Microsatellite-Based Assessment of Genetic Variation in Tench (Tinca tinca) from Different Polish Regions

Microsatellite-Based Assessment of Genetic Variation in Tench (Tinca tinca) from Different Polish Regions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Microsatellite-Based Assessment of Genetic Variation in Tench (Tinca tinca) from Different Polish Regions Martyna Gadomska, Joanna Czarzasta, Dariusz Kaczmarczyk, Jacek Wolnicki, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7171524/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The tench ( Tinca tinca ) is a freshwater fish that inhabits shallow lakes, small water bodies and lower parts of rivers. This fish is important in the conservation of the biodiversity of European ichthyofauna and aquaculture. In this paper we have estimated the genetic variability of stocks of tench from five distanced locations, initializing a program of genetic studies of this fish in Poland. The genetic variation in the investigated group of fish was moderate. Observed and expected heterozygosity was in range 0.40–0.45 and 0.44–0.48 respectively. Between 37–44 alleles were detected in investigated stocks. In all investigated stocks, genetic variation was reduced because of bottleneck and founder effect (Garza-Williamson M index in range 0.45–0.60) and inbreeding. Genetic distance between the investigated groups of fish was small or moderate. Analysis of genetic structure revealed genetic differences between two stocks from Żabieniec and confirmed their different origin. The genetic variation of stocks included in this study was typical for the Central European area of occurrence of this species. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Genetics Biological sciences/Molecular biology Biological sciences/Zoology tench genetic variation microsatellite DNA conservation genetics Figures Figure 1 Figure 2 Introduction The tench ( Tinca tinca ) is a species of freshwater fish belonging to the Cyprinidae family that is known as a valuable component of European ichthyofauna and aquaculture [ 1 ]. This fish is typical for littoral parts of lakes, but very often inhabits small and shallow water bodies which are prone to drying out and degradation as a result of human activity [ 1 , 2 ]. In such water bodies, tench is often the only species of fish, next to crucian carp. Unfortunately, the number of water bodies inhabited by tench is decreasing quite quickly [ 3 ]. Tench populations are threatened by: a drop in the groundwater level (hydrological drought) and works leading to the drying-up of oxbow lakes and small mid-field water reservoirs inhabited by this fish. The aging process of small water reservoirs of natural origin, or those resulting from human activity may lead to extinction of the tench populations. Moreover, this fish can be threatened by invasive species in the ichthyofauna [ 4 , 5 ]. Tench, despite the fact that it is not yet classified as an endangered species, requires conservation measures regarding both its population and habitats. In the Central European aquaculture, tench is an important fish [ 1 , 6 ] that is highly valued by consumers. Tasty, delicate tench meat is favored in many traditional Central European dishes and makes the price per kilogram of this fish much higher than common carp, rainbow trout and many other species. Moreover, consumer interest in this fish lasts all year round. This makes tench an attractive additional species in fish farms specializing in commercial production of common carp and those two species are often produced in polyculture [ 6 ]. The commercial breeds of tench that are used in the Central European pond aquaculture by German, Hungarian and Czech fish farmers often show a much faster growth rate than tench inhabiting open waters, but as a consequence of breeding orchestrated by humans, they are particularly exposed to a decrease in genetic variation due to inbreeding, the small number of individuals and genetic drift [ 7 ]. To our knowledge no cultured stains of this species are used in Polish aquaculture. Current broodstocks were developed from fish taken some time ago from open water. Tench is also a species highly valued by anglers and important in the recreational use of waters [ 8 , 9 ]. Despite the great importance of tench for the biodiversity of ichthyofauna, aquaculture and recreational use of water, the level of genetic diversity of this species in Poland is mostly unknown. Moreover, there is a need to use a molecular tool based on polymorphic genetic markers and multiplex PCR reactions in studies on the genetic structure of this species in Poland and other regions of its occurrence. Accordingly, the aim of this study was estimating the genetic variability of various stocks of tench from five distanced locations, initializing a program of genetic studies of this fish in Poland. Materials and Methods Ethics statement The study was conducted according to the European and national legislation for fish welfare and approved by the Local Animal Research Ethics Committee in Olsztyn, resolution no 36/2025. The animal study is reported in accordance with ARRIVE guidelines ( https://arriveguidelines.org ) for animal research. Sampling and preservation of the samples The material (fragments of fins) was taken in the autumn of 2024 from five locations: Bogaczewo Fish Farm (BCG), Jerczyńscy Fish Farm (JER), Fisheries Experimental Station in Żabieniec National Inland Fisheries Research Institute (ZAB1), Department of Pond Fishery, National Inland Fisheries Research Institute in Żabieniec (ZAB2), Fisheries Experimental Station in Zator National Inland Fisheries Research Institute (ZTR) (Fig. 1 , Table 1 ). The fish from Fisheries Experimental Station in Żabieniec (ZAB1) and Department of Pond Fishery in Żabieniec (ZAB2) were analysed the one stock named (ZAB) (50 individuals). The material for DNA extraction was collected using the standard fin clipping procedure [ 10 ]. To ensure fish welfare, this procedure was performed under anesthesia with MS-222 anesthetic. A small section of the caudal fin (approximately 10 mm²) was sampled and placed on a piece of paper. The tissue was preserved by allowing the fin clips to dry on the paper. After sampling, all fish were returned to their stocks. Overall, 200 samples were taken. All stocks investigated in those studies originated some decades ago from fish taken from open water or they still inhabit them. Table 1 Stocks characteristics and number of fish sampled. Abbreviation Sampling place Location Habitat Spawning No. of fish sampled BCG Bogaczewo Fish Farm 53°54'59.9"N 20°00'07.4"E Small water body Natural 50 JER Jerczyńscy Fish Farm 51°10'51.6"N 20°08'42.8"E Pond Human controlled 50 ZAB1 Fisheries Experimental Station in Żabieniec, National Inland Fisheries Research Institute 52°03'10.9"N 21°02'07.4"E Pond Human controlled 15 ZAB2 Department of Pond Fishery, National Inland Fisheries Research Institute in Żabieniec RAS system Human controlled 35 ZTR Fisheries Experimental Station in Zator, National Inland Fisheries Research Institute 50°00'09.7"N 19°29'20.0"E Pond Human controlled 50 Extraction of DNA The Genomic Mini AX Tissue Spin Kit (manufactured by A&A Biotechnology, Gdansk, Poland) was used to extract DNA from the fin samples. The extraction procedure was performed in accordance with the protocol provided by the kit manufacturer (A&A Biotechnology, Gdansk, Poland). The degree of DNA fragmentation was evaluated by electrophoresis in 1.5% agarose gel and the amount of DNA was measured by spectrophotometry (Nanodrop 8000, Thermo Fisher Scientific, USA) at a wavelength of 260 nm. Samples that did not show traces of fragmentation and contained at least 80 pg/µL and purity based on the absorption ratio of 260/280 nm wavelength equalled 1.8 or more were used for amplification of microsatellite loci in the next stage of the experiment. If a DNA sample failed to meet the above criteria, the extraction procedure was repeated. Choice of primers and optimisation of the PCR reaction conditions Sequences of primers used to amplify microsatellites of the tench were obtained from the papers (microsatellites from MTT-1 to MTT-9 ) [ 11 ] (microsatellite MFW1 ) [ 12 ] and (microsatellite CYPG24) [ 13 ] (Table 1 ). To select the optimal PCR conditions, different variants of a reaction were tested at temperatures ranging +/- 3 C º relative to the values given by the authors [ 11 , 12 , 13 ]. DNA from eight randomly selected samples was used as a template DNA in those tests. On this stage of optimisation work reaction mixtures were prepared in a total volume of 30 µl with a 40 ng DNA template, a 3µl of 10x PCR reaction buffer for RUN Taq polymerase (A&A Biotechnology, Gdansk, Poland), 0.4 mM of each primer, 0.25 mM of each deoxynucleotide triphosphate (dNTP), and 1 unit RUN Taq polymerase (A&A Biotechnology, Gdansk, Poland). Re-distilled water (Ambion, USA) was used to bring the reaction mixture to the desired final volume. The presence or absence of a PCR product at the target length reported in the literature, as well as the presence of additional (non-specific) bands, were checked by the use of electrophoresis in agarose gel stained by Midori Green Advance, following the methodology described in the subchapter on extraction of DNA. Pairs of primers that enabled the acquisition of a clear PCR product at a length close to that reported in the papers [ 11 , 12 , 13 ] were synthesised again as 5’ labelled oligonucleotides. The forward oligonucleotide in each of the primer sets was labelled with fluorescent dyes 6-FAM, VIC, NED, PET to enable genotyping using Applied Biosystems 3130 Genetic Analyser (Applied Biosystems, Foster City, CA, USA). 11 microsatellite primers (Table 2 ) were assigned in three multiplex sets. The assignment of primers into multiplex sets followed Roche’s guidelines [ 14 ]. All primer pairs in the set were checked for self-complementarity by using the Primer Pooler v1.88 software ( http://ssb22.user.srcf.net/pooler/ ) [ 15 ]. Those with a strong tendency to create primer dimer structures were excluded. PCR amplification in multiplex mode was performed by using QIAGEN Multiplex PCR Kit (Qiagen GmbH, Germany). Following the manufacturers’ instructions, the PCR primers were suspended in the TE buffer to reach a base concentration of 100 pmol/µl. The final concentration of each pair of the PCR primers in the multiplex PCR set was adjusted to reach a balanced yield of the PCR products (Table 2 ). The composition of PCR mixture followed the manufacturer’s recommendations and contained: 7.5 µl of QIAGEN Multiplex PCR master mix, 3 µl of Q solution, 0.2–0.9 µmol of each primer (Table 2 ), and deionised water to a final volume of 15 µl. Table 2 Characteristics of the microsatellite markers included in the sets: Multiplex I , II and III . Locus NCBI Accession number Primer’s sequence 5' dye Multiplex Primers µmol/15µl PCR mixture MTT-3 DQ080086 F:CCAGCAGAGCCCTACACTTC R:AGGACGTGACCATCAACACA PET I 0.4 CypG24 AY439120 F:CTGCCGCATCAGAGATAAACACTT R:TGGCGGTAAGGGTAGACCAC NED 0.9 MTT-4 DQ080087 F:TTAAAACCGCCACACTTTCC R:ACGTGCGGCTGTGAGATTAT 6-FAM 0.3 MTT-2 DQ080085 F:CTGGTCTCCTCCTTGTGCTC R:TGGGTGAAGGATTGGTTGTT VIC 0.3 MTT-6 DQ080089 F:TGTGTGAGGTGGCACAGAAT R:ATGTGAGCAATGGCTGTGAG VIC II 0.2 MFW1 AY703052 F:FGTCCAGACTGTCATCAGGAG R:GAGGTGTACACTGAGTCACGC 6-FAM 0.7 MTT-7 DQ080090 F:ACCTCGCCATGTATGCTTTT R:GTTGACCTGTGCATGCATTT PET 0.3 MTT-8 DQ080091 F:GAAATGTCCCCACAAACCAC R:GACACCGCTATCACCATCAG 6-FAM 0.4 MTT-9 DQ080092 F:CAATCTGGTGGAAGTGAGCA R:ACGCGTCAGTGACAGAGAGA 6-FAM III 0.4 MTT-1 DQ080084 F:GTCCTCGCAATGCAAGAAAT R:TTGGCTCATATTGGGTGTGA PET 0.7 MTT-5 DQ080088 F:GGGAGCCAGTTCACACTCAT R:GACATGAAAACGGTGCTGTG NED 0.4 Fragment analysis and genotyping Automatic capillary electrophoresis was used to verify the usefulness of individual microsatellite fragments in the multiplex PCR assays and genotyping [ 16 ]. The length of the DNA fragment was measured by using an Applied Biosystems 3130 Genetic Analyser DNA sequencer (Applied Biosystems, Foster City, CA, USA). Determination of the size of DNA fragments was performed against the GeneScan™ 500 LIZ® Size Standard (Applied Biosystems, Foster City, CA, USA). This enabled simultaneous measurement of fragments that were labelled with fluorescent dyes (6FAM, VIC, NED, PET). The mixture of chemicals used for automated capillary electrophoresis consisted of 19 µl Hi-Di Formamide, 0.5 µl GeneScan™ 500 LIZ® Size Standard, and 0.5 µl multiplex PCR product. The separation of the DNA fragments was performed in 36 cm capillary arrays and in POP-7 polymer. Genotyping was performed by using the GeneMapper 4.0 software (Applied Biosystems, USA). Polymorphism and genetic variation The polymorphism at the investigated loci and genetic diversity of stocks was assessed by using such indicators as: observed heterozygosity ( H o ), expected heterozygosity ( H e ), number of alleles ( NA ) and allelic diversity ( AD ). Values of those indicators were calculated by using the MSA software [ 17 ]. The Exact Hardy Weinberg (H-W) test [ 18 ] was used to test for deviations from H-W equilibrium. The test was performed separately for each locus in each stock as well for all loci in the given stocks. This test was performed by using Arlequin 3.5.2 software [ 19 ]. The number of steps in the Markov chain equalled 1,000,000 and the number of dememorization steps equalled 100,000. The deviations were considered significant if p ≤ 0.01. Assessment of the reduction of genetic variation as a result of bottleneck or founder effect was performed using the MSA software [ 17 ]. The occurrence of bottleneck or founder effects, and their influence on within-population genetic variability was based on the Garza-Williamson M index (the number of alleles divided by the allelic range). This index [ 20 ], including Excoffier’s adjustment, was calculated using Arlequin 3.5.2 software [ 19 ]. The evaluation of inbreeding was based on Wright’s F IS inbreeding coefficient [ 21 ] and comparison of the observed heterozygosity and expected heterozygosity. The positive values of this coefficient indicate inbreeding, negative for outbreeding. This coefficient was calculated by using Arlequin 3.5.2 software [ 19 ] as a part of Hierarchical Analysis of Molecular Variance (AMOVA) locus by locus analysis and averaged across the loci. Interpretation of F IS values and inbreeding assessment were performed according to [ 22 ] and [ 23 ]. Genetic distance and structure Genetic divergence between stocks was analysed using two different methods: the fixation index ( F ST ) [ 21 ] and the variation in average allelic size ( δµ 2 ) [ 24 ]. F ST values and their statistical significance were calculated with Arlequin 3.5.2 software [ 19 ]. The size of the genetic distance based on F ST values, and their ranges were interpreted according to [ 25 ] and [ 26 ]. Higher values of this coefficient (closer 1.0) and statistical significance indicate larger genetic differences between pairs of stocks, whereas lower values (closer to 0.0) and a lack of statistical significance indicate genetic similarity [ 26 ]. The relationships between stocks based on their genetic distance was shown as a dendrogram. Construction of this dendrogram was based on F ST values. The UPGMA algorithm implemented into the D-UPGMA tool ( http://genomes.urv.cat/UPGMA/ ) was used to construct this dendrogram. Genetic divergence was also estimated by using the sample-size independent δµ 2 method [ 24 ] calculated with MSA software [ 17 ]. Using this method higher values indicated larger genetic differences between stocks and smaller values of minor differences. The contribution of the specific components of genetic variance to the total variance observed among all investigated group of fish was estimated by means of AMOVA [ 27 , 28 ]. These calculations were performed using Arlequin 3.5.2 software [ 19 ] with 1000 permutations. The threshold for significance was set at p = 0.05. The STRUCTURE 2.3.4 software was used to detect genetic structure and gene flow [ 29 ] between investigated groups of fish. The Evanno method (ΔK) [ 30 ] was used to infer the true number of clusters (K) based on the rate of change in log probability among consecutive K values, which ranged from K = 1 to K = 10. Four iterations of each K were performed with 200,000 burnins periods and 200,000 Markov Chain Monte Carlo (MCMC) repetitions. To this end, the Clumpak program was employed to identify the optimal alignment of inferred clusters across different values of K [ 31 ]. Results Allelic diversity In the investigated stocks, nine microsatellites were polymorphic. Two loci ( MTT-4 and MTT-7 ) were monomorphic in fish from all locations, therefore they were not included in calculations of some indicators of genetic variations such as heterozygosity, deviations from H-W equilibrium, inbreeding coefficient, M value of Garza-Williamson index [ 20 ]. Those two microsatellites were also excluded from estimation of the genetic distances. Two other microsatellites ( MTT-3 , and MFW1 ) were monomorphic in some stocks and polymorphic in the others. At those loci the frequency of the second allele was low and varied from 0.01 (MTT-3 in ZTR) to 0.04 ( MTT-3 in ZAB). The polymorphism of other loci (except MTT-9 ) was not high and varied in range from two to six alleles. The locus MTT-9 was the most polymorphic among all investigated markers, 19 alleles were detected at this locus across fish from all investigated locations. Overall, 53 alleles were detected across 11 investigated loci. Within the investigated stocks, the degree of polymorphism was quite similar and ranged from 37 alleles (JER) to 44 alleles (BCG). The allelic diversity ( AD ) was moderate or high and varied from 3.36 alleles per locus in JER stock to 4.00 in the BCG (Table 3 ). Alleles specific for given stock were found in the BCG stock (6 alleles) and the stocks of ZAB and ZTR (1 allele). All of them had a low frequency (0.01–0.06) (Supplementary files - Suppl. 1). Table 3 Number of alleles detected at the eleven investigated loci and their range: number of alleles ( NA) allelic diversity ( AD ) Stock Number of alleles across stocks, (and PCR product range in (bp)) Locus (5’dye) BCG ZAB ZTR JER MTT-3 (PET) 1 2 2 1 2, (150–162) CypG24 (NED) 5 3 3 3 5, (153–177) MTT-4 (6-FAM) 1 1 1 1 1, (207) MTT-2 (VIC) 2 2 2 2 2, (238–242) MTT-6 (VIC) 5* 5 6 5 6, (158–176) MFW1 (6-FAM) 1 2 1 1 2, (169–175) MTT-7 (PET) 1 1 1 1 1, (216) MTT-8 ( 6-FAM) 5* 3 3* 4* 5, (198–236) MTT-9 (6-FAM) 14 12 12 11 19, (128–180) MTT-1 (PET) 5* 4* 5 4 5, (169–177) MTT-5 (NED) 4 4 5 4 5, (207–215) NA 44 39 41 37 53 AD 4.00 3.54 3.73 3.36 4.82 *Significant deviation from the Hardy-Weinberg equilibrium Table 4 The coefficients of genetic variation in investigated stocks calculated on the basis of nine polymorphic loci: observed ( H o ) and expected ( H e ) heterozygosity, inbreeding coefficient ( F IS ), value of the Garza-Williamson index ( M ). Stock Across all stocks Indicator BCG ZAB ZTR JER H o 0.42 0.40 0.44 0.45 0.42 H e 0.48 0.44 0.48 0.47 0.47 F IS 0.20 0.08 0.11 0.06 0.08 M 0.49 0.60 0.54 0.45 0.52 Heterozygosity, Hardy-Weinberg equilibrium, and inbreeding coefficient To evaluate genetic variation in stocks, the observed ( H o ) and expected ( H e ) heterozygosity was calculated (Table 4 ). In general, genetic variation described by this indicator was low, with average values for observed and expected heterozygosity of 0.42 and 0.47, respectively. The values of H o and H e for individual stocks were quite similar across all investigated locations. In all of them, observed heterozygosity was slightly lower than the expected heterozygosity ( H e > H o ) (Table 4 ). In all stocks, the mean H o value was close to the average percentage of heterozygotes ( H e ) expected at H-W equilibrium. When calculated across all markers, departures from this equilibrium were not significant ( p > 0.05). Significant departures ( p ≤ 0.01) were found at the level of individual loci (Table 3 ). In most cases they were at locus MTT-8 . Departures at more than one locus were observed only in the BCG stock (three loci). Positive values of F IS indicating inbreeding were found in all stocks, but they differed in values of this indicator. In the BCG, the inbreed was high ( F IS =0.20) and this was the highest score among all investigated groups of fish. In all other stocks, inbreed was moderate ( F IS 0.06–0.11). The lowest value of this indicator was calculated for JER stock ( F IS =0.06). Bottleneck and founder effects The average Garza-Williamson M index [ 20 ] value across the investigated stocks was 0.52. This value was lower than 0.68, which indicates that founder and/or bottleneck effects had a significant impact on genetic variations in these stocks (Table 4 ). The M value was highest in ZAB stock (0.60), indicating a relatively small reduction of genetic variation resulting from bottleneck and/or founder effect. The stock ZAB is the only one where the M index was equal to or greater than 0.60. The lowest M values were in stock of JER (0.45) and BCG (0.49), which suggests a slightly larger reduction of genetic variation within them than in ZAB stock (Table 4 ). Genetic divergence between stocks Based on F ST values, the genetic distances amongst most of stocks varied in range F ST 0.009–0.049 and was classified as small ( F ST <0.05). Only in one pair was it higher than 0.050 and was classified as moderate ( F ST 0.05–0.14) (Table 5 , Fig. 2 ). This largest genetic distance ( F ST =0.051) was found between stocks: the ZAB and ZTR. The smallest distance was observed between the BCG and ZTR stock ( F ST =0.009), and this distance was not significant (p > 0.05) All other genetic distances were significant at p < 0.05. Table 5 Genetic distance between stocks and tench stocks estimated by using F ST and δµ 2 methods. Genetic distance (δ µ 2 ) Genetic distance (F ST ) Stock BCG ZAB ZTR JER BCG X 0.043 0.009* 0.049 ZAB 0.405 X 0.051 0.028 ZTR 0.549 0.073 X 0.045 JER 0.562 0.247 0.169 X *Genetic distance not significant (p > 0.05) The magnitudes of the genetic distances between stocks were also estimated using the δµ 2 method (Table 3 ). This method confirmed that genetic distances between all tench stocks is small. The most differentiated were stocks BCG and JER (0.562) and the closest to each other were stocks ZTR and ZAB (0.073) (Table 3 ). The Bayesian estimation of genetic structure and individual membership indicated that the maximum value of ΔK using Evanno method [ 30 ] were for K = 3 (ΔK = 26.4) and K = 5 (ΔK 26.2). When using median values of Ln (Pr Data), an optimal K = 5 (Supplementary files – Suppl. 2). This K value was closer to our field observations than K = 5. The size of genetic distance between tench stocks was confirmed in Bayesian analysis performed by using STRUCTURE 2.3.4 software. Because this distance was small, fish from all locations could be assigned to more than one stock and differed only in likelihood of this assignment. This analysis revealed genetic differences inside ZAB stock. Both in K = 3 and K = 5 scenarios, tench from the Experimental Fisheries Station in Żabieniec (ZAB1) and the Department of Pond Fishery in Żabieniec (ZAB2) were different. The individuals from ZAB1 had their own unique cluster (violet) and a much higher probability of assigning into it than any other cluster (). Moreover, this analysis indicated that fish from BCG were very similar to individuals from ZTR, and fish from ZAB2 were similar to JER stock (Supplementary files – Suppl. 3). AMOVA revealed that the variation of all samples was 2.179 and the sum of squares was 861.4. The most important component of this variation was that within individuals (1.882 and the sum of squares 376.5). This class of variation was responsible for 86.4% of the total variance among all samples. The other components were: the variation among individuals within a stock (0.217, sum of squares 454,0 and 10.0%) and among stocks (0.080, sum of squares 30.882 and 3.7%). Their share in the whole genetic variation was low. Discussion Genetic variation in stocks The comparison of the microsatellite DNA variation revealed that genetic diversity in each of four stocks was similar to that reported in German stock/populations (33 alleles at 9 microsatellite loci, AD 3.7) [ 11 ] and Hungarian populations (29–50 alleles, at 12 loci, AD 2.41–4.16) [ 32 ]. The biggest differences in the number of alleles between stock investigated by Kohlmann and Kersten [ 11 ] and Polish stocks were found at loci MTT-3 and MTT-9 . In our studies, a locus MTT-3 was polymorphic, and polymorphism at locus MTT-9 (19 alleles) was higher than 9 alleles reported by Kohlmann and Kersten [ 11 ]. Moreover, the differences in allelic length were observed at some loci. Those differences are probably a result of the more diverse sample size in our study (200 fish, 5 stocks) and use of a different size standard in automatic DNA electrophoresis than that used by Kohlmann and Kersten [ 11 ]. It should be verified by investigation of more populations and stock whether monomorphism of microsatellite MTT-4 and MTT-7 is typical for this species or if they present monomorphism in some populations and polymorphism in others, as MTT-3 does. The fish from Bogaczewo (BCG) are those where the highest number of alleles detected. This can be explained by the fact that it is the only one among those investigated in this paper that spawns naturally. It is known that natural breeding helps maintain rare alleles in the population [ 33 ]. A small number of alleles in the cultured stocks is a consequence of decline of genetic variability as a result of breeding controlled by humans. This decline results mainly in a loss of rare alleles