Assessing Ecosystem State Space Models: Identifiability and Estimation

TL;DR

This paper evaluates the use of Bayesian state space models for ecosystem dynamics, focusing on parameter estimation, identifiability, and methods to improve estimates with incomplete data.

Contribution

It introduces a tuning method for the timestep and demonstrates data cloning for assessing parameter identifiability in ecosystem state space models.

Findings

Process precision estimates decline with larger observation gaps.

Tuning the timestep improves parameter estimates.

Data cloning effectively assesses parameter identifiability.

Abstract

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.