English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

State-space models (SSMs) are a popular tool for modeling animal abundances.Inference difficulties for simple linear SSMs are well known, particularly inrelation to simultaneous estimation of process and observation variances.Several remedies to overcome estimation problems have been studied forrelatively simple SSMs, but whether these challenges and proposed remediesapply for nonlinear stage-structured SSMs, an important class of ecologicalmodels, is less well understood. Here we identify improvements for inferenceabout nonlinear stage-structured SSMs fit with biased sequential life stagedata. Theoretical analyses indicate parameter identifiability requirescovariates in the state processes. Simulation studies show that plugging inexternally estimated observation variances, as opposed to jointly estimatingthem with other parameters, reduces bias and standard error of estimates. Incontrast to previous results for simple linear SSMs, strong confounding betweenjointly estimated process and observation variance parameters was not found inthe models explored here. However, when observation variance was also estimatedin the motivating case study, the resulting process variance estimates wereimplausibly low (near-zero). As SSMs are used in increasingly complex ways,understanding when inference can be expected to be successful, and what aidsit, becomes more important. Our study illustrates (i) the need for relevantprocess covariates and (ii) the benefits of using externally estimatedobservation variances for inference for nonlinear stage-structured SSMs.

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