Advertisement

Conservation Genetics

, Volume 19, Issue 5, pp 1123–1136 | Cite as

Population genetic structure after 125 years of stocking in sea trout (Salmo trutta L.)

  • Christoph PetereitEmail author
  • Dorte Bekkevold
  • Sascha Nickel
  • Jan Dierking
  • Harry Hantke
  • Albrecht Hahn
  • Thorsten Reusch
  • Oscar Puebla
Research Article

Abstract

Stocking can be an effective management and conservation tool, but it also carries the danger of eroding natural population structure, introducing non-native strains and reducing genetic diversity. Sea trout, the anadromous form of the brown trout (Salmo trutta), is a highly targeted species that is often managed by stocking. Here, we assess the present-day population genetic structure of sea trout in a backdrop of 125 years of stocking in Northern Germany. The study area is characterized by short distances between the Baltic and North Sea river watersheds, historic use of fish from both watersheds for stocking, and the creation of a potential migration corridor between the Baltic and North Sea with the opening of the Kiel Canal 120 years ago. A survey of 24 river systems with 180 SNPs indicates that moderate but highly significant population genetic structure has persisted both within and between the Baltic and North Sea. This genetic structure is characterized by (i) heterogeneous patterns of admixture between the Baltic and North Sea that do not correlate with distance from the Kiel Canal and are therefore likely due to historic stocking practises, (ii) genetic isolation by distance in the Baltic Sea at a spatial scale of < 200 km that is consistent with the homing behaviour of sea trout, and (iii) at least one genetically distinct Baltic Sea river system. In light of these results, we recommend keeping fish of North Sea and Baltic Sea origin separate for stocking, and restricting Baltic Sea translocations to neighbouring river systems.

Keywords

Sea trout Population genetics SNPs Stocking Baltic Sea Kiel Canal 

Notes

Acknowledgements

CP was funded by the Federal-Fishing Fund “Fischereiabgabe” of Schleswig-Holstein, the European Fisheries Fund (EFF) and GEOMAR through the Ministry of Energy, Agriculture, the Environment and Rural Areas of Schleswig-Holstein in the frame of the projects “Sea Trout Literature Study, ParrQuant, VariParr & SMARRT (30/SH305E)”. We thank all people involved in the electrofishing campaigns conducted for tissue sampling, especially Tim Kuchenbecker, Jens Wein, Sebastian Albrecht, Enno Prigge and the staff of the Fish Hatchery Altmühlendorf. We also acknowledge the respective local Water and Soil Organizations for permissions and access to their rivers and the Angling Clubs for their help during field samplings.

Supplementary material

10592_2018_1083_MOESM1_ESM.eps (1.4 mb)
Supplementary material 1 (EPS 1445 KB)
10592_2018_1083_MOESM2_ESM.eps (472 kb)
Supplementary material 2 (EPS 471 KB)
10592_2018_1083_MOESM3_ESM.eps (472 kb)
Supplementary material 3 (EPS 472 KB)
10592_2018_1083_MOESM4_ESM.xlsx (14 kb)
Supplementary material 4 (XLSX 14 KB)

