Introduction: status and future of modelling physical-biological interactions during the early life of fishes

Modelling physical-biological interactions in the early life of fish is becoming an inte- gral part of theoretical and applied marine ecology. A workshop on 'Advancements in modelling physical-biological interactions in fish early-life history' (WKAMF) was held in April 2006 to review recent developments and identify future research directions. Here we provide an overview of the information presented at WKAMF (some of which is published in this Theme Section), discussions that took place at the workshop, and the authors' perspectives as workshop co-Chairs. The major themes identified at the workshop were the need for enhanced model validation and sensitivity methods and improved understanding of physical and biological processes. Using the appropriate level of model complexity required for each model application is important; developing quantitative consistency of model results with good quality observational data is critical. In addition, improving our prediction of physical processes, such as circulation patterns and turbulence, will advance our knowledge of fish early life, as will a better understanding of biological processes like mortality, behaviour, and energetics. The latter stage-dependent, often species-specific, processes pose partic- ular challenges, although technical advances in field and laboratory observations are likely to result in considerable progress in the near future. Finally, there is a clear requirement for interdisciplinary collaboration between modellers, field scientists and laboratory scientists. Studies receiving input from a wide range of disciplines will increase our understanding of fish early-life ecology and predic- tion of recruitment to fish populations.

[1]  G. Marteinsdóttir,et al.  Maternal influence on the size and viability of Iceland cod Gadus morhua eggs and larvae , 1998 .

[2]  Geir Huse,et al.  Linking behavioural ecology and oceanography: larval behaviour determines growth, mortality and dispersal , 2007 .

[3]  A. Hermann,et al.  Development of a spatially explicit, individual-based model of marine fish early life history , 1996 .

[4]  Brian J. Rothschild,et al.  Small-scale turbulence and plankton contact rates , 1988 .

[5]  E. Houde.  Subtleties and episodes in the early life of fishes , 2006 .

[6]  André W. Visser,et al.  Using random walk models to simulate the vertical distribution of particles in a turbulent water column , 1997 .

[7]  Francisco E. Werner,et al.  Trophodynamic and advective influences on Georges Bank larval cod and haddock , 1996 .

[8]  C. Möllmann,et al.  Biophysical modeling of larval Baltic cod (Gadus morhua) growth and survival , 2002 .

[9]  H. Browman,et al.  Effect of turbulence on the energetics of foraging in Atlantic cod Gadus morhua larvae , 2004 .

[10]  M. Peck,et al.  Physiologically based limits to food consumption, and individual-based modeling of foraging and growth of larval fishes , 2007 .

[11]  Elizabeth W. North,et al.  Using a random displacement model to simulate turbulent particle motion in a baroclinic frontal zone: A new implementation scheme and model performance tests , 2006 .

[12]  Heath,et al.  Bio-physical modelling of the early life stages of haddock, Melanogrammus aeglefinus, in the North Sea , 1998 .

[13]  Claire B Paris-Limouzy,et al.  Scaling of Connectivity in Marine Populations , 2006, Science.

[14]  Claire B. Paris,et al.  Surfing, spinning, or diving from reef to reef: effects on population connectivity , 2007 .

[15]  A. Fernö,et al.  Ontogenetic- and condition-related effects of starvation on responsiveness in herring larvae (Clupea harengus L.) during repeated attacks by a model predator , 2004 .

[16]  P. Pepin,et al.  Vulnerability of fish eggs and larvae to predation: review of the influence of the relative size of prey and predator , 1996 .

[17]  C. Hannah.  Future directions in modelling physical-biological interactions , 2007 .

[18]  Ø. Fiksen,et al.  Drift, growth, and survival of larval Northeast Arctic cod with simple rules of behaviour , 2007 .

[19]  Christian Mullon,et al.  Evolutionary individual-based model for the recruitment of anchovy (Engraulis capensis) in the southern Benguela , 2002 .

[20]  André W. Visser,et al.  Individual-based simulations of larval fish feeding in turbulent environments , 2007 .

[21]  Modelling distribution of flounder larvae in the eastern English Channel: sensitivity to physical forcing and biological behaviour , 2007 .

[22]  Albert J. Hermann,et al.  Importance of spawning location and timing to successful transport to nursery areas: a simulation study of Gulf of Alaska walleye pollock , 2001 .

[23]  Christophe Lett,et al.  Assessment of an environmental barrier to transport of ichthyoplankton from the southern to the northern Benguela ecosystems , 2007 .

[24]  J. Leis.  Behaviour as input for modelling dispersal of fish larvae: behaviour, biogeography, hydrodynamics, ontogeny, physiology and phylogeny meet hydrography , 2007 .

[25]  Thomas J. Miller,et al.  Contribution of individual-based coupled physical-biological models to understanding recruitment in marine fish populations , 2007 .

[26]  P. Pepin.  Early life history studies of preypredator interactions: quantifying the stochastic individual responses to environmental variability , 2004 .

[27]  M. Huret,et al.  Dispersal modeling of fish early life stages: sensitivity with application to Atlantic cod in the western Gulf of Maine , 2007 .

[28]  R. Luettich,et al.  The role of hydrodynamics in explaining variability in fish populations , 1997 .

[29]  P. Pepin.  Estimating the encounter rate of Atlantic capelin(Mallotus villosus) with fish eggs,based on stomach content analysis , 2006 .

[30]  C. Möllmann,et al.  Survival probability of Baltic larval cod in relation to spatial overlap patterns with their prey obtained from drift model studies , 2005 .

[31]  Thomas R. Osborn,et al.  Applicability of turbulence measurement technology to small-scale plankton studies , 2007 .

[32]  H. Browman,et al.  The escape response of food-deprived cod larvae (Gadus morhua L.) , 2007 .

[33]  Uffe Høgsbro Thygesen,et al.  Simulating vertical turbulent dispersal with finite volumes and binned random walks , 2007 .

[34]  M. Heath,et al.  From the biology of the individual to the dynamics of the population: bridging the gap in fish early life studies , 1997 .

[35]  Asbjørn Christensen,et al.  Hydrodynamic backtracking of fish larvae by individual-based modelling , 2007 .

[36]  H. Batchelder.  Forward-in-Time-/Backward-in-Time-Trajectory (FITT/BITT) Modeling of Particles and Organisms in the Coastal Ocean , 2006 .

[37]  Formulation and application of an efficient optimized biophysical model , 2007 .

[38]  S. Coombs,et al.  An individual‐based model of the early life history of mackerel (Scomber scombrus) in the eastern North Atlantic, simulating transport, growth and mortality , 2004 .