Back

The historical patterns that have shaped contemporary genetic differentiation across populations of Arctic charr in Scotland

Fenton, S.; Bean, C. W.; Elmer, K. R.; Adams, C. E.

2025-10-07 genetics
10.1101/2025.10.07.680933 bioRxiv
Show abstract

Glacial history is an important contributor to contemporary biogeographic patterns because it caused population fragmentation and consequently diversification. The Arctic charr (Salvelinus alpinus) is a highly diverse non-anadromous salmonid fish species in Britain and Ireland, which likely was anadromous when it colonised around the end of the last ice age. Colonisation history of the species remains largely unexplored and so the potential impact on contemporary patterns of genetic differentiation remains unclear. To address this, we conducted a national-scale genetic study of Arctic charr using a genome-wide dataset of SNPs (24,878 SNPs and 410 individuals) and mitochondrial ND1 sequences (238 sequences). We found several mitochondrial haplotypes were shared across Britain, Ireland, and the wider Holarctic suggesting colonisation by multiple sub-lineages of the Atlantic lineage of the species. Genetic differentiation was not correlated with geographic distance among river catchments, highlighting the effect of spatial isolation and genetic drift. Several populations across different river catchments showed atypical ancestries and evidence for genetic mixing, which we speculate are due to asynchronous ice coverage and the presence of ice-dammed lakes. Our results highlight how glacial history can impact colonisation history and subsequently contemporary patterns of genetic differentiation in this widespread species.

Published in Biological Journal of the Linnean Society (predicted rank #7) · training set

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.