rather than a reduced heterozygosity and is probably a result of a relatively low number of fish being used to maintain these stocks [ 7 ]. Such situations are typical for fish culture due to the generally high fecundity of females. In the long term this may lead to measurable inbreeding depressions such as reduced vitality and growth rate. In investigated stocks, both observed and expected heterozygosity ( H o : 0.17–0.58, H e : 0.25–0.54) were in the upper range of reported by Kohlmann and Kersten [ 11 ], Kohlmann et al. [ 7 ] and Al Fatle et al. [ 32 ] for German and Hungarian populations. A common feature of fish investigated in this study and most Hungarian populations and stocks reported by Al Fatle et al. [ 32 ] was an excess of H e over H o . This could be a consequence of several factors, of which instability of environment and inbreeding are probably the most important. This is not surprising, because tench often inhabit shallow water bodies that are vulnerable to drying, resulting in bottlenecks and inbreeding. Fish from Bogaczewo (BCG) inhabit such a water body that is vulnerable to environmental changes, resulting in bottlenecks and inbreeding. Consequently, the most numerous deviations from the H-W equilibrium, the highest (0.06) difference between H e and H o the highest value of F IS and one of the lowest values of the GW index are observed in this stock. The founder and bottleneck effects are known to be important factors that determine genetic variation in broodstock and genetic characteristics of conserved populations [ 34 ]. The results of Garza-Williamson M index being lower than the critical value of 0.68 [ 20 ] suggest that none of the studied stocks avoided a reduction in genetic variation. However, there are some differences in causes and magnitude of this reduction among the stocks. The lowest values observed in JER stock are probably the consequence of founder effect and relatively low genetic variation in the group of fish used in establishing this stock. A genetic variation in BCG was probably reduced as a result of unstable environments and bottleneck effects. Fish from ZAB were probably the ones that suffered the least reduction of genetic variation as a result of bottleneck or founder effect. This is not surprising, knowing that there are two stocks in this location, they are separated from each other and have different origins. The progressive inbreeding of broodstocks or small isolated populations is a well-known problem in the conservation of fish species or in their aquaculture. It is worth noting that the actual moderate and high inbreed in Polish tench stocks is similar to that observed by Al Fatle et al. [ 32 ] in their Hungarian counterparts ( F IS -0.03-0.25) and can be evaluated as typical for this species. Although in BCG stock this inbreed is higher than in other investigated stocks, it could be a consequence of environmental stress [ 35 ] that may have occurred in the waterbody it inhabited. According to the author’s knowledge no indications of inbreeding depressions are observed at the investigated locations, but monitoring of inbreeding and other indicators of genetic variation is recommended. Genetic distance and structure Biology of tench, their spawning behavior [ 1 , 2 , 36 ] and possible transfer of the eggs between water bodies by water birds prevents geographic isolation of their populations. Moreover, human induced activities such as stocking of tench juveniles or transferring them between locations, decrease genetic differences between populations of this species [ 37 ]. In natural conditions, networks of the tench population are part of metapopulations belonging to the eastern or western phylogroups of this species [ 36 , 38 ]. It is known that some gene flow between them is common and the differentiation rate of the tench population is slow [ 39 ]. Pairwise genetic distances between the stocks reported in this paper are in a range typical for a western phylogroup of this fish ( F ST in range 0.008–0.159) [ 7 , 32 ]. However, a much greater genetic distance between some Hungarian populations ( F ST up to 0.219) has been reported [ 32 ]. These unusually high values were a consequence of population-specific factors such as increased inbreeding, bottlenecks, and strong genetic drift [ 32 ], and cannot be considered typical for the species. Genetic characteristics among fish belonging to the tench phylogroup are usually quite similar, especially in the areas of contact [ 36 ]. Although the genetic distance between tench stocks in Poland is generally small or moderate, the stocks nonetheless differ from one another, and the differences reflect their respective histories. A Bayesian analysis of the genetic structure revealed that the stocks from the Fisheries Experimental Station in Żabieniec (ZAB1) and the Department of Pond Fishery in Żabieniec (ZAB2) are genetically distinct. Those differences are due to the stock from the Department of Pond Fishery being maintained in controlled conditions through the use of RAS systems, and being physically separated from fish belonging to the Fisheries Experimental Station, which are maintained exclusively in ponds. Moreover, fish from those two stocks have different origins and have never been crossbred with each other. A tench broodstock from the Pond Fisheries Department was raised from tench larvae obtained some years ago from JER Fish Farm and this fact explains the genetic similarity of fish ZAB2 and JER. It is difficult to explain a genetic similarity between the tench from Bogaczewo (BCG) and the Fisheries Experimental Stations in Zator (ZTR). According to the information provided by the owner of the Bogaczewo Fish Farm, those fish are native, and no imports were made, therefore this similarity can be evaluated as random. A relatively large genetic difference between the stocks ZAB and ZTR are a consequence of their isolation from each other, founder effect and probably genetic drift. To our knowledge those two stocks did not have any close ancestors, and they were not transferred between those two locations. Although genetic differences between investigated stocks are clearly detectable and, in many cases, significant, they are only a small portion the overall genetic variation observed among the investigated fish. Consequently, a genetic profiling of individuals and assembly into spawning pairs of fish that differ the most to each other, may have been a better approach in a preventing a decrease of genetic variation than random transfers between stocks. Moreover, genetically different fish from ZAB1 and ZAB2, can be useful if an increase in genetic variation of this species is needed. Conclusions The genetic variation of tench in Polish stocks is moderate and comparable to that observed in other European populations. The genetic diversity of the examined stocks has been reduced due to founder effects, bottlenecks, inbreeding, and human-controlled breeding practices. Our study revealed moderate to low genetic distances between these stocks, which are typical for tench populations across the Central European range of the species. Declarations Competing interests The authors declare no competing interests. Funding This study was supported by the Ministry of Agriculture and Rural Development Republic of Poland under the task nr. RYB.rs.070.3.2024 and by Research Task Z-020 of the Department Pond Fishery, National Inland Fisheries Research Institute, Olsztyn, Poland. Author Contribution Conceptualization, J.W. and D.K.; Methodology, M.G., J.C. and D.K.; Validation, D.K.; Formal Analysis, D.K.; Investigation, M.G., J.C., D.K. and A.N.; Resources, J.W.; Data Curation, D.K.; Writing – Original Draft Preparation, D.K., J.C. and J.W.; Writing – Review & Editing, D.K., J.C. J.W., M.G. and A.N.; Visualization, D.K.; Supervision, D.K.; Project Administration, D.K.; Funding Acquisition, D.K. and J.W. Data Availability The datasets generated and/or analysed during the current study are stored in the Open Science Framework repository [https://osf.io/uk7pz/?view_only=203e5fe0fa8d47e0bacdbcaf3b8f9c15](https:/osf.io/uk7pz/?view_only=203e5fe0fa8d47e0bacdbcaf3b8f9c15) , and will be made fully publicly available upon acceptance of the manuscript for publication. References Szczerbowski, J. Tench in Inland fisheries . (ed. Szczerbowski J) 268–270 (1993). Brylinska, M., Brylinski, E., Bninska, M. & Tinca tinca in The freshwater fishes of europe (ed. Banarescu, P.) 5/I, 229–302 (1999). Skrzypczak, A. & Mamcarz, A. Changes in commercially exploited populations of tench, Tinca tinca (L.), in Lakes of northeastern poland . Aquac Int. 14 , 179–193 (2006). Bninska, M. The effect of recreational uses upon aquatic ecosystems and fish resources. in Habitat modification and freshwater fisheries (ed. Alabaster, J.) 223–235 (1985). Leopold, M., Bninska, M. & Nowak, W. Commercial fish catches as an index of lake eutrophication. Arch. Hydrobiol. 106 , 513–524 (1986). Adamek, Z., Sukop, I., Rendon, P. M. & Kouril, J. Food competition between 2 + tench ( Tinca tinca L.), common carp ( Cyprinus carpio L.) and bigmouth buffalo ( Ictiobus cyprinellus Val.) in pond polyculture. J. Appl. Ichthyol. 19 , 165–169 (2003). Kohlmann, K., Kersten, P. & Flajšhans, M. Comparison of microsatellite variability in wild and cultured tench ( Tinca tinca ). Aquaculture 272 , 147–151 (2007). Andreji, J., Dvorak, T., Randak, T. & Turek, J. Breeding of stock for open waters and their stocking in Fishery in open waters (ed. Randak, T.) 230–231 (2014). Mickiewicz, M. & Wołos, A. Economic ranking of the importance of fish species to lake fisheries stocking management in Poland. Fisheries &Aquatic Life . 20 , 11–18 (2012). Xing, L., Quist, T. S., Stevenso, T. J., Dahlem, T. J. & Bonkowsky, J. L. Rapid and efficient zebrafish genotyping using PCR with high-resolution melt analysis. J. Vis. Exp. 5 , e51138 (2014). Kohlmann, K. & Kersten, P. Microsatellite loci in tench: isolation and variability in a test population. Aquac Int. 14 , 3–7 (2006). Crooijmans, R. P., Poel, J. V., Groenen, M. A., Bierbooms, V. A. & Komen, J. Microsatellite markers in common carp ( Cyprinus carpio L). Anim. Genet. 28 , 129–134 (1997). Baerwald, M. R. & May, B. Characterization of microsatellite loci for five members of the minnow family Cyprinidae found in the Sacramento–San Joaquin Delta and its tributaries. Mol. Ecol. Notes . 4 , 385–390 (2004). Roche. Optimization of Reactions to Reduce Formation of Primer Dimers. Roche Molecular Biochemicals Technical Note 1/99. (1999). http://www.gene-quantification.de/roche-primer-dimer.pdf (2024). Brown, S. S. et al. ​PrimerPooler: automated primer pooling to prepare library for targeted sequencing. ​Biol Methods Protoc. 2 , 1–10 (2017). Butler, J. M., Ruitberg, C. M. & Vallone, P. M. Capillary electrophoresis as a tool for optimization of multiplex PCR reactions. Fresenius’ J. Anal. Chem. 369 , 200–205 (2001). Dieringer, D. & Schlötterer, C. Microsatellite analyser (MSA): a platform independent analysis tool for large microsatellite data sets. Mol. Ecol. Notes . 3 , 167–169 (2003). Nei, M. Molecular Evolutionary Genetics (Columbia University, 1987). Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10 , 564–567 (2010). Garza, J. & Williamson, E. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10 , 305–318 (2001). Wright, S. The genetical structure of populations. Ann. Eugen . 15 , 323–354 (1951). Hedrick, P. W. & Kalinowski, S. T. Inbreeding Depression in Conservation Biology. Annu. Rev. Ecol. Evol. Syst. 31 , 139–162 (2000). Llambi, S. et al. Genetic structure and population dynamics of autochthonous and modern porcine breeds. Analysis of the IGF2 and MC4R genes that determine carcass characteristics. Austral J. Veterinary Sci. 52 , 87–94 (2020). Goldstein, D. B., Linares, A. R., Cavalli-Sforza, L. L. & Feldman, M. W. An evaluation of genetic distances for use with microsatellite loci. Genetics 139 , 463–471 (1995). Wright, S. Evolution and the Genetics of Population, Variability Within and Among Natural Populations. The Univ. Chic. Press 4 , (1978). Balloux, F. & Lugon-Moulin, N. The estimation of population differentiation with microsatellite markers. Mol. Ecol. 11 , 155–165 (2002). Excoffier, L. & Slatkin, M. Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Mol. Biol. Evol. 12 , 921–927 (1995). Michalalakis, Y. & Excoffier, L. A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci. Genetics 142 , 1061–1064 (1996). Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155 , 945–959 (2000). Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol. Ecol. 14 , 2611–2620 (2005). Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15 , 1179–1191 (2015). Al Fatle, F. A. et al. Genetic structure and diversity of native tench ( Tinca tinca L. 1758) populations in Hungary—establishment of basic knowledge base for a breeding program. Diversity 14 , 336 (2022). Becker, P. A. et al. Inbreeding avoidance influences the viability of reintroduced populations of African wild dogs ( Lycaon pictus ). PLoS One . 