1. Growth and feeding of larval cod (Gadus morhua) in the Barents Sea and the Georges Bank Trond Kristiansen, Frode Vikebø, Svein Sundby, Geir Huse, Øyvind Fiksen, Greg Lough, Larry Buckley, and Cisco Werner

2. Northeast Arctic cod

3. Probability of survival through the egg and larval stages are low (more than 99.9% dies) The number of individuals that survives the critical first 5 months are positively correlated with numbers that reach age 3 years If we understand the early-life history of fish we may understand the causes of variability in recruitment to the fisheries Early life history and recruitment

4. Recruitment variability Arcto-Norwegian cod 1946-2005 Max 1973 Min 1969 Recruitment variability of Northeast Arctic cod

5. Coupled IBM+ROMS Three types of models: A mechanistic individual-based model for simulating bioenergetics, behaviour, and feeding of larval cod A general circulation model to simulate the dynamics of the ocean (the ROMS model) A 3D zooplankton model to simulate the dynamical prey field

6. The individual-based model The mechanistic feeding component uses biological and physical properties of predator, prey, and environment for calculations

7. Objectives Study how environmental conditions such as: –Light –Temperature –Turbulence –Food abundance affect growth rate of larval fish

8. Definitions –Specific growth rate (SGR): the amount of weight increase over 24 hours relative to total weight –Maximum growth: The physiologically possible growth restricted by temperature alone

9. Varying light and prey availability at two locations for two different levels of temperature, and zero turbulence.

10. Simulated spawning grounds Vikebø, F., Jørgensen, C., Kristiansen, T. and Fiksen, Ø. (In press) ’ Drift, growth and survival of larval Northeast Arctic cod with simple rules of behaviour’, MEPS.

12. Varying light and prey availability at the two locations, and increasing temperature by 2 degrees C.

14. How do light and temperature for two levels of food abundance and turbulence regulate growth of 5mm on April 1 and May 1?

15. Growth of 5mm on April 1 Number of daylight hours restricts growth (night is too long) Temperature-restricted growth

16. Growth of 5mm larva on May 1 Hours of sunlight (17) enhances larval growth to reach maximum rate even at low prey abundance

17. Varying light and temperature, with estimated prey distribution from the zooplankton model for larva kept fixed in space.

18. Coupled IBM+ROMS+zooplankton model Growth of 5mm larvae Prey distribution from zooplankton model

19. Preliminary conclusions Light is limiting feeding and growth prior to mid- April. By early May, the number of light hours increases (17/24) and growth is mainly determined by water temperature. High prey densities is not a requirement for growth, but may reduce the activity level of the larvae and reduce their visibility to predators.

20. Georges Bank Barents Sea Two important cod stocks in different habitats

21. Georges Bank cod stock

22. Spawning migration: –Georges Bank: Short spawning migration –Barents Sea: Very long spawning migration Central recruitment hypothesis: –Barents Sea: Match-mismatch –Georges Bank: Larval loss Temperature-recruitment relations: –Georges Bank: No clear temperature-recruitment relation –Barents Sea: Srong temperature-recruitment relationships Dominant prey for larvae and early juveniles - Georges Bank: Pseudo/Paracalanus spp. –Barents Sea: Calanus finmarchicus Light, climate, spawning and larval growth: -Georges Bank: Extended spawning period in winter/spring -Barents Sea: Compressed spawning around equinox and rapid larval and juvenile growth thereafter Major differences between early life history of GB and BS cod

23. Future work Objectives: Use the same model setup for the Barents Sea and the Georges Bank ecosystems and model drift, dispersal, growth, feeding, survival, and behavior. Identify the major processes that affect survival variability between ecosystems. Simulate a set of years that contributed strongly to recruitment in each of the ecosystems, and try to understand the major underlying causes. Meet objectives using: - Physical model (ROMS) - Individual based model (IBM) - What about prey fields? Modeled prey fields? Theoretical prey fields? Observed prey fields? - How many prey stages should be included? - What type of atmospheric data to use? - +++