Common trends in recruitment dynamics of north-east Atlantic fish stocks and their links to environment, ecology and management
Peer reviewed, Journal article
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Original versionZimmermann F, Claireaux M, Enberg K. Common trends in recruitment dynamics of north-east Atlantic fish stocks and their links to environment, ecology and management. Fish and Fisheries. 2019;20(3), 518-536. https://doi.org/10.1111/faf.12360
Recruitment dynamics are challenging to assess or predict because of the many underlying drivers that vary in their relevance over time and space. Stock size, demographic and trait composition, condition and distribution of spawning fish and the spatio‐temporal dynamics of trophic and environmental interactions all influence recruitment processes. Exploring common patterns among stocks and linking them to potential drivers may therefore provide insights into key mechanisms of recruitment dynamics. Here, we analysed stock‐recruitment data of 64 stocks from the north‐east Atlantic Ocean for common trends in variation and synchrony among stocks using correlation, cluster and dynamic factor analyses. We tested common trends in recruitment success for relationships with large‐scale environmental processes as well as stock state indicators, and we explored links between recruitment success and demographic, environmental and ecological variables for a subset of individual stocks. The results revealed few statistically significant correlations between stocks but showed that underlying common trends in recruitment success are linked to environmental indices and management indicators. Statistical analyses confirmed previously suggested relationships of environmental–ecological factors such as the subpolar gyre and Norwegian coastal current with specific stocks, and indicated a large relevance of spawning stock biomass and demographics, as well as predation, whereas other suggested relationships were not supported by the data. Our study shows that despite persistent challenges in determining drivers of recruitment due to poor data quality and unclear mechanisms, combining different data analysis techniques can improve our understanding of recruitment dynamics in fish stocks.