Abstract
Delineation of population structure (i.e. stocks) is crucial to successfully manage exploited species and to address conservation concerns for threatened species. Fish migration and associated movements are key mechanisms through which discrete populations mix and are thus important determinants of population structure. Detailed information on fish migration and movements is becoming more accessible through advances in telemetry and analysis methods however such information is not yet used systematically in stock structure assessment. Here, we described how detections of acoustically tagged fish across a continental-scale array of underwater acoustic receivers were used to assess stock structure and connectivity in seven teleost and seven shark species and compared to findings from genetic and conventional tagging. Network analysis revealed previously unknown population connections in some species, and in others bolstered support for existing stock discrimination by identifying nodes and routes important for connectivity. Species with less variability in their movements required smaller sample sizes (45–50 individuals) to reveal useful stock structure information. Our study shows the power of continental-scale acoustic telemetry networks to detect movements among fishery jurisdictions. We highlight methodological issues that need to be considered in the design of acoustic telemetry studies for investigating stock structure and the interpretation of the resulting data. The advent of broad-scale acoustic telemetry networks across the globe provides new avenues to understand how movement informs population structure and can lead to improved management.
Original language | English |
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Pages (from-to) | 987-1005 |
Number of pages | 19 |
Journal | Fish and Fisheries |
Volume | 22 |
Issue number | 5 |
Early online date | 5 May 2021 |
DOIs | |
Publication status | Published - Sept 2021 |
Keywords
- acoustic telemetry
- fisheries management
- individual removal analysis
- large-scale movements
- stock identification