7 , e37181 (2012). Exadactylos, A., Rigby, M. J., Geffen, A. J. & Thorpe, J. P. Conservation aspects of natural populations and captive-bred stocks of turbot (Scophthalmus maximus) and Dover sole (Solea solea) using estimates of genetic diversity. ICES J. Mar. Sci. 64 , 1173–1181 (2007). Fox, C. W. & Reed, D. H. Inbreeding depression increases with environmental stress: an experimental study and meta-analysis. Evolution 65 , 246–258 (2011). Lajbner, Z., Kohlmann, K., Linhart, O. & Kotlík, P. Lack of reproductive isolation between the Western and Eastern phylogroups of the tench. Rev. Fish. Biol. Fish. 20 , 289–300 (2010). Lajbner, Z. & Kotlik, P. PCR-RFLP assays to distinguish the Western and Eastern phylogroups in wild and cultured tench Tinca tinca . Mol. Ecol. Resour. 11 , 374–377 (2011). Karaiskou, N. et al. Genetic structure and divergence of tench Tinca tinca European populations. J. Fish. Biol. 97 , 930–934 (2020). Chiu, M-C., Nukazawa, K., Resh, V. H. & Watanabe, K. Environmental effects, gene flow and genetic drift: Unequal influences on genetic structure across landscapes. J. Biogeogr. 50 , 352–364 (2023). Additional Declarations No competing interests reported. Supplementary Files Suppl.1withchanges.pdf Suppl.2..png Suppl3..png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7171524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":501779076,"identity":"e512fe4c-9d92-43f8-b01a-13beb913e87e","order_by":0,"name":"Martyna Gadomska","email":"","orcid":"","institution":"University of Gdansk","correspondingAuthor":false,"prefix":"","firstName":"Martyna","middleName":"","lastName":"Gadomska","suffix":""},{"id":501779077,"identity":"bab92842-4285-4be0-89b1-2b7cb398b99f","order_by":1,"name":"Joanna Czarzasta","email":"","orcid":"","institution":"National Inland Fisheries Research 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19:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7171524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7171524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89516905,"identity":"57b657ff-609d-4fe8-9fc9-8cee20f6940b","added_by":"auto","created_at":"2025-08-20 20:12:23","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":162755,"visible":true,"origin":"","legend":"\u003cp\u003eSampling location on area of Poland.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7171524/v1/c9738b0f1ccb159833c740ea.jpeg"},{"id":89516524,"identity":"0d74a4b6-e1a7-47f4-906d-61b1b2f95080","added_by":"auto","created_at":"2025-08-20 19:56:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3303,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of genetic divergence between stocks based on \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e indicator.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7171524/v1/3e4c67dbd542c3e7cb8b9c47.png"},{"id":98425872,"identity":"e1fa36f8-8903-4a71-9f71-772a57dfe774","added_by":"auto","created_at":"2025-12-17 16:35:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":978541,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7171524/v1/0fe1e6ad-0041-446d-9c86-951781a7d625.pdf"},{"id":89516610,"identity":"c8d3391e-2a74-439e-b669-ff22ea376cbd","added_by":"auto","created_at":"2025-08-20 20:04:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":59997,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.1withchanges.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7171524/v1/46e6a3add63f49a1df05f740.pdf"},{"id":89516906,"identity":"a2954369-5f76-468d-b83c-6bbdfcb9f4fd","added_by":"auto","created_at":"2025-08-20 20:12:23","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":87568,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.2..png","url":"https://assets-eu.researchsquare.com/files/rs-7171524/v1/975b6149811077bd60619649.png"},{"id":89516527,"identity":"5d669fd2-7eb5-4173-a759-903f5dda4f2c","added_by":"auto","created_at":"2025-08-20 19:56:23","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":110589,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl3..png","url":"https://assets-eu.researchsquare.com/files/rs-7171524/v1/a1cb7940def6f6b4246436b4.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microsatellite-Based Assessment of Genetic Variation in Tench (Tinca tinca) from Different Polish Regions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe tench (\u003cem\u003eTinca tinca\u003c/em\u003e) is a species of freshwater fish belonging to the Cyprinidae family that is known as a valuable component of European ichthyofauna and aquaculture [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This fish is typical for littoral parts of lakes, but very often inhabits small and shallow water bodies which are prone to drying out and degradation as a result of human activity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In such water bodies, tench is often the only species of fish, next to crucian carp. Unfortunately, the number of water bodies inhabited by tench is decreasing quite quickly [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Tench populations are threatened by: a drop in the groundwater level (hydrological drought) and works leading to the drying-up of oxbow lakes and small mid-field water reservoirs inhabited by this fish. The aging process of small water reservoirs of natural origin, or those resulting from human activity may lead to extinction of the tench populations. Moreover, this fish can be threatened by invasive species in the ichthyofauna [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Tench, despite the fact that it is not yet classified as an endangered species, requires conservation measures regarding both its population and habitats.\u003c/p\u003e\u003cp\u003eIn the Central European aquaculture, tench is an important fish [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] that is highly valued by consumers. Tasty, delicate tench meat is favored in many traditional Central European dishes and makes the price per kilogram of this fish much higher than common carp, rainbow trout and many other species. Moreover, consumer interest in this fish lasts all year round. This makes tench an attractive additional species in fish farms specializing in commercial production of common carp and those two species are often produced in polyculture [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The commercial breeds of tench that are used in the Central European pond aquaculture by German, Hungarian and Czech fish farmers often show a much faster growth rate than tench inhabiting open waters, but as a consequence of breeding orchestrated by humans, they are particularly exposed to a decrease in genetic variation due to inbreeding, the small number of individuals and genetic drift [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. To our knowledge no cultured stains of this species are used in Polish aquaculture. Current broodstocks were developed from fish taken some time ago from open water. Tench is also a species highly valued by anglers and important in the recreational use of waters [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the great importance of tench for the biodiversity of ichthyofauna, aquaculture and recreational use of water, the level of genetic diversity of this species in Poland is mostly unknown. Moreover, there is a need to use a molecular tool based on polymorphic genetic markers and multiplex PCR reactions in studies on the genetic structure of this species in Poland and other regions of its occurrence.\u003c/p\u003e\u003cp\u003eAccordingly, the aim of this study was estimating the genetic variability of various stocks of tench from five distanced locations, initializing a program of genetic studies of this fish in Poland.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eEthics statement\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe study was conducted according to the European and national legislation for fish welfare and approved by the Local Animal Research Ethics Committee in Olsztyn, resolution no 36/2025. The animal study is reported in accordance with ARRIVE guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arriveguidelines.org\u003c/span\u003e\u003cspan address=\"https://arriveguidelines.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for animal research.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSampling and preservation of the samples\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe material (fragments of fins) was taken in the autumn of 2024 from five locations: Bogaczewo Fish Farm (BCG), Jerczyńscy Fish Farm (JER), Fisheries Experimental Station in Żabieniec National Inland Fisheries Research Institute (ZAB1), Department of Pond Fishery, National Inland Fisheries Research Institute in Żabieniec (ZAB2), Fisheries Experimental Station in Zator National Inland Fisheries Research Institute (ZTR) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The fish from Fisheries Experimental Station in Żabieniec (ZAB1) and Department of Pond Fishery in Żabieniec (ZAB2) were analysed the one stock named (ZAB) (50 individuals). The material for DNA extraction was collected using the standard fin clipping procedure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To ensure fish welfare, this procedure was performed under anesthesia with MS-222 anesthetic. A small section of the caudal fin (approximately 10 mm\u0026sup2;) was sampled and placed on a piece of paper. The tissue was preserved by allowing the fin clips to dry on the paper. After sampling, all fish were returned to their stocks. Overall, 200 samples were taken. All stocks investigated in those studies originated some decades ago from fish taken from open water or they still inhabit them.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStocks characteristics and number of fish sampled.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbbreviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSampling place\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpawning\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo. of fish sampled\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBogaczewo Fish Farm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53\u0026deg;54'59.9\"N 20\u0026deg;00'07.4\"E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSmall water body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNatural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJerczyńscy Fish Farm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u0026deg;10'51.6\"N 20\u0026deg;08'42.8\"E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHuman controlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZAB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFisheries Experimental Station in Żabieniec, National Inland Fisheries Research Institute\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e52\u0026deg;03'10.9\"N 21\u0026deg;02'07.4\"E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHuman controlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZAB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepartment of Pond Fishery, National Inland Fisheries Research Institute in Żabieniec\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRAS system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHuman controlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFisheries Experimental Station in Zator, National Inland Fisheries Research Institute\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50\u0026deg;00'09.7\"N 19\u0026deg;29'20.0\"E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHuman controlled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eExtraction of DNA\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe Genomic Mini AX Tissue Spin Kit (manufactured by A\u0026amp;A Biotechnology, Gdansk, Poland) was used to extract DNA from the fin samples. The extraction procedure was performed in accordance with the protocol provided by the kit manufacturer (A\u0026amp;A Biotechnology, Gdansk, Poland). The degree of DNA fragmentation was evaluated by electrophoresis in 1.5% agarose gel and the amount of DNA was measured by spectrophotometry (Nanodrop 8000, Thermo Fisher Scientific, USA) at a wavelength of 260 nm. Samples that did not show traces of fragmentation and contained at least 80 pg/\u0026micro;L and purity based on the absorption ratio of 260/280 nm wavelength equalled 1.8 or more were used for amplification of microsatellite loci in the next stage of the experiment. If a DNA sample failed to meet the above criteria, the extraction procedure was repeated.