References

  1. Aprahamian MW, Smith KM, McGinnity P, McKelvey S, Taylor J (2003) Restocking of salmonids—opportunities and limitations. Fish Res 62:211–227CrossRefGoogle Scholar
  2. Araki H, Berejikian BA, Ford MJ, Blouin MS (2008) Fitness of hatchery-reared salmonids in the wild. Evol Appl 1(2):342–355CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bell JD, Leber KM, Blankenship HL, Loneragan NR, Masuda R (2008) A new era for restocking, stock enhancement and sea ranching of coastal fisheries resources. Rev Fish Sci 16:1–9CrossRefGoogle Scholar
  4. Benjamini Y, Yekutieli D (2001) The control of false discovery rate under dependency. Ann Stat 29:1165–1188CrossRefGoogle Scholar
  5. Berg OK, Berg M (1987) Migrations of sea trout, Salmo trutta L., from the Vardnes river in northern Norway. J Fish Biol 31(1):113–121CrossRefGoogle Scholar
  6. Butler JAR, Radford A, Riddington G, Laughton R (2009) Evaluating an ecosystem service provided by Atlantic salmon, sea trout and other fish species in the river Spey, Scotland: the economic impact of recreational rod fisheries. Fish Res 96(2–3):259–266CrossRefGoogle Scholar
  7. Cowx IG (1994) Stocking strategies. Fish Manage Ecol 1:15–30CrossRefGoogle Scholar
  8. Cowx IG (1998) Stocking and introduction of fish. Fishing News Books, Blackwell Science, OxfordGoogle Scholar
  9. Degerman E, Leonardsson K, Lundqvist H (2012) Coastal migrations, temporary use of neighboring rivers, and growth of sea trout (Salmo trutta) from nine northern Baltic Sea rivers. ICES J Mar Sci 69:971–980CrossRefGoogle Scholar
  10. DiCiccio TJ, Efron B (1996) Bootstrap confidence intervals. Stat Sci 11:189–228CrossRefGoogle Scholar
  11. Dierking J, Phelps L, Praebel K, Ramm G, Borcherding J, Brunke M, Eizaguirre C (2014) Anthropogenic hybridization between endangered migratory and commercially harvested stationary whitefish taxa (Coregonus ssp.). Evol Appl 7(9):1068–1083.  https://doi.org/10.1111/eva.12166 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Drywa A, Poćwierz-Kotus A, Wąs A, Dobosz S, Kent MP, Lien S, Bernaś R, Wenne R (2013) Genotyping of two populations of southern Baltic Sea trout Salmo trutta m. trutta using an Atlantic salmon derived SNP-array. Mar Genom 9:25–32CrossRefGoogle Scholar
  13. Earl DA, vonHoldt BM (2012) Structure Harvester: a website and program for visualizing Structure output and implementing the Evanno method. Conserv Genet Resour 4:359–361CrossRefGoogle Scholar
  14. Elliot JM (1989) Wild brown trout Salmo trutta: an important national and international resource. Freshw Biol 21:1–5CrossRefGoogle Scholar
  15. Elsner B (1884) Bericht des Fischzüchters Elsner an den Vorstand des Central-Fischerei-Vereins für Schleswig Holstein. 7. Jahresbericht des Central-Fischerei-Vereins für Schleswig-Holstein. Verlag W. Böhl, Rendsburg, S. 25–42 (in German)Google Scholar
  16. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620CrossRefPubMedGoogle Scholar
  17. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure: extensions to linked loci and correlated allele frequencies. Genetics 164:1567–1587PubMedPubMedCentralGoogle Scholar
  18. Foll M, Gaggiotti O (2008) A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180:977–993CrossRefPubMedPubMedCentralGoogle Scholar
  19. Forseth T, Barlaup BT, Finstad B, Fiske P, Gjøsæter H, Falkegård M, Hindar A, Mo TA, Rikardsen AH, Thorstad EB, Vøllestad LA, Wennevik V (2017) The major threats to Atlantic salmon in Norway. ICES J Mar Sci.  https://doi.org/10.1093/icesjms/fsx020 CrossRefGoogle Scholar
  20. Frazer DJ (2008) How well can captive breeding programs conserve biodiversity? A review of salmonids. Evol Appl 1(4):535–586Google Scholar
  21. Galil BS, Marchini A, Occipinti-Ambrogi A, Minchin D, Narščius A, Ojaveer H, Olenin S (2014) International arrivals: widespread bioinvasions in European Seas. Ethol Ecol Evol 26(2–3):152–171CrossRefPubMedPubMedCentralGoogle Scholar
