Exploring population connectivity and adaptation in two deep sea fishes, Molva molva and Molva dypterygia

McGill, L

Abstract

The deep sea is typically seen as a stable and constant environment. However, in recent years we are seeing increasing fragility caused by unprecedented human exploitation. Many deep-sea fish species are typically long lived and have specific traits which often limit their resilience to impact and change. Due to the expansion of fisheries, it is important to better understand population structure and connectivity of these deep-sea populations, in order to ensure their sustainable management.
In this study, samples of two deep sea species collected across their distribution range were screened: the common ling, Molva molva, and the deeper dwelling blue ling, Molva dypterygia. Using Genotype-By-Sequencing (GBS) and the M. molva reference genome, 6,566 and 3,073 neutral Single Nucleotide Polymorphism (SNPs) were identified, respectively. Results indicate how the two species exhibit a different structure pattern, with the deeper blue ling showing fine scale differentiation within population samples along the Norwegian coast, and the common ling being more homogeneous. By identifying 3 outlier loci in the common ling, and 5 outlier loci in the blue ling, adaptive divergence is explored. No candidate genes could be identified from the common ling data. With data for the blue ling it was possible to link outlier loci with multiple genes and speculate adaptive divergence from this. Genes linked with responses to environmental variables including light and temperature were among those found.
Overall, the findings presented in this study attempt at filling a knowledge gap about exploited deep sea fish species, and will hopefully aid the sustainable management of these species.

Citation

McGill, L. Exploring population connectivity and adaptation in two deep sea fishes, Molva molva and Molva dypterygia. (Dissertation). University of Salford

• Thesis Type: Dissertation
• Deposit Date: Aug 7, 2020
• Publicly Available Date: Aug 7, 2020
• Award Date: Jan 1, 2020