Chapter 11 - Estimation of Demographic Rates, Population Size, and Projection Matrices from Multiple Data Types Using Integrated Population Models

Abstract

We call an integrated population model (IPM) the unified analysis of multiple data sets including population counts and demographic data. We show three basic steps that need to be taken to set up an IPM: (1) definition of a stage-classified model, (2) of the individual data type likelihoods, and (3) of the joint likelihood. Integrated analyses have important advantages compared to conventional analyses that analyze each data set separately to make an inference about population dynamics. First, integrated population models typically allow estimation of more demographic parameters because the full information available about all demographic processes operating in a population is exploited. Parameter estimates become more precise, which enhances statistical power to detect effects. Finally, multiple sources of uncertainty can be accounted for adequately, for example, those due to process variability and sampling error. The core of an integrated model is provided by linking observed changes in the population size and the demographic rates through a demographic model (usually a Leslie matrix model). The implicit definition of the joint likelihood for all data types is conceptually trivial when specifying a model in the BUGS language: we simply define the submodel likelihoods of each data set in turn. The integration is achieved because some parameters are shared among two or more submodels. We use simulated and real data sets to illustrate integrated population modeling. We also develop a model in which immigration into a population can be estimated without the need for explicit data on immigrants.