Accounting for Demographic and Environmental Stochasticity, Observation Error, and Parameter Uncertainty in Fish Population Dynamics Models

Abstract

Bayesian hierarchical state-space models are a means of modeling fish population dynamics while accounting for both demographic and environmental stochasticity, observation noise, and parameter uncertainty. Sequential importance sampling can be used to generate posterior distributions for parameters, unobserved states, and random effects for population models with realistic dynamics and error distributions. Such a state-space model was fit to the Sacramento River winter-run Chinook salmon Oncorhynchus tshawytscha population, where a key objective was to develop a tool for predicting juvenile out-migration based on multiple sources of data. One-year-ahead 90% prediction intervals based on 1992−2003 data, while relatively wide, did include the estimated values for 2004. Parameter estimates for the juvenile production function based on the state-space model formulation differed appreciably from Bayesian estimates that ignored autocorrelation and observation noise.