Integrated Population Models:Achieving Their Potential

Frost, Fay and McCrea, Rachel and King, Ruth and Gimenez, Olivier and Zipkin, Elise (2023) Integrated Population Models:Achieving Their Potential. Journal of Statistical Theory and Practice, 17 (1). ISSN 1559-8608

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Abstract

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:
Journal Article
Journal or Publication Title:
Journal of Statistical Theory and Practice
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
179874
Deposited By:
Deposited On:
29 Nov 2022 15:35
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Jan 2023 15:45