\u003c/p\u003e\u003cp\u003e\u003cem\u003eChoice of primers and optimisation of the PCR reaction conditions\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSequences of primers used to amplify microsatellites of the tench were obtained from the papers (microsatellites from \u003cem\u003eMTT-1\u003c/em\u003e to \u003cem\u003eMTT-9\u003c/em\u003e) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] (microsatellite \u003cem\u003eMFW1\u003c/em\u003e) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and (microsatellite \u003cem\u003eCYPG24)\u003c/em\u003e [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To select the optimal PCR conditions, different variants of a reaction were tested at temperatures ranging +/- 3 C\u003csup\u003e\u0026ordm;\u003c/sup\u003e relative to the values given by the authors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. DNA from eight randomly selected samples was used as a template DNA in those tests. On this stage of optimisation work reaction mixtures were prepared in a total volume of 30 \u0026micro;l with a 40 ng DNA template, a 3\u0026micro;l of 10x PCR reaction buffer for RUN Taq polymerase (A\u0026amp;A Biotechnology, Gdansk, Poland), 0.4 mM of each primer, 0.25 mM of each deoxynucleotide triphosphate (dNTP), and 1 unit RUN Taq polymerase (A\u0026amp;A Biotechnology, Gdansk, Poland). Re-distilled water (Ambion, USA) was used to bring the reaction mixture to the desired final volume. The presence or absence of a PCR product at the target length reported in the literature, as well as the presence of additional (non-specific) bands, were checked by the use of electrophoresis in agarose gel stained by Midori Green Advance, following the methodology described in the subchapter on extraction of DNA. Pairs of primers that enabled the acquisition of a clear PCR product at a length close to that reported in the papers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] were synthesised again as 5\u0026rsquo; labelled oligonucleotides. The forward oligonucleotide in each of the primer sets was labelled with fluorescent dyes 6-FAM, VIC, NED, PET to enable genotyping using Applied Biosystems 3130 Genetic Analyser (Applied Biosystems, Foster City, CA, USA). 11 microsatellite primers (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were assigned in three multiplex sets. The assignment of primers into multiplex sets followed Roche\u0026rsquo;s guidelines [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. All primer pairs in the set were checked for self-complementarity by using the Primer Pooler v1.88 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ssb22.user.srcf.net/pooler/\u003c/span\u003e\u003cspan address=\"http://ssb22.user.srcf.net/pooler/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Those with a strong tendency to create primer dimer structures were excluded. PCR amplification in multiplex mode was performed by using QIAGEN Multiplex PCR Kit (Qiagen GmbH, Germany). Following the manufacturers\u0026rsquo; instructions, the PCR primers were suspended in the TE buffer to reach a base concentration of 100 pmol/\u0026micro;l. The final concentration of each pair of the PCR primers in the multiplex PCR set was adjusted to reach a balanced yield of the PCR products (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The composition of PCR mixture followed the manufacturer\u0026rsquo;s recommendations and contained: 7.5 \u0026micro;l of QIAGEN Multiplex PCR master mix, 3 \u0026micro;l of Q solution, 0.2\u0026ndash;0.9 \u0026micro;mol of each primer (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and deionised water to a final volume of 15 \u0026micro;l.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the microsatellite markers included in the sets: Multiplex \u003cem\u003eI\u003c/em\u003e, \u003cem\u003eII\u003c/em\u003e and \u003cem\u003eIII\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNCBI Accession number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrimer\u0026rsquo;s sequence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5' dye\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMultiplex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003ePrimers \u0026micro;mol/15\u0026micro;l PCR mixture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDQ080086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:CCAGCAGAGCCCTACACTTC\u003c/p\u003e\u003cp\u003eR:AGGACGTGACCATCAACACA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCypG24\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAY439120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:CTGCCGCATCAGAGATAAACACTT\u003c/p\u003e\u003cp\u003eR:TGGCGGTAAGGGTAGACCAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDQ080087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:TTAAAACCGCCACACTTTCC\u003c/p\u003e\u003cp\u003eR:ACGTGCGGCTGTGAGATTAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6-FAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDQ080085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:CTGGTCTCCTCCTTGTGCTC\u003c/p\u003e\u003cp\u003eR:TGGGTGAAGGATTGGTTGTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-6\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDQ080089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:TGTGTGAGGTGGCACAGAAT\u003c/p\u003e\u003cp\u003eR:ATGTGAGCAATGGCTGTGAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cem\u003eII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMFW1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAY703052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:FGTCCAGACTGTCATCAGGAG\u003c/p\u003e\u003cp\u003eR:GAGGTGTACACTGAGTCACGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6-FAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDQ080090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:ACCTCGCCATGTATGCTTTT\u003c/p\u003e\u003cp\u003eR:GTTGACCTGTGCATGCATTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-8\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDQ080091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:GAAATGTCCCCACAAACCAC\u003c/p\u003e\u003cp\u003eR:GACACCGCTATCACCATCAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6-FAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-9\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDQ080092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:CAATCTGGTGGAAGTGAGCA\u003c/p\u003e\u003cp\u003eR:ACGCGTCAGTGACAGAGAGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6-FAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eIII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDQ080084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:GTCCTCGCAATGCAAGAAAT\u003c/p\u003e\u003cp\u003eR:TTGGCTCATATTGGGTGTGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDQ080088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF:GGGAGCCAGTTCACACTCAT\u003c/p\u003e\u003cp\u003eR:GACATGAAAACGGTGCTGTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eFragment analysis and genotyping\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAutomatic capillary electrophoresis was used to verify the usefulness of individual microsatellite fragments in the multiplex PCR assays and genotyping [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The length of the DNA fragment was measured by using an Applied Biosystems 3130 Genetic Analyser DNA sequencer (Applied Biosystems, Foster City, CA, USA). Determination of the size of DNA fragments was performed against the GeneScan\u0026trade; 500 LIZ\u0026reg; Size Standard (Applied Biosystems, Foster City, CA, USA). This enabled simultaneous measurement of fragments that were labelled with fluorescent dyes (6FAM, VIC, NED, PET). The mixture of chemicals used for automated capillary electrophoresis consisted of 19 \u0026micro;l Hi-Di Formamide, 0.5 \u0026micro;l GeneScan\u0026trade; 500 LIZ\u0026reg; Size Standard, and 0.5 \u0026micro;l multiplex PCR product.\u003c/p\u003e\u003cp\u003eThe separation of the DNA fragments was performed in 36 cm capillary arrays and in POP-7 polymer. Genotyping was performed by using the GeneMapper 4.0 software (Applied Biosystems, USA).\u003c/p\u003e\u003cp\u003e\u003cem\u003ePolymorphism and genetic variation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe polymorphism at the investigated loci and genetic diversity of stocks was assessed by using such indicators as: observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e), expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e), number of alleles (\u003cem\u003eNA\u003c/em\u003e) and allelic diversity (\u003cem\u003eAD\u003c/em\u003e). Values of those indicators were calculated by using the MSA software [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Exact Hardy Weinberg (H-W) test [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] was used to test for deviations from H-W equilibrium. The test was performed separately for each locus in each stock as well for all loci in the given stocks. This test was performed by using Arlequin 3.5.2 software [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The number of steps in the Markov chain equalled 1,000,000 and the number of dememorization steps equalled 100,000. The deviations were considered significant if \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;0.01. Assessment of the reduction of genetic variation as a result of bottleneck or founder effect was performed using the MSA software [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The occurrence of bottleneck or founder effects, and their influence on within-population genetic variability was based on the Garza-Williamson \u003cem\u003eM\u003c/em\u003e index (the number of alleles divided by the allelic range). This index [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], including Excoffier\u0026rsquo;s adjustment, was calculated using Arlequin 3.5.2 software [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The evaluation of inbreeding was based on Wright\u0026rsquo;s \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e inbreeding coefficient [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and comparison of the observed heterozygosity and expected heterozygosity. The positive values of this coefficient indicate inbreeding, negative for outbreeding. This coefficient was calculated by using Arlequin 3.5.2 software [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] as a part of Hierarchical Analysis of Molecular Variance (AMOVA) locus by locus analysis and averaged across the loci. Interpretation of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e values and inbreeding assessment were performed according to [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eGenetic distance and structure\u003c/em\u003e\u003c/p\u003e\u003cp\u003eGenetic divergence between stocks was analysed using two different methods: the fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and the variation in average allelic size (\u003cem\u003eδ\u0026micro;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values and their statistical significance were calculated with Arlequin 3.5.2 software [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The size of the genetic distance based on \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values, and their ranges were interpreted according to [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Higher values of this coefficient (closer 1.0) and statistical significance indicate larger genetic differences between pairs of stocks, whereas lower values (closer to 0.0) and a lack of statistical significance indicate genetic similarity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The relationships between stocks based on their genetic distance was shown as a dendrogram. Construction of this dendrogram was based on \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values. The UPGMA algorithm implemented into the D-UPGMA tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genomes.urv.cat/UPGMA/\u003c/span\u003e\u003cspan address=\"http://genomes.urv.cat/UPGMA/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to construct this dendrogram. Genetic divergence was also estimated by using the sample-size independent \u003cem\u003eδ\u0026micro;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e method [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] calculated with MSA software [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Using this method higher values indicated larger genetic differences between stocks and smaller values of minor differences.