Bio‐physical modelling of the early life stages of haddock, Melanogrammus aeglefinus, in the North Sea

Abstract

An individual‐based modelling approach was developed to investigate the spatial and temporal patterns in the recruitment processes of North Sea haddock, Melanogrammus aeglefinus. The approach was based on the realization that the survivors to recruitment of an annual cohort are most probably not drawn at random from the initial population of eggs, but represent the fastest‐growing individuals. Individual growth rates reflect the unique exposure of each larva to the environment along its drift trajectory. In this context, the environment refers to a wide range of factors affecting growth such as food, turbulence and temperature. A combination of a model of egg production by the adult stock, a particle‐tracking scheme, and a model of larval growth and mortality rate was used to simulate the dispersal trajectories, and the survival of haddock larvae spawned at different times and locations on the continental shelf. The particle tracking was driven by flowfields from a climatological implementation of the Hamburg Shelf–Ocean Model (HAMSOM) for the North Sea and NE Atlantic. The system was able to resolve spatial and temporal patterns in the recruitment process and indicated that the surviving population of larvae was drawn from a restricted part of the spawning distribution. The results have the potential to guide the development of future conservation measures in fisheries management.

Understanding the Impact of Environmental Variability on Anchovy Overwintering Migration in the Black Sea and its Implications for the Fishing Industry

“…Considering the long-distance migration that anchovy in the Black Sea undergo every year, studying movement of populations can provide valuable insights for fisheries science (Goodwin et al, 2006). Therefore, simulation of movement of individual particles in a dynamic environment is an important tool for investigating ecological processes in the marine environment (Heath and Gallego, 1998;Miller et al, 1998;Hare et al, 1999).…”

Developing Baltic cod recruitment models. II. Incorporation of environmental variability and species interaction

“…Furthermore, the effects of predation on fish eggs and larvae (e.g., Bailey and Houde 1989) have the potential to reduce the reproductive success of a stock. Similarly, the loss of eggs and larvae due to transport to suboptimal environments (e.g., Heath and Gallego 1998) can influence recruitment negatively.…”

Scientific knowledge of biological processes potentially useful in fish stock predictions

“…Rose et al, 1999). Simulations of survival success of haddock egg production in different areas off Scotland (Heath and Gallego, 1998) suggest considerable deviations in survival to the larval stage depending on where the egg production originates. Results suggest that 13.6% of the egg production west of Scotland generates 24% of the surviving larvae at 7 mm length.…”

“…Several of these bio-physical models have been established, e.g. for walleye pollock (Hermann et al, 1996;Hinckley et al, 2000), haddock off Scotland (Heath and Gallego, 1998;Heath and Gallego, 2000), bay anchovy from Chesapeake Bay (Rose et al, 1999), haddock on Browns Bank (Brickman and Frank, 2000;Brickman et al, 2001) and cod in the Baltic (Hinrichsen et al, 2002a). In general the hydrodynamic models using either Lagrangian particle tracking (Werner et al, 1996) or advectiondiffusion equation models (Lynch et al, 1998) are forced by realistic atmospheric conditions and resolve the small-scale vertical and meso-scale horizontal hydro-dynamics quite well.…”

“…This leads to the following assumptions: i) prey concentration does not limit larval growth, thereby making inclusion of fooddependent feeding and growth rates redundant (Heath and Gallego, 1998), ii) mean prey densities are uniform in space and time (e.g. Herman et al, 1996), iii) prey density is variable over season, but invariate over years (Tyler et al, 1997), partly considering feed back by predation on prey density (Rose et al, 1999), and iv) prey density is horizontally and seasonally variable, applying overall yearly weighting factors to account for inter-annual variability in integrated prey abundance (Hinrichsen et al, 2002a).…”