Capture-recapture surveys are widely used to study survival rates and population trends in animal populations. They consist of a series of occasions, on which animals are captured and released in the population. Individuals captured for the first time are marked with a unique identification number, while recaptured individuals have their identifier number recorded. Oftentimes, a single population is studied using additional types of surveys, e.g. visual count surveys, carcass recoveries or telemetry surveys. The integration of all the data in a single statistical analysis is a very exciting mechanism to fully exploit the potential of the data. This is typically achieved by approximating the full joint likelihood of the data as a product of likelihoods.
In this talk I will introduce a new Bayesian modeling approach which integrates capture-recapture data with other sources of data in a fully explicit manner. I will focus on the simplest case of integrating capture-recapture data along with count data and compare the performance of the new approach with the usual approach (product of likelihoods) using both a simulation study and a real world application. If time permits I will present a more complex use of the method which integrates capture-recapture data, dead recovery data and snorkel survey data to study the movement of Chinook salmon (Oncorhynchus tshawytscha) from the ocean to spawning grounds on the West Coast of Vancouver Island, Canada.