\u003c/p\u003e\u003cp\u003eThe contribution of the specific components of genetic variance to the total variance observed among all investigated group of fish was estimated by means of AMOVA [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These calculations were performed using Arlequin 3.5.2 software [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] with 1000 permutations. The threshold for significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eThe STRUCTURE 2.3.4 software was used to detect genetic structure and gene flow [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] between investigated groups of fish. The Evanno method (ΔK) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] was used to infer the true number of clusters (K) based on the rate of change in log probability among consecutive K values, which ranged from K\u0026thinsp;=\u0026thinsp;1 to K\u0026thinsp;=\u0026thinsp;10. Four iterations of each K were performed with 200,000 burnins periods and 200,000 Markov Chain Monte Carlo (MCMC) repetitions. To this end, the Clumpak program was employed to identify the optimal alignment of inferred clusters across different values of K [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eAllelic diversity\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn the investigated stocks, nine microsatellites were polymorphic. Two loci (\u003cem\u003eMTT-4\u003c/em\u003e and \u003cem\u003eMTT-7\u003c/em\u003e) were monomorphic in fish from all locations, therefore they were not included in calculations of some indicators of genetic variations such as heterozygosity, deviations from H-W equilibrium, inbreeding coefficient, \u003cem\u003eM\u003c/em\u003e value of Garza-Williamson index [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Those two microsatellites were also excluded from estimation of the genetic distances. Two other microsatellites (\u003cem\u003eMTT-3\u003c/em\u003e, and \u003cem\u003eMFW1\u003c/em\u003e) were monomorphic in some stocks and polymorphic in the others. At those loci the frequency of the second allele was low and varied from 0.01 \u003cem\u003e(MTT-3\u003c/em\u003e in ZTR) to 0.04 (\u003cem\u003eMTT-3\u003c/em\u003e in ZAB). The polymorphism of other loci (except \u003cem\u003eMTT-9\u003c/em\u003e) was not high and varied in range from two to six alleles. The locus \u003cem\u003eMTT-9\u003c/em\u003e was the most polymorphic among all investigated markers, 19 alleles were detected at this locus across fish from all investigated locations. Overall, 53 alleles were detected across 11 investigated loci. Within the investigated stocks, the degree of polymorphism was quite similar and ranged from 37 alleles (JER) to 44 alleles (BCG). The allelic diversity (\u003cem\u003eAD\u003c/em\u003e) was moderate or high and varied from 3.36 alleles per locus in JER stock to 4.00 in the BCG (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Alleles specific for given stock were found in the BCG stock (6 alleles) and the stocks of ZAB and ZTR (1 allele). All of them had a low frequency (0.01\u0026ndash;0.06) (Supplementary files - Suppl. 1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of alleles detected at the eleven investigated loci and their range: number of alleles (\u003cem\u003eNA)\u003c/em\u003e allelic diversity (\u003cem\u003eAD\u003c/em\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eStock\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of alleles across stocks, (and PCR product range in (bp))\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocus (5\u0026rsquo;dye)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBCG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZAB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZTR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJER\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-3\u003c/em\u003e (PET)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2, (150\u0026ndash;162)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCypG24\u003c/em\u003e (NED)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5, (153\u0026ndash;177)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-4\u003c/em\u003e (6-FAM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,\u0026nbsp;(207)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-2\u003c/em\u003e (VIC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2, (238\u0026ndash;242)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-6\u003c/em\u003e (VIC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6, (158\u0026ndash;176)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMFW1\u003c/em\u003e (6-FAM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2, (169\u0026ndash;175)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-7\u003c/em\u003e (PET)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1, (216)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-8 (\u003c/em\u003e6-FAM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5, (198\u0026ndash;236)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-9\u003c/em\u003e (6-FAM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19, (128\u0026ndash;180)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-1\u003c/em\u003e (PET)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5, (169\u0026ndash;177)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMTT-5\u003c/em\u003e (NED)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5, (207\u0026ndash;215)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Significant deviation from the Hardy-Weinberg equilibrium\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe coefficients of genetic variation in investigated stocks calculated on the basis of nine polymorphic loci: observed (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e) and expected (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e) heterozygosity, inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e), value of the Garza-Williamson index (\u003cem\u003eM\u003c/em\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eStock\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAcross all stocks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBCG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZAB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZTR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJER\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eHeterozygosity, Hardy-Weinberg equilibrium, and inbreeding coefficient\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo evaluate genetic variation in stocks, the observed (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e) and expected (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e) heterozygosity was calculated (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In general, genetic variation described by this indicator was low, with average values for observed and expected heterozygosity of 0.42 and 0.47, respectively. The values of \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e for individual stocks were quite similar across all investigated locations. In all of them, observed heterozygosity was slightly lower than the expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e \u0026gt;\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In all stocks, the mean \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e value was close to the average percentage of heterozygotes (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e) expected at H-W equilibrium. When calculated across all markers, departures from this equilibrium were not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Significant departures (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;0.01) were found at the level of individual loci (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In most cases they were at locus \u003cem\u003eMTT-8\u003c/em\u003e. Departures at more than one locus were observed only in the BCG stock (three loci).\u003c/p\u003e\u003cp\u003ePositive values of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e indicating inbreeding were found in all stocks, but they differed in values of this indicator. In the BCG, the inbreed was high (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e=0.20) and this was the highest score among all investigated groups of fish. In all other stocks, inbreed was moderate (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e 0.06\u0026ndash;0.11). The lowest value of this indicator was calculated for JER stock (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e=0.06).\u003c/p\u003e\u003cp\u003e\u003cem\u003eBottleneck and founder effects\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe average Garza-Williamson \u003cem\u003eM\u003c/em\u003e index [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] value across the investigated stocks was 0.52. This value was lower than 0.68, which indicates that founder and/or bottleneck effects had a significant impact on genetic variations in these stocks (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The \u003cem\u003eM\u003c/em\u003e value was highest in ZAB stock (0.60), indicating a relatively small reduction of genetic variation resulting from bottleneck and/or founder effect. The stock ZAB is the only one where the \u003cem\u003eM\u003c/em\u003e index was equal to or greater than 0.60. The lowest \u003cem\u003eM\u003c/em\u003e values were in stock of JER (0.45) and BCG (0.49), which suggests a slightly larger reduction of genetic variation within them than in ZAB stock (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eGenetic divergence between stocks\u003c/em\u003e\u003c/p\u003e\u003cp\u003eBased on \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values, the genetic distances amongst most of stocks varied in range \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e 0.009\u0026ndash;0.049 and was classified as small (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u0026lt;0.05). Only in one pair was it higher than 0.050 and was classified as moderate (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e 0.05\u0026ndash;0.14) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This largest genetic distance (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e=0.051) was found between stocks: the ZAB and ZTR. The smallest distance was observed between the BCG and ZTR stock (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e=0.009), and this distance was not significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) All other genetic distances were significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenetic distance between stocks and tench stocks estimated by using \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eδ\u0026micro;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e methods.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eGenetic distance (δ\u003cem\u003e\u0026micro;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eGenetic distance \u003cem\u003e(F\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBCG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Genetic distance not significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe magnitudes of the genetic distances between stocks were also estimated using the \u003cem\u003eδ\u0026micro;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e method (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This method confirmed that genetic distances between all tench stocks is small. The most differentiated were stocks BCG and JER (0.562) and the closest to each other were stocks ZTR and ZAB (0.073) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Bayesian estimation of genetic structure and individual membership indicated that the maximum value of ΔK using Evanno method [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] were for K\u0026thinsp;=\u0026thinsp;3 (ΔK\u0026thinsp;=\u0026thinsp;26.4) and K\u0026thinsp;=\u0026thinsp;5 (ΔK 26.2). When using median values of Ln (Pr Data), an optimal K\u0026thinsp;=\u0026thinsp;5 (Supplementary files \u0026ndash; Suppl. 2). This K value was closer to our field observations than K\u0026thinsp;=\u0026thinsp;5. The size of genetic distance between tench stocks was confirmed in Bayesian analysis performed by using STRUCTURE 2.3.4 software. Because this distance was small, fish from all locations could be assigned to more than one stock and differed only in likelihood of this assignment. This analysis revealed genetic differences inside ZAB stock. Both in K\u0026thinsp;=\u0026thinsp;3 and K\u0026thinsp;=\u0026thinsp;5 scenarios, tench from the Experimental Fisheries Station in Żabieniec (ZAB1) and the Department of Pond Fishery in Żabieniec (ZAB2) were different. The individuals from ZAB1 had their own unique cluster (violet) and a much higher probability of assigning into it than any other cluster (). Moreover, this analysis indicated that fish from BCG were very similar to individuals from ZTR, and fish from ZAB2 were similar to JER stock (Supplementary files \u0026ndash; Suppl. 3).\u003c/p\u003e\u003cp\u003eAMOVA revealed that the variation of all samples was 2.179 and the sum of squares was 861.4. The most important component of this variation was that within individuals (1.882 and the sum of squares 376.5). This class of variation was responsible for 86.4% of the total variance among all samples. The other components were: the variation among individuals within a stock (0.217, sum of squares 454,0 and 10.0%) and among stocks (0.080, sum of squares 30.882 and 3.7%). Their share in the whole genetic variation was low.