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Hydrobiologia

, Volume 826, Issue 1, pp 209–231 | Cite as

Brown trout (Salmo trutta L.) high genetic diversity around the Tyrrhenian Sea as revealed by nuclear and mitochondrial markers

  • P. BerrebiEmail author
  • V. Caputo Barucchi
  • A. Splendiani
  • S. Muracciole
  • A. Sabatini
  • F. Palmas
  • C. Tougard
  • M. Arculeo
  • S. Marić
Primary Research Paper

Abstract

The brown trout (Salmo trutta L.) is widely distributed all around Europe but its natural diversity is threatened by massive stocking with Atlantic domestic strains. Describing the remaining natural genetic diversity and the proportion of domestic hatchery strains in rivers is a prerequisite for smart conservation. The high genetic diversity of brown trout populations around the Tyrrhenian Sea is well known. Use of twelve microsatellites has allowed description of the natural genetic structure of populations and detection of the consequences of stocking. Mitochondrial DNA control region sequences and the LDH-C1* gene enabled placement of each population into one of the six mitochondrial and two allozymic known evolutionary lineages. The Corsican populations showed low intra-population genetic diversity but an exceptionally high level of inter-population differentiation. More southern Tyrrhenian regions exhibited opposite pattern of diversity, partly due to the Atlantic domestic introgression. Globally, the natural structure outlines two north–south clines: high inter-population differentiation and predominance of the Adriatic lineage in the north, but lower inter-population differentiation and the presence of the natural Atlantic lineage in the south. In addition, the Tyrrhenian region is the contact zone between the widespread Adriatic lineage and a local natural Atlantic lineage probably coming from North Africa through the Strait of Gibraltar.

Keywords

Microsatellites mtDNA control region LDH-C1* Tyrrhenian brown trout Conservation 

Notes

Acknowledgements

The authors thank Bernard Lasserre, Sophie Dubois, Yuki Minegishi, and Zhaojun Shao for laboratory technical help, Pietro Armenia, Giuseppe Pisani, and Andrea Belluscio for field help, and David Schikorski (of the private company Labofarm-Genindexe—France) for genotyping most of the considered trouts. Thank you to Martin Laporte and Séverine Roques who participated to statistical solving. Some of the samples were constituted by the French anglers’ Federations of Alpes Maritimes and Corsica and the Genesalm project (CIPA). They are warmly thanked. Sicilian samples were collected with permission from the Regional Province of Syracuse and the professional assistance of Pietro Armena. In Calabria and Lazio (Rome), corresponding managers (Giuseppe Pisani in Calabria and Andrea Belluscio in Lazio) linked us with professional fishermen for sampling. We thank them for their irreplaceable assistance. The authors would also like to acknowledge OSU-OREME and Juliette Fabre for their help in database construction and mapping. Saša Marić was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No. 173045).

Supplementary material

10750_2018_3734_MOESM1_ESM.docx (35 kb)
Table S1: Accession numbers of all haplotypes positioned in Fig. 2 (* = new), completed with their geographic distribution according to twenty main publications. Supplementary material 1 (DOCX 34 kb)
10750_2018_3734_MOESM2_ESM.docx (33 kb)
Table S2: Hierarchical steps in estimating K (the number of genetic clusters) from STRUCTURE runs using the ΔK method. L(K) - posterior probability of K; stdev - standard deviation of L(K) from seven independent runs; ΔK - an ad hoc quantity, predictor of the real number of clusters (Evanno et al., 2005), best ΔK are highlighted. Supplementary material 2 (DOCX 33 kb)
10750_2018_3734_MOESM3_ESM.docx (38 kb)
Table S3: Detection of full-sibs in Corsican and Sardinian samples. Supplementary material 3 (DOCX 38 kb)
10750_2018_3734_MOESM4_ESM.docx (150 kb)
Fig. S4: New projection of the FCA analysis presented in Fig. 3a according to axes 1 and 3. In this new perspective, Sardinian samples and E Maghine Corsican one, together with the Corsican samples Lataga and Aqua d’Acelli, which seemed similar in Fig. 3a, are in fact clearly different (red arrows) while Corsican samples Pozzi and Val d’Ese confirm their similarity. Supplementary material 4 (DOCX 150 kb)
10750_2018_3734_MOESM5_ESM.jpg (132 kb)
Fig. S5: Estimation of the number of genetic clusters (K) for the first level, from STRUCTURE runs using the ΔK method. Supplementary material 5 (JPEG 132 kb)
10750_2018_3734_MOESM6_ESM.jpg (660 kb)
Fig. S6. Estimated population structure as inferred by STRUCTURE analysis of microsatellite marker DNA data. White lines separate sampling sites, the most probable K = 19 is based on maximizing the mean estimated ln probability of data (Pritchard et al., 2000). Names and codes of sampling sites/clusters are reported in Tables 1 and 4 and Fig. 4. Supplementary material 6 (JPEG 660 kb)

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.ISEM, Université de Montpellier, CNRS, IRD, EPHEMontpellierFrance
  2. 2.Dipartimento di Scienze della Vita e dell’AmbienteUniversita Politecnica delle MarcheAnconaItaly
  3. 3.Office National des ForêtsCorteFrance
  4. 4.Dipartimento di Scienze della Vita e dell’AmbienteUniversità di CagliariCagliariItaly
  5. 5.Dipartimento di STEBICEFUniversità di PalermoPalermoItaly
  6. 6.Faculty of Biology, Institute of ZoologyUniversity of BelgradeBelgradeSerbia
  7. 7.Genome – Research & DiagnosticMontpellierFrance

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