Integrated Population Models: Achieving their Potential

Fay Frost*, Rachel McCrea, Ruth King, Olivier Gimenez, Elise Zipkin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Precise and accurate estimates of abundance and demographic rates are primary quantities of interest within wildlife conservation and management. Such quantities provide insight into population trends over time and the associated underlying ecological drivers of the systems. This information is fundamental in managing ecosystems, assessing species conservation status and developing and implementing effective conservation policy. Observational monitoring data are typically collected on wildlife populations using an array of different survey protocols, dependent on the primary questions of interest. For each of these survey designs, a range of advanced statistical techniques have been developed which are typically well understood. However, often multiple types of data may exist for the same population under study. Analysing each data set separately implicitly discards the common information contained in the other data sets. An alternative approach that aims to optimise the shared information contained within multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model”. This integrated modeling approach simultaneously analyses all the available data within a single, and robust, statistical framework. This paper provides a statistical overview of ecological integrated models, with a focus on integrated population models (IPMs) which include abundance and demographic rates as quantities of interest. Four main challenges within this area are discussed, namely model specification, computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical tools to ensure that full utility can be made of IPMs for future studies.

Original languageEnglish
Article number6
JournalJournal of Statistical Theory and Practice
Publication statusPublished - 15 Nov 2022


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