ˛ Drweca and short accessible stretches of the river in its lower course. The stocking activity in Vistula basin has long history and started in the end of nineteenth century. However these practices were occasional and based on small numbers of specimens. More regular stocking was performed from 60s last century and number of released sea trout gradually increased (Bartel et al., 2007). Regular intensive enhancement programme, mainly based on sea trout smolt stocking, has been conducted since the 1990s. Also from beginning of 90s it is obligatory in Poland that sea trout juveniles from artificial reproduction are released only to their parental river, which reduced the mixing of the gene pool of fish from different populations what was occurred in the past (Bartel, 1993). A large number of smolts (around 700,000 annually) have been released at the river mouth and in the lower run. Also some 400,000 sea trout parr are released annually, predominantly into the upper part (HELCOM, 2011b). Mainly spawners caught in the river are used for artificial reproduction and for the breeding programme to support the releases of summer stock. In contrast, the production of winter stock smolts and parr is based on hatchery resources. Over the last few decades, main river catches in the Vistula have come from large-scale supportive breeding. Despite the very high abundance of the sea trout in the river, and a very high level of anthropogenic activity, detailed studies on the genetic polymorphism of the Vistula sea trout are rare (Was and Wenne, 2002, 2003; Drywa et al., 2013) To date, knowledge of the genetic structure of species within the river system has been neglected. Furthermore, a temporal comparison of genetic differentiation, including the period before stocking activities has not been available. The aim of this study was to meet the need for research on the genetic structure of Vistula River sea trout population in order to assess the spatial dislocation of spawning grounds of winter and summer sea trout and the effects of strong anthropogenic activities during last forty years on genetic diversity. A further aim was to examine polymorphism in the hatchery stock of winter sea trout originating from the Vistula River and the extent of its conservation in the hatchery at Miastko. Such an assessment would enable the evaluation of the suitability of this stock for use in a large-scale, supportive breeding and release programme for the Vistula winter stock.

2. Material and methods 2.1. Sample collections Key to samples abbreviation: S—summer, W—winter, w—wild, ´ ˛ River, S—caught e—enhanced, h—hatchery, D—caught in Drweca ´ close to the mouth of the Vistula River in Swibno. Two samples of sea trout representing the winter stock of the Vistula River, from earlier (before stocking) and present population, were collected by fishermen. Fish for the first sample were caught in the Vistula River nearby the Włocławek dam in May 1971 (Ww1971) but only dried scales were preserved for analysis (archive sample). The second sample was composed of fish caught around Tczew (in the lower reaches of the Vistula at Tczew, 180 kms below the dam) in December–February 2010/2011 (We2010/11). An additional hatchery sample, composed of descendants of winter stock sea trout originating from the Vistula River, was sampled in the hatchery farm ‘Aquamar’ in Miastko in November 2003 (Wh2003). This breeding stock is used for stocking of the Vistula River as the only source of sea trout winter stock. To complete a sample set, sea trout of summer stock from the Vistula River close ´ and from the entrance of the Drweca ´ ˛ (Se2003S) River to Swibno (Se2003D), were collected in November 2003 (Fig. 1; Table 1). Fish sampled in 2003 in the rivers and in the hatchery farm were ready for spawning or in spawning process (in stages 5, 6 and 7 of gonad maturation—according to the Maier’s scale; FAO 1965). Two other river-caught samples (Ww1971, We2010/11) collected in spring 1971 and winter 2010/2011 were composed of adult fish (length and weight of fish exceeded 60 cm and 3 kg) but without breeding colours and potentially ready to spawn in the following year; a typical attribute of winter stock sea trout present in the river. There was no possibility to collect appropriate sample from historical summer sea trout stock. 2.2. Microsatellite data Genomic DNA was extracted from muscle tissue preserved in 96% ethanol, or archived scales (6 per fish), using Genomic Mini Kit—A&A Biotechnology or Bio-Trace DNA Purification Kit—EURx, Poland respectively, and diluted to a concentration of 30–100 ng ␮l−1 . A set of twelve polymorphic microsatellite loci (OneU 9, Strutta 58P, Ssosl 438, Ssosl 311, Str 15INRA, Str 543INRA, Str 60INRA, Str 73INRA, Ssosl417, Str 85INRA, Ssa 85, Bs 131) was applied to perform a single multiplex-PCR using Qiagen Multiplex PCR Kit (Qiagen, Germany). The 7 ␮l multiplex PCR reaction consisted of ca. 100 ng of template DNA, 1× multiplex PCR master mix and 0.2–0.6 ␮M of each primer. Amplifications were carried out in a TProfessional Basic Gradient thermal cycler (Biometra) with an initial heat-activation at 95 ◦ C for 5 min followed by 38 cycles of denaturation at 94 ◦ C for 30 s, annealing at 55 ◦ C for 90 s and extension at 72 ◦ C for 60 s. The PCR was terminated after 30 min of final extension at 60 ◦ C. The reversed primers were labelled fluorescently in turn, forward ones had a ‘GTTT’ tail added to the 5 end for enhancing the adenylation of the nascent DNA strand to facilitate accurate genotyping (Brownstein et al., 1996). For the archived sample (dried scales from 1971) only seven loci, with sequences shorter then 200 bp, were successfully co-amplified using the same single multiplex-PCR, due to the low-quality DNA template. PCR products were sized in a single capillary electrophoresis on an ABI Prism 3130xl genetic analyser (Applied Biosystems) along with GeneScan 600LIZ size standard (Applied Biosystems). DNA fragments were genotyped using Peak Scanner Software v1.0 (Applied Biosystems) twice by two different people, to cross-check data. Details regarding locus sources, concentrations and labelling of primers, as well number and size range of alleles or quality of

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Fig. 1. Sampling locations for sea trout of Vistula River winter (circles) and summer stocks (triangles) collected in the period 1971–2011; Ww1971 (winter wild stock 1971), We2010/11 (winter enhanced stock 2010/11), Wh2003 (winter hatchery stock 2003), Se2003D (summer enhanced stock 2003 from Drweca), Se2003S´ (summer enhanced ´ ´ ˛ River, S—caught Key to samples abbreviation: S—summer, W—winter, w—wild, e—enhanced, h—hatchery, D—caught in Drweca close to the mouth stock 2003 from Swibno) ´ of the Vistula River in Swibno.

co-amplification, are provided in the Supplementary material (see App. 1). 2.3. Data analysis All computations and tests were performed for five samples using 7 loci, and for four samples (excluding the archive sam-

ple Ww1971) using 12 loci. The use of 12 loci was to ensure the reliability of analysis results based only on seven loci (the only data available for the archive sample). Allelic distribution, private alleles, observed (HO ) and unbiased, expected (HE ) heterozygosity estimates (Nei, 1978) for all samples were computed for each locus individually, and as a multilocus estimate using GenAIEx 6.4. (Peakall and Smouse, 2012). Conformance to Hardy–Weinberg

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11 3/5 1/2 1/5 1/4

PrAl FIS

0.23 0.051/0.066 −0.096/-0.081 0.058/0.034 −0.007/−0.009 0.67 0.66/0.65 0.62/0.61 0.65/0.65 0.65/0.65

HE AR

8.25 6.96/8.40 6.28/7.53 6.64/8.57 6.55/8.25 69(NA) Dried scale 75(NA) Preserved tissue 96(80/16) Preserved tissue 110(99/11) Preserved tissue 139(119/20) Preserved tissue Winter wild Winter enhanced Winter hatchery Summer enhanced Summer enhanced Vistula River–Włocławek dam Vistula River–Tczew Hatchery farm (Aquamar–Miastko) ˛ River–Lubicz Drweca ´ Vistula River–Swibno May 1971 Dec–Feb 2010/2011 Nov 2003 Nov 2003 Nov 2003 Ww1971 We2010/11 Wh2003 Se2003D Se2003S´

