Module 3. Larval and juvenile transport, growth and survival

Participants:
Øyvind Fiksen (IFM, UoB)(Module leader), Bjørn Ådlandsvik (IMR), Hans Pecseli (PHI, UoO), Jan Erik Stiansen (IMR), Svein Sundby (IMR), Frode Vikebø (IMR/GI,UoB)

Objective:
Develop an integrated model system based on first-principles physics and biology to simulate distribution, transport, growth and survival of fish larvae from the spawning areas in spring to 0-group distribution in autumn when year-class strengths are largely determined.

In this module we will apply a multitude of models to predict effects on growth and recruitment success imposed by changes in food abundance, sea temperature, irradiance, turbidity, turbulence and currents.

Spawning activity, i.e. the total egg production and the spatio-temporal distribution of eggs is the starting point of Module 3, and the output from Module 4. This includes eggs from herring, NEA cod and capelin.

The basis for the transport modelling in Module 3 is a regional ocean circulation model with high resolution. This shelf model will take lateral boundary conditions from the larger scale modelling in Module 1. The model is based on the Regional Ocean Model System (ROMS) developed by H. Arango and A. Shchepetkin, an updated version of the SCRUM model (Song and Haidvogel, 1994) with improved numerics and parallellization properties.

The physical transport of eggs and larvae will be done by a Lagrangian particle tracking model (Ådlandsvik and Sundby, 1994; Vikebø 2000). This model will be integrated with the biological models on growth and survival.

In addition to the transportation of eggs, the physical models generate drift trajectories for both yolk-sac and feeding larvae. The coupled physical-biological models provides 3D fields of sea temperature, algal biomass, vertical profiles of light and energy dissipation rates, and the drift trajectories which are required to calculate egg development times, feeding and growth rates of larval fish. Since the vertical positioning of eggs and larvae are critical for the exposure to environmental variables, we will use a mixture of field-data assimilation and evolutionary reasoning to implement vertical behaviour (diel, ontogenetic) in the models.

From activities in Module 2, we will 1) assimilate field data on observed prey (Calanus nauplii) distribution and 2) implement scenarios on variations in time of appearance, abundance and distribution of nauplii. This will enable us to evaluate the importance of climatic effects on prey relative to the physical factors that influence larval fish directly (e.g. turbulence, light, drift patterns). The issue of turbulence-induced plankton contact rates and length scales is a basic problem that still is unresolved. We will collaborate with Physical Institute, University of Oslo to explore the problem to obtain correct parameterisation of turbulence length scales in the individual-based models.

The first part of the project will be to establish the ‘bottom-up-physics-in’ model framework outlined above. Certainly, the realism of the model system can only be assessed by its ability to mimic the past. A major task will therefore be to run hind-cast scenarios of the model. We need to evaluate the ability of the model system to reproduce both weak- and strong year classes.

In parallel with the validation activity, large-scale climatic scenarios from Module 1 can be used to force our model system. Then, we will be able to make predictions of how patterns of recruitment success may change under the predicted scenarios: Will the frequency of strong year classes change? Should the average recruitment success improve or decrease? How will the relative success of e.g. cod and herring be affected? It is also worth noting that this scheme could provide the foundation for a general operative prognostic model on the recruitment success in several important commercial species.

We are well aware of the ability of phytoplankton, zooplankton and fish to adapt to climatic fluctuations, both through phenotypic plasticity and natural selection (Strand et al. 2002). Therefore, we will strive to initiate new projects dealing with this type of questions, using our proposed model system as a basis.