loading page

Gene flow and migration routes in Salmo trutta L.: toward developing protected area systems in the English Channel
  • +3
  • Mathieu Vanhove,
  • R. Andrew King,
  • Lisa Meslier,
  • Anne-Laure Besnard,
  • Jamie Stevens,
  • Sophie Launey
Mathieu Vanhove
INRAE, Institut Agro, IFREMER

Corresponding Author:[email protected]

Author Profile
R. Andrew King
Exeter University
Author Profile
Lisa Meslier
INRAE, Institut Agro, IFREMER
Author Profile
Anne-Laure Besnard
INRAE, Institut Agro, IFREMER
Author Profile
Jamie Stevens
University of Exeter
Author Profile
Sophie Launey
INRAE, Institut Agro, IFREMER,
Author Profile


Understanding gene flow can help biodiversity to mitigate habitat changes by contributing to inform and design protected areas. The brown trout, Salmo trutta, displays a multitude of life-history strategies and represents an ideal model for applications in conservation genetics. Using a panel of 185-single nucleotide polymorphism markers, the present study aimed to explore the population structure of the brown trout and in the English Channel. The genotypes of 2,729 individual trout from 88 rivers were obtained across England and France. Population structure revealed the presence of genetic clusters following an east/west gradient. The maximum threshold distance between genetic distance and geographic distance was 344 km. The measure appeared relative to the studied spatial environment and reflected Salmo trutta capacity to achieve long migration distances. A machine-learning framework derived from a gradient forest analysis was used to generate a resistance surface using changes in allelic frequencies and environmental predicators. The resulting surface identified areas limiting gene flow. On the British coast, a genetic break was observed along the Jurassic coast, whereas the Cotentin peninsula acted as a physical barrier among French coastal populations. Salmo trutta populations appeared to be differently affected by environmental factors reflecting demes preference to specific breeding ground. Using our resistance map, the distance of maximum correlation using cost distance were computed allowing the pruning of our genetic graph. The resulting least cost path connections were mapped to reveal the main dispersal routes. Finally, a prioritization analysis using connectivity surface was implemented to design potential protected areas.
26 Jan 2024Submitted to Molecular Ecology
29 Jan 2024Assigned to Editor
29 Jan 2024Submission Checks Completed
29 Jan 2024Review(s) Completed, Editorial Evaluation Pending
30 Jan 2024Reviewer(s) Assigned