Matured Matured Spawning Spawning Spawning

Diversity (for 7/12loci) Sample size (F/M)/Material Stock Phase Location Sampling date Sample

Table 1 Sample information for sea trout of Vistula River winter and summer stocks collected in the period 1971-2011. AR —allelic richness (based on 66 or 74 re-sampling diploid individuals for 7 and 12 loci, respectively); HE —expected heterozygosity; FIS —inbreeding coefficient; P < 0.05 after Bonferroni correction in bold; PrAl—private alleles (unique to single population).

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equilibrium (HWE) was assessed using the procedure of Guo and Thompson (1992) in GENEPOP 3.4 (Raymond and Rousset, 1995), with specified Markov chain parameters of 10,000 dememorization steps, followed by 100 batches of 5000 iterations per batch for all loci and samples. Tests for linkage disequilibrium between pairs of loci were also performed using the same program and default settings listed above. Allelic richness, with the rarefaction method employed to correct for variation in a sample of allelic richness (Hurlbert, 1971), and single and multilocus FIS , indicating heterozygote deficiency/excess (Weir and Cockerham, 1984) were estimated using FSTAT 2.9.3.2 (Goudet, 2001). The significance of all FIS estimates was tested with 4800 (12 loci) or 35,000 (7 loci) randomizations. The test for population contemporary bottlenecks with the, standardized differences test for excess heterozygosity was carried out in BOTTLENECK (Piry et al., 1999). Since crossbreeding is random the risk of full siblings cross-breeding increases in communities with a high level of relatedness. Therefore, Colony 2.0 (Wang, 2004) was used to identify related individuals/spawners within the hatchery as well as other samples based on 7 loci. The programme enabled the comparison of the level of relatedness inside the wild, the enhanced, and the hatchery samples used for artificial spawning for supportive stocking purpose. Colony 2.0 was further used to exclude genetic estimate bias appearing in situations where individuals of full-sib families composed the estimated populations (Hansen et al., 1997), and to compare genetic structure and genetic relationship among populations when individuals of full-sib families were omitted from analyses. To precisely asses the number of full-sib families this program was set to have a typing error rate in the data set = 0 (no error) and 0.0001, for comparative purposes. Population differentiation was analysed using global and pairwise FST (Weir and Cockerham, 1984) in FSTAT2.9.3.2, and Fisher’s exact tests of genic and genotypic distributions (Raymond and Rousset, 1995) between pairs of populations (single and multilocus) in GENEPOP 3.4. The significance of FST estimates was tested with 1000 permutations and for Fisher’s exact test, default settings were used. To correct for multiple pairwise comparisons, Bonferroni correction was applied (Rice, 1989). Clustering analysis was conducted in STRUCTURE v. 2.3.3 (Pritchard et al., 2000). To assess population clusters and membership validity, an admixture model with 5 replicates was applied for each number of partitions/clusters permitted in the analysis (K) using 500,000 iterations with a burnin period of 250,000 iterations. K was expected from 2 to 6 and the optimal value of K was identified following the delta K method (Evanno et al., 2005) using STRUCTURE HARVESTER (Earl and von Holdt, 2011) (Earl and von Holdt, 2012). To determine membership probability for clusters across the 5 runs CLUMPP v.1.1.2 (Jakobsson and Rosenberg, 2007) with FullSearch algorithm and 10,000 random permutations was applied. Visualization of assignments to clusters was performed in DISTRUCT v. 1.1 (Rosenberg, 2004). The genetic relationship among samples was also examined using multidimensional principal coordinate analysis implemented in FAMD 1.31 (Schlüter and Harris, 2006).

