| Literature DB >> 32040756 |
Rafał Bernaś1, Anna Wąs-Barcz2.
Abstract
The history of brown trout Salmo trutta L. stocking has long tradition in the European Union and other countries. Hundreds of hatchery facilities on continent have artificial broodstocks used for enhancement of neighbouring and also geographically far river basins. These practices have substantial effect on wild brown trout populations. To illuminate this phenomenon, eleven hatchery stocks and wild populations from northern Poland and Carpathian region were analysed using 13 microsatellite markers. Obtained results revealed high genetic diversity between studied stocks and clear differentiation between northern and southern populations and hybridization between these two major clads. As a recommendation, the principle of treating regions as metapopulations should be applied, which, in the case of Poland, means using the division of the northern and southern genetic lines that were revealed in the present study.Entities:
Keywords: Brown trout; Hatchery lines; Stocking
Mesh:
Year: 2020 PMID: 32040756 PMCID: PMC7148263 DOI: 10.1007/s13353-020-00548-6
Source DB: PubMed Journal: J Appl Genet ISSN: 1234-1983 Impact factor: 3.240
Numbers of brown trout specimens examined, sampling date, place, age, ecological form, origin, and basin
| Stock | Date | Place | Age | Form | Origin | Basin | |
|---|---|---|---|---|---|---|---|
| PF | 33 | Fall 2017 | Folusz | Parr | Resident | Hatchery | Vistula River, Baltic Sea |
| SV | 44 | Fall 2017 | Slovryb | Parr | Resident | Hatchery | Danube, Black Sea |
| MS | 46 | Fall 2018 | Myślenice | Adults | Resident | Hatchery | Vistula River, Baltic Sea |
| PS | 35 | Fall 2018 | Pasłęka | Adults | Resident | Wild/hatchery | Baltic Sea |
| CJ | 27 | Spring 2017 | Czarci Jar | Fry | Resident | Hatchery | Vistula River, Baltic Sea |
| BR | 54 | Spring 2017 | Rumia | Fry | Resident | Hatchery | Baltic Sea |
| PD | 40 | Fall 2016 | Dąbie | Adults | Resident | Hatchery | |
| RU | 30 | Fall 2016 | Rutki | Adults | Resident | Hatchery | Radunia River, Baltic Sea |
| MU | 37 | Summer 2017 | Mogilica | Parr | Resident | Wild | Parsęta River, Baltic sea |
| PA | 44 | Fall 2017 | Parsęta | Adults | Anadromous | Wild | Baltic Sea |
| RE | 44 | Fall 2016 | Rega | Adults | Anadromous | Wild | Baltic Sea |
Fig. 1Map showing locations of the brown trout (Salmo trutta) stocks sampled and analysed
Basic statistics of eleven brown trout stocks from the southern Baltic and the Carpathian area. N, number of analysed fish; M, mean allele number; H, observed heterozygosity; H, expected heterozygosity; A, allelic richness; P, private alleles; DHWE, Hardy–Weinberg equilibrium deviations; F, stock-specific inbreeding coefficient (significant values are italicized)
| Stock | DHWE | |||||||
|---|---|---|---|---|---|---|---|---|
| PF | 33 | 9.77 | 0.71 | 0.72 | 9.01 | 0.85 | 1 | 0.02 |
