Improving inference for nonlinear state-space models of animal population dynamics given biased sequential life stage data.

ABSTRACT: State-space models (SSMs) are a popular tool for modeling animal abundances. Inference difficulties for simple linear SSMs are well known, particularly in relation to simultaneous estimation of process and observation variances. Several remedies to overcome estimation problems have been studied for relatively simple SSMs, but whether these challenges and proposed remedies apply for nonlinear stage-structured SSMs, an important class of ecological models, is less well understood. Here we identify improvements for inference about nonlinear stage-structured SSMs fit with biased sequential life stage data. Theoretical analyses indicate parameter identifiability requires covariates in the state processes. Simulation studies show that plugging in externally estimated observation variances, as op

SUBMITTER: Polansky L 

PROVIDER: S-EPMC7984174 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature