Estimation methods for nonlinear state-space models in ecology

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.

Research highlights
► We compare three estimation methods for nonlinear state-space models.
► The three methods are: Hidden Markov model (HMM), AD model builder (ADMB), and BUGS.
► ADMB outperformed the two other methods with respect to computing speed.
► State and parameter estimation performance was largely identical for all methods.
► BUGS provided wider intervals under vague prior assumptions than HMM and ADMB.