Introduction

Abstract

Knowing how an animal or plant population “works” is of paramount importance in many fields including population ecology, conservation, and wildlife management. In the typical case, this requires estimation of demographic rates and population size based on multiple and disparate data sets. The traditional approach to population analysis is usually a two-step approach—first the parameters are estimated, and second, the estimates are used to parameterize a mathematical population model, often a matrix population model, for making inferences about population dynamics. This approach can be inefficient, because information about demographic processes contained in the timeseries of population size data is not exploited. The two-step approach may also be inaccurate because error propagation is typically not done correctly. Integrated population models jointly analyze individual- and population-level data in a single statistical model to allow the use of available information and to achieve comprehensive error propagation throughout the model. The resulting models are fairly complex hierarchical models, but owing to the flexible, powerful, and most of all, highly accessible BUGS language, these models are relatively easy to code, even for nonstatisticians. Here we introduce integrated population models, provide a brief overview of the contents of the book, and make suggestions about how the book can be used most efficiently for teaching and self-study.