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Dryad

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

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Feb 05, 2024 version files 78.66 MB

Abstract

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.