  Bayesian methods are increasingly being applied to parameterize mechanistic
process models used in environmental prediction and forecasting. In particular,
models describing ecosystem dynamics with multiple states that are linear and
autoregressive at each step in time can be treated as statistical state space
models. In this paper we examine this subset of ecosystem models, giving closed
form Gibbs sampling updates for latent states and process precision parameters
when process and observation errors are normally distributed. We use simulated
data from an example model (DALECev) to assess the performance of parameter
estimation and identifiability under scenarios of gaps in observations. We show
that process precision estimates become unreliable as temporal gaps between
observed state data increase. To improve estimates, particularly precisions, we
introduce a method of tuning the timestep of the latent states to leverage
higher-frequency driver information. Further, we show that data cloning is a
suitable method for assessing parameter identifiability in this class of
models. Overall, our study helps inform the application of state space models
to ecological forecasting applications where 1) data are not available for all
states and transfers at the operational timestep for the ecosystem model and 2)
process uncertainty estimation is desired.