3. Results 3.1. Microsatellite diversity, Hardy–Weinberg equilibrium and genotypic linkage disequilibrium The average values for allele richness, based on computation of 7 loci (66 specimens), and 12 loci (74 specimens), varied from 6.28 (Wh2003) to 8.25 (Ww1971) and from 7.53 (Wh2003) to 8.57 (Se2003D) respectively. Twenty seven private alleles for all samples and for 12 analysed loci were observed. Most of them appeared with a frequency 0.02 or lower (Table 1). While their presence was proportional to the number of analysed loci (17 private alleles were

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Fig. 2. Relatedness inside wild (Ww1971), enhanced (We2010/11) and hatchery (Wh2003) stocks of Vistula winter sea trout. ♦/F—full-sib individuals (above the diagonal); /H—half-sib individuals (below the diagonal); N—number of analysed fish; on the axes of charts individuals are pointed.

Table 2 Result from bottleneck tests. Standardized difference test computed in BOTTLENECK 1.2.02 with three mutation models: IAM (infinite alleles model), TPM (two-phase model) and SMM (stepwise mutation model). Significant values are bolded P < 0.05. Samples

Ww1971 We2010/11 Wh2003 Se2003D Se2003S´

Stand. difference test IAM

TPM

SMM

0.0139 0.1754 0.0992 0.0085 0.0443

0.2960 0.0753 0.2664 0.1551 0.4852

0.0041 0,0000 0,0000 0.0175 0.0000

observed for the set of 7 loci), they were most abundant (11) in the sample collected in the Vistula River in 1971 (Ww1971). The linkage disequilibrium test indicated non-random association of alleles in 64 locus pairs (22.45% from the total of 285 pairs; P < 0.05). However, over 87% of these were statistically significant in the hatchery sea trout sample (56 of 64 tests). The expected heterozygosity was similar throughout the analysed samples, and ranged from 0.61 in Wh2003 to 0.67 in Ww1971 (Table 1). Two samples (Wh2003, ´ exhibited significant deviation from Hardy–Weinberg Se2003S) equilibrium across loci after Bonferroni correction. But FIS estimates were significant only for the hatchery sample: −0.096 and −0.081 (for 7 and 12 loci, respectively) (Table 1). 3.2. Bottleneck effect and relatedness inside stocks Significant contemporary bottleneck estimates were found in all studied samples (8 of 15 tests). Using standardized difference test, t bottleneck effect was observed for all analysed samples on SMM model and for three with IAM model (Table 2). The highest level of relatedness was inferred for the hatchery stock (Wh2003). In this sample composed of 95 fish, only 36 families were pointed, with 65 full-siblings assigned to 6 full-sib families (with members ≥2). In turn, for enhanced stocks (We2010/11,

´ all family numbers varied from 67 to 127 Se2003D, Se2003S) and from only 15–22 full-siblings per sample were observed. The wild fish sample, caught in the Vistula River in 1971 (Ww1971), exhibited the lowest level of relatedness within sample. Only 9 full-siblings and 4 full-sib families were computed by Colony 2.0. Furthermore, the wild Vistula sample, in contrast to others, showed 64 families in a sample composed of only 69 individuals. (Table 3, Fig. 2, App. A2).

