Applied and theoretical considerations for constructing spatially explicit individual-based models of marine larval fish that include multiple trophic levels

• Published in: ICES Journal of Marine Science
• Main Authors: Hermann, Albert J., Hinckley, Sarah, Megrey, Bernard A., Napp, Jeffrey M.
• Format: Text
• Language: English
• Published: Oxford University Press 2001
• Subjects: Regular Articles; Gulf of Alaska; Theragra chalcogramma; Alaska
• Online Access: http://icesjms.oxfordjournals.org/cgi/content/short/58/5/1030; https://doi.org/10.1006/jmsc.2001.1087

Individual-based modelling (IBM) techniques offer many advantages for spatially explicit modelling of marine fish early life history. However, computationally efficient methods are needed for incorporating spatially explicit circulation and prey dynamics into IBMs. Models of nutrient–phytoplankton–zooplankton (NPZ) dynamics have traditionally been formulated in an Eulerian (fixed spatial grid) framework, as opposed to the pseudo-Lagrangian (individual-following) framework of some IBMs. We describe our recent linkage of three models for the western Gulf of Alaska: (1) a three-dimensional, eddy-resolving, wind- and runoff-driven circulation model, (2) a probabilistic IBM of growth and mortality for egg and larval stages of walleye pollock ( Theragra chalcogramma ), and (3) an Eulerian, stage-structured NPZ model which specifies production of larval pollock prey items. Individual fish in the IBM are tracked through space using daily velocity fields generated from the hydrodynamic model, along with self-directed vertical migrations of pollock appropriate to each life stage. The NPZ dynamics are driven by the same velocity, temperature, and salinity fields as the pollock IBM, and provide spatially and temporally varying prey fields to that model. The resulting prey fields yield greater variance of individual fish attributes (e.g. length), relative to models with spatially uniform prey. Practical issues addressed include the proper time filtering and storage of circulation model output for subsequent use by biological models, and use of different spatial grids for physical and biological dynamics. We demonstrate the feasibility and computational costs of our coupled approach using specific examples from the western Gulf of Alaska.