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eGenetic variation in stocks\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe comparison of the microsatellite DNA variation revealed that genetic diversity in each of four stocks was similar to that reported in German stock/populations (33 alleles at 9 microsatellite loci, \u003cem\u003eAD\u003c/em\u003e 3.7) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and Hungarian populations (29\u0026ndash;50 alleles, at 12 loci, \u003cem\u003eAD\u003c/em\u003e 2.41\u0026ndash;4.16) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The biggest differences in the number of alleles between stock investigated by Kohlmann and Kersten [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and Polish stocks were found at loci \u003cem\u003eMTT-3\u003c/em\u003e and \u003cem\u003eMTT-9\u003c/em\u003e. In our studies, a locus \u003cem\u003eMTT-3\u003c/em\u003e was polymorphic, and polymorphism at locus \u003cem\u003eMTT-9\u003c/em\u003e (19 alleles) was higher than 9 alleles reported by Kohlmann and Kersten [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, the differences in allelic length were observed at some loci. Those differences are probably a result of the more diverse sample size in our study (200 fish, 5 stocks) and use of a different size standard in automatic DNA electrophoresis than that used by Kohlmann and Kersten [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It should be verified by investigation of more populations and stock whether monomorphism of microsatellite \u003cem\u003eMTT-4\u003c/em\u003e and \u003cem\u003eMTT-7\u003c/em\u003e is typical for this species or if they present monomorphism in some populations and polymorphism in others, as \u003cem\u003eMTT-3\u003c/em\u003e does.\u003c/p\u003e\u003cp\u003eThe fish from Bogaczewo (BCG) are those where the highest number of alleles detected. This can be explained by the fact that it is the only one among those investigated in this paper that spawns naturally. It is known that natural breeding helps maintain rare alleles in the population [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A small number of alleles in the cultured stocks is a consequence of decline of genetic variability as a result of breeding controlled by humans. This decline results mainly in a loss of rare alleles rather than a reduced heterozygosity and is probably a result of a relatively low number of fish being used to maintain these stocks [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Such situations are typical for fish culture due to the generally high fecundity of females. In the long term this may lead to measurable inbreeding depressions such as reduced vitality and growth rate.\u003c/p\u003e\u003cp\u003eIn investigated stocks, both observed and expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e: 0.17\u0026ndash;0.58, \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e: 0.25\u0026ndash;0.54) were in the upper range of reported by Kohlmann and Kersten [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Kohlmann et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and Al Fatle et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] for German and Hungarian populations. A common feature of fish investigated in this study and most Hungarian populations and stocks reported by Al Fatle et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was an excess of \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e over \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e. This could be a consequence of several factors, of which instability of environment and inbreeding are probably the most important. This is not surprising, because tench often inhabit shallow water bodies that are vulnerable to drying, resulting in bottlenecks and inbreeding. Fish from Bogaczewo (BCG) inhabit such a water body that is vulnerable to environmental changes, resulting in bottlenecks and inbreeding. Consequently, the most numerous deviations from the H-W equilibrium, the highest (0.06) difference between \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e the highest value of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e and one of the lowest values of the \u003cem\u003eGW\u003c/em\u003e index are observed in this stock.\u003c/p\u003e\u003cp\u003eThe founder and bottleneck effects are known to be important factors that determine genetic variation in broodstock and genetic characteristics of conserved populations [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The results of Garza-Williamson \u003cem\u003eM\u003c/em\u003e index being lower than the critical value of 0.68 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] suggest that none of the studied stocks avoided a reduction in genetic variation. However, there are some differences in causes and magnitude of this reduction among the stocks. The lowest values observed in JER stock are probably the consequence of founder effect and relatively low genetic variation in the group of fish used in establishing this stock. A genetic variation in BCG was probably reduced as a result of unstable environments and bottleneck effects. Fish from ZAB were probably the ones that suffered the least reduction of genetic variation as a result of bottleneck or founder effect. This is not surprising, knowing that there are two stocks in this location, they are separated from each other and have different origins.\u003c/p\u003e\u003cp\u003eThe progressive inbreeding of broodstocks or small isolated populations is a well-known problem in the conservation of fish species or in their aquaculture. It is worth noting that the actual moderate and high inbreed in Polish tench stocks is similar to that observed by Al Fatle et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] in their Hungarian counterparts (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e -0.03-0.25) and can be evaluated as typical for this species. Although in BCG stock this inbreed is higher than in other investigated stocks, it could be a consequence of environmental stress [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] that may have occurred in the waterbody it inhabited. According to the author\u0026rsquo;s knowledge no indications of inbreeding depressions are observed at the investigated locations, but monitoring of inbreeding and other indicators of genetic variation is recommended.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGenetic distance and structure\u003c/em\u003e\u003c/p\u003e\u003cp\u003eBiology of tench, their spawning behavior [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and possible transfer of the eggs between water bodies by water birds prevents geographic isolation of their populations. Moreover, human induced activities such as stocking of tench juveniles or transferring them between locations, decrease genetic differences between populations of this species [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In natural conditions, networks of the tench population are part of metapopulations belonging to the eastern or western phylogroups of this species [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. It is known that some gene flow between them is common and the differentiation rate of the tench population is slow [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePairwise genetic distances between the stocks reported in this paper are in a range typical for a western phylogroup of this fish (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e in range 0.008\u0026ndash;0.159) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, a much greater genetic distance between some Hungarian populations (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e up to 0.219) has been reported [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These unusually high values were a consequence of population-specific factors such as increased inbreeding, bottlenecks, and strong genetic drift [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and cannot be considered typical for the species. Genetic characteristics among fish belonging to the tench phylogroup are usually quite similar, especially in the areas of contact [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the genetic distance between tench stocks in Poland is generally small or moderate, the stocks nonetheless differ from one another, and the differences reflect their respective histories. A Bayesian analysis of the genetic structure revealed that the stocks from the Fisheries Experimental Station in Żabieniec (ZAB1) and the Department of Pond Fishery in Żabieniec (ZAB2) are genetically distinct. Those differences are due to the stock from the Department of Pond Fishery being maintained in controlled conditions through the use of RAS systems, and being physically separated from fish belonging to the Fisheries Experimental Station, which are maintained exclusively in ponds. Moreover, fish from those two stocks have different origins and have never been crossbred with each other. A tench broodstock from the Pond Fisheries Department was raised from tench larvae obtained some years ago from JER Fish Farm and this fact explains the genetic similarity of fish ZAB2 and JER. It is difficult to explain a genetic similarity between the tench from Bogaczewo (BCG) and the Fisheries Experimental Stations in Zator (ZTR). According to the information provided by the owner of the Bogaczewo Fish Farm, those fish are native, and no imports were made, therefore this similarity can be evaluated as random. A relatively large genetic difference between the stocks ZAB and ZTR are a consequence of their isolation from each other, founder effect and probably genetic drift. To our knowledge those two stocks did not have any close ancestors, and they were not transferred between those two locations. Although genetic differences between investigated stocks are clearly detectable and, in many cases, significant, they are only a small portion the overall genetic variation observed among the investigated fish. Consequently, a genetic profiling of individuals and assembly into spawning pairs of fish that differ the most to each other, may have been a better approach in a preventing a decrease of genetic variation than random transfers between stocks. Moreover, genetically different fish from ZAB1 and ZAB2, can be useful if an increase in genetic variation of this species is needed.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe genetic variation of tench in Polish stocks is moderate and comparable to that observed in other European populations. The genetic diversity of the examined stocks has been reduced due to founder effects, bottlenecks, inbreeding, and human-controlled breeding practices. Our study revealed moderate to low genetic distances between these stocks, which are typical for tench populations across the Central European range of the species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the Ministry of Agriculture and Rural Development Republic of Poland under the task nr. RYB.rs.070.3.2024 and by Research Task Z-020 of the Department Pond Fishery, National Inland Fisheries Research Institute, Olsztyn, Poland.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, J.W. and D.K.; Methodology, M.G., J.C. and D.K.; Validation, D.K.; Formal Analysis, D.K.; Investigation, M.G., J.C., D.K. and A.N.; Resources, J.W.; Data Curation, D.K.; Writing \u0026ndash; Original Draft Preparation, D.K., J.C. and J.W.; Writing \u0026ndash; Review \u0026amp; Editing, D.K., J.C. J.W., M.G. and A.N.; Visualization, D.K.; Supervision, D.K.; Project Administration, D.K.; Funding Acquisition, D.K. and J.W.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are stored in the Open Science Framework repository [https://osf.io/uk7pz/?view_only=203e5fe0fa8d47e0bacdbcaf3b8f9c15](https:/osf.io/uk7pz/?view_only=203e5fe0fa8d47e0bacdbcaf3b8f9c15) , and will be made fully publicly available upon acceptance of the manuscript for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSzczerbowski, J. Tench in \u003cem\u003eInland fisheries\u003c/em\u003e. (ed. \u003cem\u003eSzczerbowski J)\u003c/em\u003e 268\u0026ndash;270 (1993).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrylinska, M., Brylinski, E., Bninska, M. \u0026amp; Tinca tinca in \u003cem\u003eThe freshwater fishes of europe\u003c/em\u003e (ed. Banarescu, P.) 5/I, 229\u0026ndash;302 (1999).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkrzypczak, A. \u0026amp; Mamcarz, A. Changes in commercially exploited populations of tench, \u003cem\u003eTinca tinca\u003c/em\u003e (L.), in \u003cem\u003eLakes of northeastern poland\u003c/em\u003e. \u003cem\u003eAquac Int.