  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 opposed to jointly estimating
them with other parameters, reduces bias and standard error of estimates. In
contrast to previous results for simple linear SSMs, strong confounding between
jointly estimated process and observation variance parameters was not found in
the models explored here. However, when observation variance was also estimated
in the motivating case study, the resulting process variance estimates were
implausibly low (near-zero). As SSMs are used in increasingly complex ways,
understanding when inference can be expected to be successful, and what aids
it, becomes more important. Our study illustrates (i) the need for relevant
process covariates and (ii) the benefits of using externally estimated
observation variances for inference for nonlinear stage-structured SSMs.