3.3. Genetic differentiation and relationships between stocks The global FST across all analysed samples was low, 0.018 and 0.023 for 12 and 7 analysed loci, respectively. However, even for analyses based only on 7 loci, genetic differentiation was significant for all but two of the samples pairs: the sample of sea trout winter stock collected in the Vistula River in 2010/11 versus the ˛ River 2003 sample (We2010/11–Se2003D), and two samDrweca ´ (Table 4). When 12 loci ples collected in 2003 (Se2003D–Se2003S) were analysed the FST estimate for the first pair (winter vs. summer) appeared statistically significant in line with other groups (FST = 0.004; P < 0.002), but the river-collected samples of 2003 ´ remained homogenous. (Se2003D–Se2003S) The highest level of differentiation was observed between hatchery stock (Wh2003) and the others, FST ranged from 0.057 to 0.024 (Table 4). The archived sample also differed considerably from other samples (the lowest FST value was 0.022). The results from exact tests of genic and genotypic proportions for seven and twelve loci were congruent with pairwise multilocus FST estimates. Bayesian analysis in STRUCTURE conducted for 7 loci and 5 samples suggested the existence of two separate groups, where the hatchery sample was distinguished (in Evanno table K = 2; K value = 38.44) (Fig. 3). However, tests also indicated a small peak for K = 4 with K value = 6.67 suggesting some level of deeper structuring. In this interpretation, samples collected in 2003 from the ´ and the Drweca ˛ mouth of the Vistula (Se2003S) River (Se2003D)

Table 3 Details of relatedness within wild, enhanced and hatchery stocks of sea trout from the Vistula River. Samples (no. of fish)

Families

Full-sib families with members ≥2

Full-siblings

Full-siblings/ Specimens [%]

Diads of full-siblings

♂/♀ in full-sib families

Ww1971 (69) We2010/11 (75) Wh2003 (95) Se2003D (110) Se2003S´ (138)

64 67 36 99 127

4 7 6 8 11

9 15 65 19 22

13 20 68 17 16

6 9 1162 15 11

NA NA 62/3 18/1 18/4

with members ≥2

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Table 4 Pairwise FST estimates computed for the sets of 7 loci/5samples below the diagonal and for 12 alleles/4samples above the diagonal; **—statistically significant (after Bonferroni correction P < 0.001 for 5 populations; P < 0.002 for 4 populations). Sample

Ww1971

We2010/11

Wh2003

Se2003D

Se2003S´

Ww1971 We2010/11 Wh2003 Se2003D Se2003S´

0 0.0233** 0.0575** 0.0220** 0.0207**

– 0 0.0308** 0.0031 0.0075**

– 0.0241** 0 0.0424** 0.0375**

– 0.0041** 0.0347** 0 0.0001

– 0.0066** 0.0317** 0.0000 0

Fig. 3. Population structure of Vistula sea trout based on Bayesian clustering analysis (STRUCTURE) for the sets of 7 loci/5samples. Computations from 5 independent runs were treated in CLUMPP and plotted with DISTRUCT. Upper bars represent results for population level where each colour corresponds to the number of suggested populations (K = 2) and the lower shows individual levels where each line correspond to an individual.

were assigned to the same cluster, but samples representing winter stock collected in 1971 and 2010/11 years, together with the hatchery winter sea trout, were grouped separately. A similar structure was observed when analyses were conducted for 12 loci for 4 samples omitting the historical sample (in Evanno table K = 2; K value = 205.64). The hatchery sample was distinguished and the sample from the winter stock collected in 2010/11 (We2010/11) was assigned to the same cluster with the summer Vistula samples ´ Se2003D) of 2003 (App. 3). (Se2003S, The genetic relationships among all 5 samples were also investigated using a multidimensional principal coordinate analysis (PCoA) applied to 7 loci on population level. Axis X accounted for 70.14 percent of the variability and Axis Y and Axis Z for 28.42 and 1.44 percent, respectively. Separation was sufficient to observe relations between groups. In particular, the contemporary sea trout samples: winter sample collected in 2010/11 (We2010/11) from the Vistula, with summer sea trout samples Se2003D and Se2003S´ were relatively close related (Fig. 4). The historical winter sea trout stock Ww1971 and the hatchery sample Wh2003 composed separate units (Fig. 4). 4. Discussion 4.1. Changes in genetic diversity The linkage disequilibrium test showed non-random association of alleles in 22.45%. This result, however, was an effect of a genetic estimates bias caused by the high number of closely related individuals in the hatchery sample. The analysed loci appeared not to be physically linked when full-siblings were removed from the analysis. Lack of random association of alleles for the same set of loci was reported by Swatdipong et al. (2010). The average allelic richness was comparable in all of the samples, except for the archive one (Ww1971) showing a considerably higher value (average of more than 20%). Similarly, private alleles were the most frequently observed in the archive sample (11 out of 17 based on 7 loci). The PCoA test also showed that the historical winter sea trout stock substantially differed from the current hatchery and enhanced stocks (Fig. 4). This indicates that the winter sea trout stock sampled in 1971 in the Vistula River had a unique genotype composition, different from that of the current summer or winter stock. Unfortunately, this uniqueness was lost as a result of the construction of the dam more than fish-stocking.