  22. Glover KA, Quintela M, Wennevik V, Besnier F, Sørvik AGE, Skaala Ø (2012) Three decades of farmed escapees in the wild: a spatio-temporal analysis of Atlantic Salmon population genetic structure throughout Norway. PLoS ONE 7(8):e43129.  https://doi.org/10.1371/journal.pone.0043129 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Gollasch S, Rosenthal H (2006) The Kiel Canal: the world’s busiest man-made waterway and biological invasions. In: Gollasch S, Galil BS, Cohen A (eds) Bridging divides: Maritime Canals as invasion corridors. Springer, Dordrecht, pp 5–90CrossRefGoogle Scholar
  24. Grant WS, Jasper J, Bekkevold D, Adkinson M (2017) Responsible genetic approach to stock restoration, sea ranching and stock enhancement of marine fishes and invertebrates. Rev Fish Biol Fisher 27(3):615–649CrossRefGoogle Scholar
  25. Hall CJ, Jordaan A, Frisk MG (2012) Centuries of anadromous forage fish loss: consequences for ecosystem connectivity and productivity. Bioscience 62(8):723–731CrossRefGoogle Scholar
  26. Hansen MM (2002) Estimating the long-term effects of stocking domesticated trout into wild brown trout (Salmo trutta) populations: an approach using microsatellite DNA analysis of historical and contemporary samples. Mol Ecol 11:1003–1015CrossRefPubMedGoogle Scholar
  27. Hansen MM, Mensberg K-LD (1998) Genetic differentiation and relationship between genetic and geographical distance in Danish sea trout (Salmo trutta L.) populations. Heredity 81:493–504CrossRefGoogle Scholar
  28. Hansen MM, Nielsen EE, Ruzzante DE, Bouza C, Mensberg K-LD (2000) Genetic monitoring of supportive breeding in brown trout (Salmo trutta L.) using microsatellite DNA markers. Can J Fish Aquat Sci 57:2130–2139CrossRefGoogle Scholar
  29. Hansen MM, Fraser DJ, Meier K, Mensberg K-LD (2009) Sixty years of anthropogenetic pressure: a spatio-temporal genetic analysis of brown trout populations subject to stocking and population declines. Mol Ecol 18:2549–2562CrossRefPubMedGoogle Scholar
  30. Harris G (2017) Sea trout: science and management. Troubador Publishing Ltd. ISBN: 9781788035354Google Scholar
  31. Harris GS, Millner NJ (2006) Sea trout: biology, conservation and management. Blackwell Scientific Publications, OxfordGoogle Scholar
  32. Hartmann U (1996) Zur Meerforelle (Salmo trutta L.) in Schleswig-Holstein. In: Verband Deutscher Sportfischer (ed) Die Meerforelle—Fisch des Jahres 1996, pp 76–104 (in German)Google Scholar
  33. Hemmer-Hansen J, Nielsen EE, Grønkjaer P, Loeschke V (2007) Evolutionary mechanisms shaping the genetic population structure of marine fishes: lessons from the European flounder (Platichthys flesus L.). Mol Ecol 16:3104–3118CrossRefPubMedGoogle Scholar
  34. Hess MA, Rabe CD, Vogel JL, Stephenson JJ, Nelson DD, Narum SR (2012) Supportive breeding boosts natural population abundance with minimal negative impacts on fitness of a wild population of Chinook salmon. Mol Ecol 21(21):5236–5250CrossRefPubMedPubMedCentralGoogle Scholar
  35. ICES (2017) Report of the Baltic Salmon and Trout Assessment Working Group (WGBAST). 27. March-April 4th 2017, Gdansk Poland. ICES CM 2017/ACOM:10. p 298Google Scholar
  36. Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801–1806CrossRefPubMedGoogle Scholar
  37. Johannesson K, André C (2006) Life on the margin: genetic isolation and diversity loss in a peripheral marine ecosystem, the Baltic Sea. Mol Ecol 15:2013–2029CrossRefPubMedGoogle Scholar
  38. Jones O, Wang J (2010) COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol Ecol Resour 10:551–555CrossRefPubMedGoogle Scholar
  39. Kallio-Nyberg I, Veneranta L, Saloniemi I, Jutila E, Pakarinen T (2017) Spatial distribution of migratory Salmo trutta in the northern Baltic Sea. Boreal Environ Res 22:431–444Google Scholar
  40. Klemetsen A, Amundsen PA, Dempson JB, Jonsson B, Jonsson N, O´Connell MF, Mortensen E (2003) Atlantic Salmon (Salmo salar L.), brown trout (Salmo trutta L.) and Arctic charr (Salvelinus alpinus L.): a review of aspects of their life histories. Ecol Freshw Fish 12:1–59CrossRefGoogle Scholar
