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

Northeast Arctic cod

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

Recruitment variability Arcto-Norwegian cod 1946-2005

Max 1973

Min 1969

Recruitment variability of Northeast Arctic cod

Coupled IBM+ROMSThree types of models:

A mechanistic individual-based model for simulatingbioenergetics, behaviour, and feeding of larval cod

A general circulation model to simulate the dynamicsof the ocean (the ROMS model)

A 3D zooplankton model to simulate the dynamicalprey field

The individual-based model

The mechanistic feeding component uses biological and physical properties of predator, prey, and environment for calculations

Objectives

• Study how environmental conditions such as:– Light– Temperature– Turbulence– Food abundance

affect growth rate of larval fish

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

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

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.

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

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

Growth of 5mm on April 1

Number of daylight hours restricts growth (night is too long)

Temperature-restricted growth

Growth of 5mm larva on May 1

Hours of sunlight (17) enhances larval growth to reach maximum rate even at low prey abundance

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

Coupled IBM+ROMS+zooplankton model

Growth of 5mm larvae

Prey distribution from zooplankton model

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.

Georges Bank

Barents Sea

Two important cod stocks in different habitats

Georges Bank cod stock

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

Future workObjectives: 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? - +++