Both long-term stability and pronounced temporal heterogeneity have been reported for salmonids. (e.g. Nielsen et al., 1999; Hansen et al., 2002; Østergaard et al., 2003; Palm et al., 2003). The predominant factor determining temporal stability of allele frequencies at neutral loci is drift, and thereby effective population size (Ne) (Jensen et al., 2005). The construction of the dam in Włocławek in 1968 cut off the primary sea trout spawning grounds in the upper Vistula River system and caused a drastic reduction of the winter stock of sea trout. Before damming, the average annual catches in the river below the dam amounted to 33.3 tons of sea trout. After 1968, they dropped to 12.9 tons. The average catches above the dam were reduced from 14.7 before damming to only several kg following the construction (Bartel et al., 2007). This clearly resulted in a decrease in the effective population size (Ne), and consequently in the bottleneck effect. A part of less frequent alleles (approximately 30% of alleles observed in the archive sample, half of which were identified as private alleles) were not passed down to the next generations. This was confirmed by the results of the bottleneck test. It indicates that the demographic history and genetic diversity of sea trout from the Vistula River was affected before the period of intense restocking. It is also likely, however, that some of the rare alleles, currently identified as private alleles, were eliminated from the archived sea trout stock from the Vistula River over the following twenty years of restocking, or as a result of temporal changes. The hatchery stock was established in the 1990s as a reserve stock for the Vistula River winter sea trout stock. Over the last decade, the size of the stock has increased from 360 to more than 1000 females, and from 140 to 500 males. The increase in brood stock numbers was realized predominantly by using the offspring of hatchery fish and mating groups consisted of an sex-ration imbalance of 20–40 females and 4–10 males. This resulted in a reduction of the effective size of the hatchery population (Ne), an increase in genetic drift and higher probability of inbreeding (Wright, 1931). The lowest allelic richness and number of private alleles (2 out of 27) recorded in the hatchery stock support our assumption that the aforementioned fish-crossing procedure evidently led to a decrease in genetic diversity, also observed in the Słupia River in Pomerania (Berna´s et al., 2014). The high number of full-siblings (68%) in the stock, and the observed bottleneck effect additionally confirm the hypothesis. The negative effect of the construction of the dam and artificial spawning on the genetic diversity of sea trout in the Vistula River

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Fig. 4. Genetic relationship among stocks based on multidimensional principal coordinate analysis—PCoA for the sets of 7 loci/5samples. Diamonds represent single population. Thin vertical lines show Y axis ordination to zero.

was cumulative. It is also possible that in the hatchery stock, developed by the descendants of the winter stock present in the Vistula River in 1971, only a part of the Ww1971 stock gene pool was accumulated. The above could account for the fact that only a part of the original gene-pool of the wild winter stock of sea trout is preserved in the current hatchery stock.

4.2. Genetic structure of sea trout stocks in the Vistula River Three genetically distinct units were observed based on FST pairwise comparison tests and multidimensional principal coordinate

analysis. The winter sea trout stock existing in the Vistula River before intensive restocking (Ww1971) was genetically different from both current stocks, the winter and summer, and the hatchery stock was distinct from all of those. In turn, genetic differentiation between the winter and summer stock currently found in the Vistula River was very weak. The analyses confirmed the hypothesis of the homogeneity of samples Se2003S´ and Se2003D. This result is in accordance with the assumption considered during artificial spawning that fish trapped ˛ in the October/November represent a in the mouth of the Drweca summer stock.

