Individual-based modeling of walleye pollock in the southeast Bering Sea

S. Hinckley, B.A. Megrey, A.J. Hermann

Much of the structure of the Individual-Based Model (IBM, Fig 1) will remain the same as configured for the GOA, in terms of the basic life stage structure. Progress has been made, however, in extending the model, in adapting parts of the model to the Bering Sea, and in collecting parameters specific to the region. The juvenile stage module has been revised and extended based on recent information on this stage from the Bering Sea. A major extension to the IBM has been the addition of a mortality component, similar to the "super-individuals" scheme of Scheffer et al (1995). This component was needed to deal with the problem of modelling large numbers of individuals without incurring impractically large computation times. This component adds a variable that tracks the number of real individuals represented in the model, which is reduced by daily mortality. This method implies that each of the "floats" which are tracked through space in the 3D model represent a "cohort" of individuals, rather than single individuals. Each member of the cohort follows the same trajectory through space, experiences the same environmental conditions of temperature, salinity, and food, and has the same growth pattern over time. The "value" of the float, ie. the number of real individuals represented in the cohort, is reduced daily by stage-specific mortality rates, which may be either constant (as presently in the egg stage), or size-based (as in the larval stage). This represents a "total" mortality. Starvation mortality, which is calculated by the model based on a critical condition factor, is subtracted from this total mortality to obtain an estimate of predation mortality.

Parameters specific to the Bering Sea which are being incorporated into the IBM include: (1) spawning times, locations and depths (P. Dell'Arciprete), (2) low-temperature egg development rates (D. Blood), (3) low-temperature larval metabolic rates and Q1 0 (S. Porter), (4) depth distributions of different life stages (A. Kendall, A. Brase, P. Dell'Arciprete, R. Brodeur and M. Wilson (5) bioenergetics and consumption parameters for the juvenile stage (L. Cianelli and R. Brodeur, (6) an algorithm to predict the hours of feeding of juveniles (R. Brodeur, (7) an algorithm to predict the depth of juveniles (M. Wilson).

Scheffer, J, JM Baveco, DL DeAngelis, KA Rose and EH Van Nes. 1995. Super-individuals: a simple solution for modelling large populations on an individual basis. Ecol. Model. 80: 161-170.