A probabilistic cellular automata approach for predator–prey interactions of arrowtooth flounder ( Atheresthes stomias ) and walleye pollock ( Theragra chalcogramma ) in the eastern Bering Sea

• Published in: Canadian Journal of Fisheries and Aquatic Sciences
• Main Authors: Chen, Kun, Chan, Kung-Sik, Bailey, Kevin M., Aydin, Kerim, Ciannelli, Lorenzo
• Other Authors: Chen, Yong
• Format: Article in Journal/Newspaper
• Language: English
• Published: Canadian Science Publishing 2012
• Subjects: Aquatic Science; Ecology, Evolution, Behavior and Systematics; Bering Sea; Theragra chalcogramma
• Online Access: http://dx.doi.org/10.1139/f2011-160; http://www.nrcresearchpress.com/doi/full-xml/10.1139/f2011-160; http://www.nrcresearchpress.com/doi/pdf/10.1139/f2011-160

We developed a hybrid cellular automata (CA) modelling approach to explore the dynamics of a key predator–prey interaction in a marine system; our study is motivated by the quest for better understanding of the scale and heterogeneity-related effects on the arrowtooth flounder (Atheresthes stomias) and walleye pollock (Theragra chalcogramma) dynamics during the summer feeding season in the eastern Bering Sea (EBS), but can be readily extended to other systems. The spatially explicit and probabilistic CA model incorporates individual behaviours and strategies and local interactions among species, as well as spatial and temporal heterogeneity due to geographical and (or) environmental changes in the physical environment. The model is hybridized, with an individual-based model (IBM) approach for increasing its capacity and continuum and for balancing between computational efficiency and model validity, which makes it suitable for simulating predator–prey dynamics in a large, complex ecological environment. We focus on the functional and aggregative responses of predators to prey density at different spatial scales, the effects of individual behaviours, and the impacts of systematic heterogeneity. Simulations from the model with suitable parameter values share qualitatively similar features found in field observations, e.g., local aggregations around hydrographical features. Spatial heterogeneity is an important aspect of whether local-scale functional and aggregative responses reflect those operating over large, or global, scales.