Sensitivity of modelled North Sea cod larvae transport to vertical behaviour, ocean model resolution and interannual variation in ocean dynamics

• Published in: ICES Journal of Marine Science
• Main Authors: Kvile, Kristina Øie, Romagnoni, Giovanni, Dagestad, Knut-Frode, Langangen, Øystein, Kristiansen, Trond
• Other Authors: Watson, James, Research Council of Norway, RCN, SUSTAIN, Norden Top-level Research Initiative, OILCOM, National Infrastructure for High Performance Computing and Data Storage in Norway
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2018
• Subjects: Gadus morhua
• Online Access: http://dx.doi.org/10.1093/icesjms/fsy039; http://academic.oup.com/icesjms/article-pdf/75/7/2413/31236805/fsy039.pdf

Abstract Transport with ocean currents affects the spatial distribution and survival of fish eggs and larvae and thereby population connectivity. Biophysical models are commonly used to understand these dynamics. Advancements such as implementing vertical swimming behaviour and higher resolution ocean circulation models are known to improve model performance, however, the relative importance of vertical behaviour vs. ocean model resolution is elusive. Here, we use North Sea cod (Gadus morhua) as a case study to assess how vertical movement, ocean model resolution and interannual variation in ocean dynamics influence drift patterns and population connectivity. We couple a fine (1.6 km, 3 h) and coarser (4 km, 24 h) ocean model to an individual-based model for cod eggs and larvae, and compare simulations with and without vertical movement of eggs and larvae. The results are moderately influenced by vertical movement and ocean model resolution but differ substantially between years. While ocean model resolution is consistently more influential than vertical movement, the effect of vertical movement strongly depends on the spatiotemporal scale of the analyses. This study highlights which aspects of biophysical modelling of connectivity that most critically affect the results, allowing better investing computational resources and proposing goal-based guidelines for future studies.