Integrating genetic and genomic data into fisheries assessment and management

Abstract

Advances in DNA sequencing and computational power have enabled the generation of extensive genetic and genomic datasets regarding marine fish populations and species. Despite the significant potential of these data to enhance fisheries assessment and management, their implementation remains relatively variable at the global level. Among a plethora of potential applications, genetic and genomic studies can yield high-resolution insights into the spatio-temporal dynamics of marine fish populations, facilitating definition of stock units and the monitoring of their movements (mixing) and distribution. Despite the advantages for defining biologically identifiable assemblages, most fish stocks are typically assessed and managed at spatial scales that are inconsistent with genetic data, creating mismatches between genetic, assessment and management units. In this thesis, we evaluate such mismatches in North-East (NE) Atlantic commercial fish species by comparing genetic data ─ which we consider representative of biological populations ─ with assessment and management units. Through a systematic review, synthesizing four decades of population genetic and genomic studies from 42 NE Atlantic commercial fish species, we assessed how well genetic data aligns with stock assessment and management units. Although genetic studies remain scarce for less commercially important species and mismatches persist for others, we found that informative genetic and genomic data are increasingly being used to revise stock boundaries and for mixed stock analysis for assessment and management purposes. To understand the impact of such mismatches, we evaluated how misalignment may affect the recovery of fish stocks, in response to reduced fishing pressure, showing that stocks congruent with genetic units respond differently than those with mismatches. Finally, we demonstrate a practical application of genomic data by exploring environmental DNA (eDNA) metabarcoding as a tool for estimating bycatch composition in demersal bottom trawl fisheries. Our findings suggests that eDNA metabarcoding offers a cost-effective method for detecting not only landed species but also bycatch species, non-commercial species and potentially endangered or vulnerable species. Hence its integration could provide important data for both stock assessment, management and conservation strategies. Overall, this thesis illustrates a shift from viewing genetic and genomic data as promising to actively integrating them into practical applications in fisheries assessment and management. Grounded in high-resolution data, such a shift reflects an increasing reliance on scientific evidence and cross-disciplinary cooperation within fisheries science and management.

Date of Award	1 Apr 2025
Original language	English
Awarding Institution	Bangor University
Sponsors	EU Horizon 2020
Supervisor	Gary Carvalho (Supervisor), Alexander Papadopulos (Supervisor), Einar E. Nielsen (Supervisor), Jann Th. Martinsohn (Supervisor) & Ernesto Jardim (Supervisor)