| SV | 44 | 10.23 | 0.71 | 0.74 | 8.85 | 0.89 | 2 | |
| MS | 46 | 6.31 | 0.69 | 0.69 | 5.87 | 0.03 | 1 | 0.00 |
| PS | 35 | 9.15 | 0.71 | 0.72 | 8.41 | 0.45 | 1 | 0.01 |
| CJ | 27 | 5.77 | 0.62 | 0.61 | 5.69 | 0.03 | 0 | − 0.01 |
| BR | 54 | 7.54 | 0.69 | 0.68 | 6.71 | 0.18 | 1 | 0.00 |
| PD | 40 | 4.92 | 0.54 | 0.54 | 4.68 | 0.05 | 0 | 0.00 |
| RU | 30 | 4.85 | 0.59 | 0.58 | 4.72 | 0.13 | 1 | − 0.01 |
| MU | 37 | 3.62 | 0.48 | 0.50 | 3.54 | 0.1 | 3 | |
| PA | 44 | 10.39 | 0.64 | 0.70 | 8.91 | 0.26 | 1 | |
| RE | 44 | 10.00 | 0.71 | 0.70 | 8.67 | 0.36 | 0 | − 0.01 |
Genetic diversity indices for the eleven investigated brown trout stocks. FST values for pairwise comparisons of eleven brown trout stocks, which were all significant (P = 0.05), are below the diagonal; the average numbers of within-stocks pairwise differences are on the diagonal in italic characters; Nei’s genetic distances DA are above the diagonal
| PF | SV | MS | PS | CJ | BR | PD | RU | MU | PA | RE | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| PF | 0.272 | 0.494 | 0.368 | 0.761 | 0.548 | 0.995 | 1.036 | 1.702 | 0.420 | 0.534 | |
| SV | 0.028 | 0.526 | 0.330 | 0.847 | 0.480 | 0.947 | 0.837 | 1.748 | 0.444 | 0.551 | |
| MS | 0.051 | 0.053 | 0.606 | 1.059 | 0.695 | 1.263 | 1.186 | 1.721 | 0.485 | 0.655 | |
| PS | 0.038 | 0.033 | 0.062 | 0.544 | 0.290 | 0.744 | 0.922 | 1.612 | 0.198 | 0.276 | |
| CJ | 0.080 | 0.085 | 0.109 | 0.058 | 0.389 | 0.877 | 1.340 | 1.705 | 0.430 | 0.576 | |
| BR | 0.057 | 0.049 | 0.072 | 0.031 | 0.043 | 0.820 | 0.971 | 1.505 | 0.292 | 0.462 | |
| PD | 0.110 | 0.101 | 0.135 | 0.084 | 0.106 | 0.092 | 0.343 | 2.191 | 0.785 | 1.173 | |
| RU | 0.109 | 0.086 | 0.123 | 0.097 | 0.147 | 0.103 | 0.045 | 2.225 | 0.838 | 1.193 | |
| MU | 0.178 | 0.175 | 0.179 | 0.170 | 0.194 | 0.159 | 0.244 | 0.242 | 1.059 | 1.327 | |
| PA | 0.044 | 0.045 | 0.051 | 0.021 | 0.047 | 0.031 | 0.088 | 0.090 | 0.115 | 0.143 | |
| RE | 0.055 | 0.055 | 0.068 | 0.029 | 0.062 | 0.049 | 0.126 | 0.123 | 0.140 | 0.015 |
Proportion of baseline individuals correctly assigned to their own stocks and share of largest misidentifications (%)
| Stock | % correctly assigned | % of largest misidentification | ||
|---|---|---|---|---|
| PF | 32 | 84.40 | 9.40 | SV |
| SV | 44 | 90.90 | 6.80 | PF |
| MS | 46 | 93.50 | 4.30 | PF |
| PS | 35 | 65.70 | 14.30 | RE |
| CJ | 27 | 81.50 | 7.40 | BR |
| BR | 54 | 87.00 | 3.70 | CJ |
| PD | 40 | 82.50 | 17.50 | RU |
| RU | 30 | 93.30 | 6.70 | PD |
| MU | 44 | 100.0 | ||
| PA | 44 | 59.10 | 25.00 | RE |
| RE | 44 | 52.30 | 25.00 | PA |
Fig. 2Clustering of 434 specimens from eleven stocks with putative K = 4. Each individual is represented by a column divided into K shades with each shade representing a cluster
Fig. 3A neighbour-joining tree based on Nei’s distances among the eleven brown trout stocks. Bootstrap probabilities are shown on the tree