An index of fish abundance is often calculated from the estimated marginal means predicted from a generalised linear model fitted to fishery catch rate data with suitable explanatory variables. However, fishing grounds can change, because fleets often shift their activity to target different areas of a fish population over time, which can lead to spatiotemporal gaps in catch rate data. These missing data, if ignored, may result in a biased index. This thesis develops and evaluates several alternative imputation methods for reducing such biases. Evaluations were done by analysing both simulated and real fisheries datasets.
|Award date||19 May 2017|
|Publication status||Unpublished - 2017|