ABSTRACT
In the context of wildlife population ecology, multiple data source models have become known as “integrated population models”. In wildlife analyses, integrated population models (IPM) often combine two or more sources of data to learn about difficult to estimate population vital rates such as survival probabilities and recruitment rates. For example, data sources that are useful for estimating population abundance often are combined with data that are easier to collect over long time periods such as time series of count data for a set of unmarked individuals in a population. In their IPM, Tom Hobbs et al. combined multiple data sources including observed abundance data, age and sex classification data, and serology data into a single dynamic population model to understand the influence of various management actions on transmission of the disease. In general, the Bayesian approach naturally accommodates multiple data sources in an IPM framework.
