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

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.

Influence of larval transport and temperature on recruitment dynamics of North Sea cod (Gadus morhua) across spatial scales of observation

“…The individual‐based model (IBM) simulates development and transport of cod eggs and larvae based on earlier studies of larval cod (Kristiansen, Lough, Werner, Broughton, & Buckley, 2009;Kristiansen, Stock, Drinkwater, & Curchitser, 2014;Kristiansen, Vikebø, Sundby, Huse, & Fiksen, 2009). The IBM is integrated as a module in the open source Lagrangian particle tracking framework OpenDrift (github.com/opendrift; Dagestad, Röhrs, Breivik, & Ådlandsvik, 2018;Kvile et al., 2018), and the code for the cod eggs and larvae module is available on http://github.com/trondkr/KINO-ROMS/tree/master/Romagnoni-2019-OpenDrift. To simulate transport with ocean currents and temperature‐dependent development, the IBM was coupled offline to a reanalysis of the regional ocean circulation model ROMS (Shchepetkin & McWilliams, 2005) configured for ocean regions covering the Nordic Seas (including the North Sea) and parts of the Arctic Ocean, with 4 km horizontal resolution, 32 vertical layers and output stored daily (Lien, Gusdal, Albretsen, & Melsom, 2013).…”

“…Further details on the characteristics and limitations of the ELS dispersal model are available in Kvile et al. (2018).…”

“…After release, eggs and larvae were advected horizontally at fixed depths using an Euler interpolation scheme without horizontal diffusion and a 1‐hr time step. The Euler scheme differed minimally compared to a more computationally costly Runge–Kutta scheme (Kvile et al., 2018). We used different drift depths to represent vertical movement within the depth range typically available in the North Sea, based on the finding that incorporating a more computationally costly vertical movement behaviour had limited effect on connectivity and retention of cod ELS at settlement in the North Sea (Kvile et al., 2018).…”

“…The Euler scheme differed minimally compared to a more computationally costly Runge–Kutta scheme (Kvile et al., 2018). We used different drift depths to represent vertical movement within the depth range typically available in the North Sea, based on the finding that incorporating a more computationally costly vertical movement behaviour had limited effect on connectivity and retention of cod ELS at settlement in the North Sea (Kvile et al., 2018). Development time of planktonic eggs ( d , days) was a function of the ambient sea water temperature (T, °C) according to the ocean model reanalyses, parameterised based on observations for cod eggs (Langangen, Stige, Yaragina, Vikebø, et al, 2014, based on data in Ellertsen, Fossum, Solemdal, Sundby, & Tilseth, 1987, Figure 2b):…”

“…Moreover, transport can influence connectivity among populations (e.g. through interannual variability in oceanographic current patterns, Huwer et al., 2016;Kvile, Romagnoni, Dagestad, Langangen, & Kristiansen, 2018) and recruitment dynamics across large geographic scales (Cadrin, Goethel, Morse, Fay, & Kerr, 2019;Henriksen et al., 2018;Hinrichsen, Von Dewitz, & Dierking, 2018) and thereby population management (Fogarty & Botsford, 2007;Hidalgo et al., 2019;Ramesh, Rising, & Oremus, 2019). Critically, the spatial scale of observation can affect the stock–recruitment relationship, providing contrasting results across scales (Chang, Chen, Halteman, & Wilson, 2016).…”