Capture-recapture for population modelling.

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My research combines the use of statistics and biology to solve population dynamics problems. In particular I employ mark-recapture experiments to estimate population parameters such as survival, abundance, and migration of populations. To do so I develop stochastic models, fit and test these models, and develop computer programs to suit the needs of other scientists and wildlife managers. I study real-world applications because my problems come from biological and sociological settings. This research is important as it tackles current applied significant problems faced by fisheries scientists, wildlife managers, ecologists, and epidemiologists. Three of my projects are as follows: 1. I continue to develop methodology to deal with tag loss in mark-recapture experiments. I wish to extend this by studying dependent tag loss where individual behaviour influences tag loss. Tag loss is known to cause bias in estimates and their standard errors, and is often ignored. 2. The fit of a model to the data is an important step in modelling. There are currently no goodness-of-fit methods for closed population capture-recapture models which incorporate explanatory variables; there are an infinite number of saturated models to choose from because values of the explanatory variables for individuals not captured are unknown. I will investigate development of a goodness-of-fit test using resampling methods for a two-sample closed population capture-recapture study of injection drug users. 3. Fisheries tagging studies often violate the instantaneous-sampling assumption of mark-recapture models (see 2012 SSC Case Study) resulting in biased parameter estimates. I propose to eliminate the instantaneous sampling assumption by developing a continuous-time multistate model.