Many standard statistical models used to examine population dynamics ignore significant sources of stochasticity. Usually only process error is included, and uncertainty due to errors in data collection is omitted or not directly specified in the model. We show how standard time-series models for population dynamics can be extended to include both observational and process error and how to perform inference on parameters in these models in the Bayesian setting. Using simulated data, we show how ignoring observation error can be misleading. We argue that the standard Bayesian techniques used to perform inference, including freely available software, are generally applicable to a variety of time-series models.

Keywords: Bayesian; MCMC; Normal Dynamic Linear Models; Nonlinear Models; Observation Error; State-Space; Time-Series; Uncertainty

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