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Although some FST pairwise differences for samples of current enhanced stocks were statistically significant (P<0.002), the values of the estimates were very low (0.004–0.008). Bayesian estimation conducted in STRUCTURE with two data sets (7 loci/5 populations and 12 loci/4 populations) produced congruent results regarding the current winter and summer stocks. In both of the models, the winter stock collected in 2010/11 was assigned to the same cluster as samples of the summer stock of sea trout from the Vistula River ´ Se2003D). It is possible that gene flow occurs between (Se2003S, winter and summer stocks. No evidence exist, however, of differences in the forms in historical time. There may have been gene flow between the spawning time forms always. It is also possible that the regulation of spawning time and observed certain phenotypic differentiation (e.g. size) are not reflected in the variation of neutral markers. The wild winter stock differed substantially from the current winter stock, however, it was the hatchery stock (used as a reserve stock for wild winter sea trout), which showed the highest interstock genetic differentiation. Even when closely related individuals from the analysed group were omitted, impractical here in a selfreproducing hatchery source, pairwise FST values were significant and the highest (ranged from 0.038 to 0.011). 4.3. Supplementary stocking effects and management recommendations Loss of genetic diversity, a shift in genetic structure and composition, or the decline of genetic adaptations resulting from artificial spawning (e.g. Araki et al., 2008; Allendorf et al., 2008) have been well documented, and their negative consequence is unquestionable. Many studies have also demonstrated the negative effects of barriers on salmonid populations, caused by restricted gene flow or declining population size (e.g. Meldgaard et al., 2003; Yamamoto et al., 2004; Hansen et al., 2014). In the Vistula River, such negative consequences for sea trout population are evident. The results of this study, covering more than 40 years under continuous anthropogenic pressure in the Vistula River, revealed the decline of genetic diversity and a change in long-term genetic differentiation of sea trout stocks. The primary causes of genetic changes are associated with the dam in Włocławek and artificial spawning, constituting outcomes of anthropogenic activity. Significant estimates of the bottleneck effect were determined for all of the samples. This suggests that the sea trout population from the Vistula River was affected by a reduction of population size already in the early 1970s. This was also reflected in substantial reduction of commercial catches following the construction of the dam in Włocławek (Bartel et al., 2007). Although the enhanced and hatchery stocks were particularly affected by restocking, the dam also contributed to the effect. In the case of the winter stock, the effects of the dam and restocking were cumulative. The genetic diversity of the summer stock was indirectly affected by the dam, causing or supporting gene flow from the genetically reduced source of winter sea trout. Temporal variation and local differentiation may have also contributed to the observed divergence. Although lack of short-time temporal sampling does not permit the determination of the effect of temporal variation on genetic diversity, a local differentiation effect was observed. Sample We2010/11 was similar to sample Se2003D, but it significantly differed from sample Se2003S´ collected in the same year. Considering the depleted genetic resources of sea trout in the Vistula River, low diversity and high inter-stock differentiation of the hatchery stock may constitute a problem. Using it as the exclusive source of winter stock through artificial spawning may be inadvisable.The allele richness was the lowest in the hatchery stock compared to the other stocks. Moreover, a high level of relatedness

