Genome-wide SNP datasets for the non-native pink salmon in Norway

Effective management of non-indigenous species requires knowledge of their
dispersal factors and founder events. We aim to identify the main
environmental drivers favouring dispersal events along the invasion
gradient and to characterize the spatial patterns of genetic diversity in
feral populations of the non-native pink salmon within its epicentre of
invasion in Norway. We first conducted SDM using four modelling techniques
with varying levels of complexity, which encompassed both regression-based
and tree-based machine-learning algorithms, using climatic data from the
present to 2050. Then we used the triple-enzyme restriction-site
associated DNA sequencing (3RADseq) approach to genotype over 30,000
high-quality single-nucleotide polymorphisms to elucidate patterns of
genetic diversity and gene flow within the pink salmon putative invasion
hotspot. We discovered temperature- and precipitation-related variables
drove pink salmon distributional shifts across its non-native ranges, and
that climate-induced favourable areas will remain stable for the next 30
years. In addition, all SDMs identified north-eastern Norway as the
epicentre of the pink salmon invasion, and genomic data revealed that
there was minimal variation in genetic diversity across the sampled
populations at a genome-wide level in this region. While, upon utilizing a
specific group of ‘diagnostic’ SNPs, we observed a significant degree of
genetic differentiation, ranging from moderate to substantial, and
detected four hierarchical genetic clusters concordant with geography. Our
findings suggest that fluctuations of climate extreme events associated
with ongoing climate change will likely maintain environmental
favourability for the pink salmon outside its ‘native’/introduced ranges.
Local invaded rivers are themselves a potential source population of
invaders in the ongoing secondary spread of pink salmon in Northern
Norway. Our study shows that SDMs and genomic data can reveal species
distribution determinants and provide indicators to aid in post-control
measures and potential inferences of their success.