  41. Lassen H (1914) Die Salmonidenfischerei an unserer schleswig-holsteinischen Ostseeküste. In: 37. Jahresbericht des Central-Fischerei-Vereins für Schleswig-Holstein, 1913-1914, Verlag DJ, Carstens, Rendsburg, pp 173–192 (in German)Google Scholar
  42. Lehtonen PK, Tonteri A, Sendek D, Titov S, Primmer CR (2009) Spatio-temporal genetic structuring of brown trout (Salmo trutta L.) populations within the River Luga, northwest Russia. Conserv Genet 10(2):281–289CrossRefGoogle Scholar
  43. Leitwein M, Gagnaire P-A, Desmarais E, Guendouz S, Rohmer M, Berrebi P, Guinand B (2016) Genome-wide nucleotide diversity of hatchery-reared Atlantic and Mediterranean strains of brown trout Salmo trutta compared to wild Mediterranean populations. J Fish Biol 89(6):2717–2734CrossRefPubMedGoogle Scholar
  44. Limborg MT, Helyar SJ, de Bruyn M, Taylor MI, Nielsen EE, Ogden R, Carvalho GR, Consortium FPT, Bekkevold D (2012) Environmental selection on transcriptome-derived SNPs in a high gene flow marine fish, the Atlantic herring (Clupea harengus). Mol Ecol 21:3686–3703CrossRefPubMedGoogle Scholar
  45. Limburg KE, Waldman JR (2009) Dramatic declines in Northern Atlantic diadromous fishes. Bioscience 59(11):955–965CrossRefGoogle Scholar
  46. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Can Res 27:209–220Google Scholar
  47. Mota M, Rochard E, Antunes C (2017) Status of the diadromous fish of the Iberian Peninsula: past, present and trends. Limnetica 35(1):1–18Google Scholar
  48. Narum SR (2006) Beyond Bonferroni: less conservative analyses for conservation genetics. Conserv Genet 7:783–787CrossRefGoogle Scholar
  49. Nielsen EE, Hansen MM, Ruzzante DE, Meldrup D, Grønkjaer P (2003) Evidence of a hybrid-zone in Atlantic cod (Gadus morhua) in the Baltic and the Danish Belt Sea revealed by individual admixture analysis. Mol Ecol 12(6):1497–1508CrossRefPubMedGoogle Scholar
  50. Nilsson J, Gross R, Asplund T, Dove O, Jansson H, Kelloniemi J, Kohlmann K, Löytynoja A, Nielsen EE, Paaver T, Primmer CR, Titov S, Vasemägi A, Veselov A, Ost T, Lumme J (2001) Matrilinear phylogeography of Atlantic salmon (Salmo salar L.) in Europe and postglacial colonization of the Baltic Sea. Mol Ecol 10(1):89–102CrossRefPubMedGoogle Scholar
  51. Östergren J, Nilsson J, Lundquist H, Dannewitz J, Palm S (2016) Genetic baseline for conservation and management of sea trout in the northern Baltic Sea. Conserv Genet 17:177–191CrossRefGoogle Scholar
  52. Petereit C, Reusch THB, Dierking J, Hahn A (2013) Literaturrecherche, Aus- und Bewertung der Datenbasis zur Meerforelle (Salmo trutta trutta L.): Grundlage für ein Projekt zur Optimierung des Meerforellenmanagements in Schleswig-Holstein. GEOMAR Report N. Ser. 010. p 158.  https://doi.org/10.3289/GEOMAR_REP_NS_10_2013. http://oceanrep.geomar.de/21919/
  53. Piccolo JJ (2016) Conservation genomics: coming to a salmonid near you. J Fish Biol 89(6):2735–2740CrossRefPubMedGoogle Scholar
  54. Poćwierz-Kotus A, Bernaś R, Dębowski P, Kent MP, Lien S, Kesler M, Titov S, Leliūna E, Jespersen H, Drywa A, Wenne R (2014) Genetic differentiation of southeast Baltic populations of sea trout inferred from single nucleotide polymorphisms. Anim Genet 45(1):96–104CrossRefPubMedGoogle Scholar
  55. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  56. Rice WR (1989) Analyzing tables of statistical tests. Evolution 43:223–225CrossRefPubMedGoogle Scholar
  57. Rosenberg NA (2004) DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes 4:137–138CrossRefGoogle Scholar
  58. Rousset F (1997) Genetic differentiation and estimation of gene low from F-statistics under isolation by distance. Genetics 145:1219–1228PubMedPubMedCentralGoogle Scholar
  59. Rousset F (2008) GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour 8:103–106CrossRefPubMedGoogle Scholar
  60. Ryman N, Laikre L (1991) Effects of supportive breeding on the genetically effective population size. Conserv Biol 5:325–329CrossRefGoogle Scholar
  61. Ryman N, Ståhl G (1980) Genetic changes in hatchery stocks of brown trout (Salmo trutta). Can J Fish Aquat Sci 37:82–87CrossRefGoogle Scholar
  62. Samarasin P, Shuter BJ, Rodd FH (2017) After 100 years: hydroelectric dam-induced life-history divergence and population genetic changes in sockeye salmon (Oncorhynchus nerka). Conserv Genet 18(6):1449–1462CrossRefGoogle Scholar
  63. Samuiloviene A, Kontautas A, Gross R (2009) Genetic diversity and differentiation of sea trout (Salmo trutta) populations in Lithuanian rivers assessed by microsatellite DNA variation. Fish Physiol Biochem 35:649–659.  https://doi.org/10.1007/s10695-009-9310-1 CrossRefPubMedGoogle Scholar
  64. Schindler DE, Hilborn R, Chasco B, Boatright CP, Quinn TP, Rogers LA, Webster MS (2010) Population diversity and the portfolio effect in an exploited species. Nature 465:609–612CrossRefPubMedGoogle Scholar
  65. Schonevelde S (1624) Ichthyologia et nomenclaturae animalium marinorum, fluviatilium, lacustrium, quae in florentissimis ducatibus Slesvigi et Holsatiae & celeberrimo Emporio Hamburgo occurent triviales. Bibliopolis Heringianus, p 87 (in Latin)Google Scholar
  66. Shirvell CS, Dungey RG (1983) Microhabitats chosen by brown trout for feeding and spawning in rivers. Trans Am Fish Soc 112:355–367CrossRefGoogle Scholar
  67. Sjöqvist C, Godhe A, Jonsson PR, Sundqvist L, Kremp A (2015) Local adaptation and oceanographic connectivity patterns explain genetic differentiation of a marine diatom across the North Sea-Baltic Sea salinity gradient. Mol Ecol 24(11):2871–2885CrossRefPubMedPubMedCentralGoogle Scholar
  68. Smith SA, Bell G, Bermingham E (2014) Cross-Cordillera exchange mediated by the Panama Canal increased the species richness of local freshwater fish assemblages. Proc R Soc B 271:1889–1896CrossRefGoogle Scholar
  69. Sparrevohn CR, Storr-Paulsen M (2012) Eel, cod and seatrout harvest in Danish recreational fishing during 2011. DTU Aqua Report No. 253-2012 Charlottenlund. National Institute of Aquatic Resources, Technical University of Denmark, p 20Google Scholar
  70. Taylor MD, Chick RC, Lorenzen K, Agnalt A-L, Leber KM, Blankenship HL, Haegen GV, Loneragan NR (eds) (2017) Fisheries enhancement. Fish Res 186(Part 2):407–598Google Scholar
  71. Thaulow J, Borgstrøm R, Heun M (2013) Brown trout population structure highly affected by multiple stocking and river diversion in a high mountain national park. Conserv Genet 14(1):145–158CrossRefGoogle Scholar
  72. Thaulow J, Borgstrøm R, Heun M (2014) Genetic persistence of an initially introduced brown trout (Salmo trutta L.) population despite restocking of foreign conspecifics. Ecol Freshw Fish 23:485–497CrossRefGoogle Scholar
  73. Thorstadt EB, Økland F, Aarestrup K, Heggberget TG (2008) Factors affecting the within-river spawning migration of Atlantic salmon, with emphasis on human impacts. Rev Fish Biol Fisher 18(4):345–371CrossRefGoogle Scholar
  74. Wąs A, Bernaś R (2016) Long-term and seasonal genetic differentiation in wild and enhanced stocks of the sea trout (Salmo trutta f. trutta L.) from the Vistula River, in the southern Baltic—management implications. Fish Res 175:57–65CrossRefGoogle Scholar
  75. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370PubMedGoogle Scholar
  76. Wenne R, Bernaś R, Poćwierz-Kotus A, Drywa A, Wąs A (2016) Recent genetic changes in enhanced populations of sea trout (Salmo trutta m. trutta) in the Southern Baltic rivers revealed with SNP analysis. Aquat Living Resour 29:103CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.GEOMAR Helmholtz Centre for Ocean Research Kiel, Evolutionary Ecology of Marine FishesKielGermany
  2. 2.DTU-Aqua National Institute of Aquatic ResourcesTechnical University of DenmarkSilkeborgDenmark
  3. 3.NLWKN – Lower Saxony Water Management, Coastal Defence and Nature Conservation AgencyLüneburgGermany
  4. 4.Institute of FisheriesMecklenburg-Vorpommern State Research Center for Agriculture and FisheriesRostockGermany
  5. 5.Fish Hatchery Altmühlendorf, Inland and Pond Fisheries Association Schleswig-HolsteinRendsburgGermany
  6. 6.Faculty of Mathematics and Natural SciencesUniversity of KielKielGermany

Personalised recommendations