Skip to main content
Kent Academic Repository

Integrated Population Models: Achieving Their Potential

Frost, Fay, McCrea, Rachel, King, Ruth, Gimenez, Olivier, Zipkin, Elise (2022) Integrated Population Models: Achieving Their Potential. Journal of Statistical Theory and Practice, 17 (1). Article Number 6. ISSN 1559-8616. (doi:10.1007/s42519-022-00302-7) (KAR id:98075)


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. Analyzing each data set separately implicitly discards the common information contained in the other data sets. An alternative approach that aims to optimize 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 analyzes 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.

Item Type: Article
DOI/Identification number: 10.1007/s42519-022-00302-7
Additional information: For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
Uncontrolled keywords: Ecological Statistics, Abundance, Ecological insight, Integrating data, Multiple surveys
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Funders: Engineering and Physical Sciences Research Council (
Leverhulme Trust (
Agence Nationale de la Recherche (
National Science Foundation (
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 30 Nov 2022 14:21 UTC
Last Modified: 12 Dec 2023 12:55 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.