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 179\u0026ndash;193 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBninska, M. The effect of recreational uses upon aquatic ecosystems and fish resources. in \u003cem\u003eHabitat modification and freshwater fisheries\u003c/em\u003e (ed. Alabaster, J.) 223\u0026ndash;235 (1985).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeopold, M., Bninska, M. \u0026amp; Nowak, W. Commercial fish catches as an index of lake eutrophication. \u003cem\u003eArch. Hydrobiol.\u003c/em\u003e \u003cb\u003e106\u003c/b\u003e, 513\u0026ndash;524 (1986).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdamek, Z., Sukop, I., Rendon, P. M. \u0026amp; Kouril, J. Food competition between 2\u0026thinsp;+\u0026thinsp;tench (\u003cem\u003eTinca tinca\u003c/em\u003e L.), common carp (\u003cem\u003eCyprinus carpio\u003c/em\u003e L.) and bigmouth buffalo (\u003cem\u003eIctiobus cyprinellus\u003c/em\u003e Val.) in pond polyculture. \u003cem\u003eJ. Appl. Ichthyol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 165\u0026ndash;169 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKohlmann, K., Kersten, P. \u0026amp; Flajšhans, M. Comparison of microsatellite variability in wild and cultured tench (\u003cem\u003eTinca tinca\u003c/em\u003e). \u003cem\u003eAquaculture\u003c/em\u003e \u003cb\u003e272\u003c/b\u003e, 147\u0026ndash;151 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndreji, J., Dvorak, T., Randak, T. \u0026amp; Turek, J. Breeding of stock for open waters and their stocking in \u003cem\u003eFishery in open waters\u003c/em\u003e (ed. Randak, T.) 230\u0026ndash;231 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMickiewicz, M. \u0026amp; Wołos, A. Economic ranking of the importance of fish species to lake fisheries stocking management in Poland. \u003cem\u003eFisheries \u0026amp;Aquatic Life\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, 11\u0026ndash;18 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXing, L., Quist, T. S., Stevenso, T. J., Dahlem, T. J. \u0026amp; Bonkowsky, J. L. Rapid and efficient zebrafish genotyping using PCR with high-resolution melt analysis. \u003cem\u003eJ. Vis. Exp.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, e51138 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKohlmann, K. \u0026amp; Kersten, P. Microsatellite loci in tench: isolation and variability in a test population. \u003cem\u003eAquac Int.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 3\u0026ndash;7 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrooijmans, R. P., Poel, J. V., Groenen, M. A., Bierbooms, V. A. \u0026amp; Komen, J. Microsatellite markers in common carp (\u003cem\u003eCyprinus carpio\u003c/em\u003e L). \u003cem\u003eAnim. Genet.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 129\u0026ndash;134 (1997).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaerwald, M. R. \u0026amp; May, B. Characterization of microsatellite loci for five members of the minnow family Cyprinidae found in the Sacramento\u0026ndash;San Joaquin Delta and its tributaries. \u003cem\u003eMol. Ecol. Notes\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e, 385\u0026ndash;390 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoche. Optimization of Reactions to Reduce Formation of Primer Dimers. Roche Molecular Biochemicals Technical Note 1/99. (1999). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gene-quantification.de/roche-primer-dimer.pdf\u003c/span\u003e\u003cspan address=\"http://www.gene-quantification.de/roche-primer-dimer.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown, S. S. et al. ​PrimerPooler: automated primer pooling to prepare library for targeted sequencing. \u003cem\u003e​Biol Methods Protoc.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 1\u0026ndash;10 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eButler, J. M., Ruitberg, C. M. \u0026amp; Vallone, P. M. Capillary electrophoresis as a tool for optimization of multiplex PCR reactions. \u003cem\u003eFresenius\u0026rsquo; J. Anal. Chem.\u003c/em\u003e \u003cb\u003e369\u003c/b\u003e, 200\u0026ndash;205 (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDieringer, D. \u0026amp; Schl\u0026ouml;tterer, C. Microsatellite analyser (MSA): a platform independent analysis tool for large microsatellite data sets. \u003cem\u003eMol. Ecol. Notes\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, 167\u0026ndash;169 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNei, M. \u003cem\u003eMolecular Evolutionary Genetics\u003c/em\u003e (Columbia University, 1987).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eExcoffier, L. \u0026amp; Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 564\u0026ndash;567 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarza, J. \u0026amp; Williamson, E. Detection of reduction in population size using data from microsatellite loci. \u003cem\u003eMol. Ecol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 305\u0026ndash;318 (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright, S. The genetical structure of populations. \u003cem\u003eAnn. Eugen\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 323\u0026ndash;354 (1951).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHedrick, P. W. \u0026amp; Kalinowski, S. T. Inbreeding Depression in Conservation Biology. \u003cem\u003eAnnu. Rev. Ecol. Evol. Syst.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 139\u0026ndash;162 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLlambi, S. et al. Genetic structure and population dynamics of autochthonous and modern porcine breeds. Analysis of the IGF2 and MC4R genes that determine carcass characteristics. \u003cem\u003eAustral J. Veterinary Sci.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e, 87\u0026ndash;94 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoldstein, D. B., Linares, A. R., Cavalli-Sforza, L. L. \u0026amp; Feldman, M. W. An evaluation of genetic distances for use with microsatellite loci. \u003cem\u003eGenetics\u003c/em\u003e \u003cb\u003e139\u003c/b\u003e, 463\u0026ndash;471 (1995).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright, S. Evolution and the Genetics of Population, Variability Within and Among Natural Populations. \u003cem\u003eThe Univ. Chic. Press\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, (1978).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalloux, F. \u0026amp; Lugon-Moulin, N. The estimation of population differentiation with microsatellite markers. \u003cem\u003eMol. Ecol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 155\u0026ndash;165 (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eExcoffier, L. \u0026amp; Slatkin, M. Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. \u003cem\u003eMol. Biol. Evol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 921\u0026ndash;927 (1995).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichalalakis, Y. \u0026amp; Excoffier, L. A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci. \u003cem\u003eGenetics\u003c/em\u003e \u003cb\u003e142\u003c/b\u003e, 1061\u0026ndash;1064 (1996).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePritchard, J. K., Stephens, M. \u0026amp; Donnelly, P. Inference of population structure using multilocus genotype data. \u003cem\u003eGenetics\u003c/em\u003e \u003cb\u003e155\u003c/b\u003e, 945\u0026ndash;959 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEvanno, G., Regnaut, S. \u0026amp; Goudet, J. Detecting the number of clusters of individuals using the software structure: a simulation study. \u003cem\u003eMol. Ecol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 2611\u0026ndash;2620 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. \u0026amp; Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1179\u0026ndash;1191 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl Fatle, F. A. et al. Genetic structure and diversity of native tench (\u003cem\u003eTinca tinca\u003c/em\u003e L. 1758) populations in Hungary\u0026mdash;establishment of basic knowledge base for a breeding program. \u003cem\u003eDiversity\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 336 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBecker, P. A. et al. Inbreeding avoidance influences the viability of reintroduced populations of African wild dogs (\u003cem\u003eLycaon pictus\u003c/em\u003e). \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, e37181 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eExadactylos, A., Rigby, M. J., Geffen, A. J. \u0026amp; Thorpe, J. P. Conservation aspects of natural populations and captive-bred stocks of turbot (Scophthalmus maximus) and Dover sole (Solea solea) using estimates of genetic diversity. \u003cem\u003eICES J. Mar. Sci.\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e, 1173\u0026ndash;1181 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFox, C. W. \u0026amp; Reed, D. H. Inbreeding depression increases with environmental stress: an experimental study and meta-analysis. \u003cem\u003eEvolution\u003c/em\u003e \u003cb\u003e65\u003c/b\u003e, 246\u0026ndash;258 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLajbner, Z., Kohlmann, K., Linhart, O. \u0026amp; Kotl\u0026iacute;k, P. Lack of reproductive isolation between the Western and Eastern phylogroups of the tench. \u003cem\u003eRev. Fish. Biol. Fish.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 289\u0026ndash;300 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLajbner, Z. \u0026amp; Kotlik, P. PCR-RFLP assays to distinguish the Western and Eastern phylogroups in wild and cultured tench \u003cem\u003eTinca tinca\u003c/em\u003e. \u003cem\u003eMol. Ecol. Resour.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 374\u0026ndash;377 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaraiskou, N. et al. Genetic structure and divergence of tench \u003cem\u003eTinca tinca\u003c/em\u003e European populations. \u003cem\u003eJ. Fish. Biol.\u003c/em\u003e \u003cb\u003e97\u003c/b\u003e, 930\u0026ndash;934 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChiu, M-C., Nukazawa, K., Resh, V. H. \u0026amp; Watanabe, K. Environmental effects, gene flow and genetic drift: Unequal influences on genetic structure across landscapes. \u003cem\u003eJ. Biogeogr.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 352\u0026ndash;364 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"tench, genetic variation, microsatellite DNA, conservation genetics","lastPublishedDoi":"10.21203/rs.3.rs-7171524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7171524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe tench (\u003cem\u003eTinca tinca\u003c/em\u003e) is a freshwater fish that inhabits shallow lakes, small water bodies and lower parts of rivers. This fish is important in the conservation of the biodiversity of European ichthyofauna and aquaculture. In this paper we have estimated the genetic variability of stocks of tench from five distanced locations, initializing a program of genetic studies of this fish in Poland. The genetic variation in the investigated group of fish was moderate. Observed and expected heterozygosity was in range 0.40\u0026ndash;0.45 and 0.44\u0026ndash;0.48 respectively. Between 37\u0026ndash;44 alleles were detected in investigated stocks. In all investigated stocks, genetic variation was reduced because of bottleneck and founder effect (Garza-Williamson \u003cem\u003eM\u003c/em\u003e index in range 0.45\u0026ndash;0.60) and inbreeding. Genetic distance between the investigated groups of fish was small or moderate. Analysis of genetic structure revealed genetic differences between two stocks from Żabieniec and confirmed their different origin. The genetic variation of stocks included in this study was typical for the Central European area of occurrence of this species.\u003c/p\u003e","manuscriptTitle":"Microsatellite-Based Assessment of Genetic Variation in Tench (Tinca tinca) from Different Polish Regions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 19:56:18","doi":"10.21203/rs.3.rs-7171524/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8377e329-1502-43ff-a5f4-49a49f37e3dc","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53292395,"name":"Biological sciences/Ecology"},{"id":53292396,"name":"Earth and environmental sciences/Ecology"},{"id":53292397,"name":"Biological sciences/Genetics"},{"id":53292398,"name":"Biological sciences/Molecular biology"},{"id":53292399,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2025-12-12T09:24:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 19:56:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7171524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7171524","identity":"rs-7171524","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}