Combining multiple data sources with different biases in state-space models for population dynamics.

ABSTRACT: The resolution at which animal populations can be modeled can be increased when multiple datasets corresponding to different life stages are available, allowing, for example, seasonal instead of annual descriptions of dynamics. However, the abundance estimates used for model fitting can have multiple sources of error, both random and systematic, namely bias. We focus here on the consequences of, and how to address, differing and unknown observation biases when fitting models.State-space models (SSMs) separate process variation and observation error, thus providing a framework to account for different and unknown estimate biases across multiple datasets. Here we study the effects on the inference of including or excluding bias parameters for a sequential life stage population dynamics SSM u

SUBMITTER: Polansky L 

PROVIDER: S-EPMC10249046 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature