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Conservation Genetics

, Volume 20, Issue 5, pp 1133–1148 | Cite as

Current genetic admixture between relictual populations might enhance the recovery of an elusive carnivore

  • Lise-Marie PigneurEmail author
  • Gaëlle Caublot
  • Christine Fournier-Chambrillon
  • Pascal Fournier
  • Gloria Giralda-Carrera
  • Xavier Grémillet
  • Bruno Le Roux
  • Daniel Marc
  • Franck Simonnet
  • Nathalie Smitz
  • Eric Sourp
  • Julien Steinmetz
  • Fermin Urra-Maya
  • Johan R. Michaux
Research Article

Abstract

The present study investigated the natural recovery of the Eurasian otter (Lutra lutra) in France. The otter was widely distributed in France at the dawn of the 20th century, but then its range considerably shrank and became highly fragmented until the early 1970s, just before it was legally protected. However, for more than 25 years, the otter has been reconquering several parts of its original range and is now considered to be in expansion in France. We investigated the genetic differentiation and diversity of several populations from western and central France and northern Spain to gain insight into the recolonisation dynamics of this elusive species. The present study, based on the use of 14 microsatellite markers, revealed that otter populations seem to be split into five distinct groups. The distribution of samples in those five clusters was closely correlated with suspected refugia where the otter probably survived during the 20th century. Admixture was observed between genetic lineages, possibly enhancing their genetic diversity and thus increasing the recolonisation dynamics of these populations. This phenomenon resembles the genetic pattern noted in many invasive exotic species derived from multiple sources and introduction events. Finally, a demographic approach revealed the probable link between historical human pressure and otter population fragmentation patterns.

Keywords

Eurasian otter France Genetic diversity Lutra lutra Population genetic structure Recolonisation 

Notes

Acknowledgements

The authors thank all sample contributors from the different regions, and especially Sébastien Gautier (ONCFS), Ludovic Fleury and the following partners: Cistude-Nature, Charente-Nature, CEN Midi-Pyrénées, GREGE, ONCFS (a.o. Délégation Interrégionale Nord Ouest and Cellule Technique Sud Ouest), GMB, GMN, GMHL, LPO, Museum of Toulouse (MHNT), Fédération Aude Claire, Government of Navarra via the GANASA (Gestión Ambiental de Navarra), French ministry MTES and the DREAL’s, SFEPM and PNA Loutre. The authors thank the editor and three anonymous reviewers for their fruitful comments on the manuscript. We are also grateful to Prof. K. Van Doninck (University of Namur) for help in fundraising and logistic support.

Author contributions

CFC, DM, PF, NS, JRM and LMP designed research, performed analyses and wrote the paper. CFC, PF, DM, GC, XG, BL, FS, ES, JS and FUM contributed samples and helped for interpreting data and improving the manuscript.

Funding

LMP and JRM benefited from FRS-FNRS grants (“chargée de recherches” and “directeur de recherches”, respectively).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10592_2019_1199_MOESM1_ESM.png (160 kb)
Supplementary material 1 Principal component analysis performed on the first 10,000 simulated datasets using DIY ABC. The large yellow dot represents the observed dataset. (PNG 159 kb)
10592_2019_1199_MOESM2_ESM.pdf (29 kb)
Supplementary material 2 Results of the Bayesian clustering analysis with STRUCTURE software. Left: mean lnP(D) for each tested K value, right: ΔK values for each K (PDF 28 kb)
10592_2019_1199_MOESM3_ESM.jpg (61 kb)
Supplementary material 3 Bayesian information criterion distribution of k-means algorithm results (JPG 61 kb)
10592_2019_1199_MOESM4_ESM.xlsx (31 kb)
Supplementary material 4 Model specifications and prior distributions for demographic parameters (here for Scenario 5) (XLSX 30 kb)
10592_2019_1199_MOESM5_ESM.xlsx (35 kb)
Supplementary material 5 Performance analysis of the model choice procedure. D = proportion of cases in which the simulation-based model choice procedure selected a scenario as the most probable with non-overlapping confidence intervals of the posterior probabilities of each scenario. * Type-I or α-error rate. ° Type-II or β-error rate (1- Σ βi is used to determine the power of the model choice procedure) (XLSX 35 kb)

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Lise-Marie Pigneur
    • 1
    Email author
  • Gaëlle Caublot
    • 2
  • Christine Fournier-Chambrillon
    • 3
  • Pascal Fournier
    • 3
  • Gloria Giralda-Carrera
    • 4
  • Xavier Grémillet
    • 5
  • Bruno Le Roux
    • 6
  • Daniel Marc
    • 7
  • Franck Simonnet
    • 5
  • Nathalie Smitz
    • 1
    • 8
  • Eric Sourp
    • 9
  • Julien Steinmetz
    • 10
  • Fermin Urra-Maya
    • 11
  • Johan R. Michaux
    • 1
    • 12
  1. 1.Conservation Genetics LaboratoryUniversity of LiègeLiègeBelgium
  2. 2.Groupe Mammalogique et Herpétologique du LimousinAixe-sur-VienneFrance
  3. 3.Groupe de Recherche et d’Etude pour la Gestion de l’EnvironnementUzesteFrance
  4. 4.Servicio de Conservación de la BiodiversidadGobierno de NavarraNavarraSpain
  5. 5.Groupe Mammalogique BretonSizunFrance
  6. 6.Fédération Aude ClaireLimouxFrance
  7. 7.Conservatoire d’Espaces Naturels de Midi-PyrénéesToulouseFrance
  8. 8.Barcoding Facility for Organisms and Tissues of Policy ConcernRoyal Museum for Central AfricaTervurenBelgium
  9. 9.Parc National des PyrénéesPyreneesFrance
  10. 10.Office National de la Chasse et de la Faune SauvageParisFrance
  11. 11.Equipo de BiodiversidadGestión Ambiental de NavarraNavarraSpain
  12. 12.CIRAD, UR ASTREMontpellierFrance

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