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In the context of state-space modeling, conventional usage of the deviance information criterion (DIC) evaluates the ability of the model to predict an observation at time t given the underlying state at time t. Motivated by the failure of conventional DIC to clearly choose between competing multivariate nonlinear Bayesian state-space models for coho salmon population dynamics, and the computational challenge of alternatives, this work proposes a one-step-ahead DIC [ABSTRACT FROM AUTHOR]