causing genetic drift changed the internal structure of the stock, and strongly affected the genetic structure between the analysed stocks (see Fig. 4). Although inbreeding was not observed, its risk for future generations is high under such conditions. In order to prevent a reduction in the efficiency of spawning individuals and the weakening of the genetic potential of the hatchery population, an increase in matched spawning pairs and replenishment of the stock is recommended (Tave, 1993). Moreover, genotyping and tagging fish could help avoid crossing of related specimens. But primarily action to improve, and subsequently maintain, conditions for natural spawning should be prioritized. Acknowledgements This study was partially founded by project No.: ZPB/62/72380/IT2/10 by the National Centre for Research and Development, by the statutory topic NB-56 carried out in the National Marine Fisheries Research Institute and statutory topic S-25 in Inland Fisheries Institute in Olsztyn. Bayesian computations were done on clusters from PL-GRID framework. We gratefully acknowledge constructive comments and great improvements from the editor and two anonymous referees on an earlier version of the manuscript. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.fishres.2015.11. 006. References Allendorf, F.W., England, P.R., Luikart, G., Ritchie, P.A., Ryman, N., 2008. Genetic effects of harvest on wild animal populations. Trends Ecol. Evol. 23, 327–337. Araki, H., Berejikian, B.A., Ford, M.J., Blouin, M.S., 2008. SYNTHESIS: fitness of hatchery-reared salmonids in the wild: fitness of hatchery fish. Evol. Appl. 1, 342–355. BACC Author Team, 2015. Second assessment of climate change for the Baltic. In: Regional Climate Studies. Springer, Berlin. Bartel, R., 1988. Trout in Poland. Pol. Arch. Hydrobiol. 35, 321–339. Bartel, R., 1993. Present situation of the Vistula sea trout. Arch. Pol. Fish. 1, 101–111. Bartel, R., Bontemps, S., 1995. Possibility of natural spawning of Vistula sea trout ˛ river. ICES C.M. 1995/M:35. (Salmo trutta L.) in the Drweca Bartel, R., Wi´sniewolski, W., Prus, P., 2007. Impact of the Włocławek dam on migratory fish in the Vistula River. Arch. Pol. Fish 15, 141–156. Bartel, R., Pachur, M., Berna´s, R., 2010. Distribution, migrations, and growth of tagged sea trout released into the Vistula River. Arch. Pol. Fish. 18, 225–237. ´ ˛ ´ Berna´s, R., Burzynski, A., Debowski, P., Pocwierz-Kotus, A., Wenne, R., 2014. Genetic diversity within sea trout population from an intensively stocked southern Baltic river, based on microsatellite DNA analysis. Fish. Manag. Ecol. 21, 398–409. ˛ Borzecka, I., 2003. Characteristics of sea trout (Salmo trutta m: trutta) from the ˛ river based on scale samples collected between 1988–1992. Arch. Pol. Drweca Fish 11, 165–179. Brownstein, M.J., Carpten, J.D., Smith, J.R., 1996. Modulation of non-templated nucleotide addition by Taq DNA polymerase: primer modifications that facilitate genotyping. BioTechniques 20 (1004–1006), 1008–1010. ´ ˛ A., Dobosz, S., Kent, M.P., Lien, S., Berna´s, R., Drywa, A., Pocwierz-Kotus, A., Was, Wenne, R., 2013. Genotyping of two populations of Southern Baltic Sea trout Salmo trutta m. trutta using an Atlantic salmon derived SNP-array. Mar. Genomics 9, 25–32. Earl, D.A., von Holdt, B.M., 2011. Structure harvester: a website and program for visualizing structure output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. Elliott, J.M., 1994. Quantitative Ecology and the Brown Trout. Oxford University Press, USA. Evanno, G., Regnaut, S., Goudet, J., 2005. Detecting the number of clusters of individuals using the software Structure: a simulation study. Mol. Ecol. 14, 2611–2620. Fahy, E., 1978. Variation in some biological characteristics of British sea trout, Salmo trutta L. J. Fish Biol. 13, 123–138. Goudet, J., 2001. FSTAT. A program to estimate and test gene diversities and fixation indices 2.9.3. Institute of Ecology. Guo, S.W., Thompson, E.A., 1992. Performing the exact test of Hardy–Weinberg proportion for multiple alleles. Biometrics 48, 361–372.

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