Integrated Population Models: Achieving Their Potential

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.

Realising the promise of large data and complex models

“…There has been a natural development towards integrating datasets within a single model in recent years (Frost et al, 2023), spanning both multiple data types of a single species (Isaac et al, 2020) and data from multiple species (Barraquand & Gimenez, 2019). This means that one of the biggest challenges facing statistical ecologists is to think about whether the types of data being combined in an analysis are indeed comparable—do they have differing quality and will this affect the model performance?…”

Integrated integral population models

“…The history of the study of population dynamics has been the history of the development of increasingly complex models (Benton et al, 2006; Metcalf & Pavard 2006; Evans, 2012; Riecke et al, 2019). These models aim to make a more detailed description of the structure of a population and the drivers that determine its dynamics (Ellner & Rees 2006; Schaub & Abadi, 2011; Ellner et al 2016; Plard et al, 2019a; 2019b) This complexity goes hand in hand with a need for higher maths literacy among demographers, and computational power (Besbeas & Morgan, 2019; Plard et al, 2019b; Fung et al, 2022; Frost et al, 2023). In recent years, data integration has allowed us to obtain better descriptions and forecasting of a population’s behaviour by incorporating data at both the individual and population levels (Evans et al, 2016; Zipkin & Saunders, 2018; Zipkin et al, 2019; Frost et al, 2023).…”

“…Currently, data integration exists for population models where the population is structured by discrete variables, but their correlate for continuous variables is still missing (Schaub & Abadi, 2011; Zipkin et al, 2019; Frost et al, 2023). A revision of existing models allows for a clear overview of how this model comes about and can be readily constructed.…”

“…1, Shaub & Abadi, 2007; Zipkin & Saunders, 2018). Integration is a new trend in population ecology which recognises the benefits of incorporating multilevel data in a single population dynamics model (Evans et al, 2016, Zipkin & Saunders, 2018; Frost et al, 2023). This approach allows us to obtain better descriptions and forecasting of the population behaviour and solves the input-output mismatch of non-integrated models (Kéry & Shaub, 2011; Plard et al, 2019a; 2019b).…”

First estimate of distribution, abundance, and risk of encounter with aquaculture vessels for the rare Chilean dolphin in the entire Northern Chilean Patagonia

“…First, a comparison between an SDM fitted only to line transect data and an alternative approach integrating presence–pseudoabsence data (Bedriñana‐Romano et al, 2018) was conducted using environmental variables derived from topographic features and oceanographic models in both cases. Methods that integrate independent data sources provide novel analytical avenues for improving inference in data‐poor settings and addressing complex ecological processes across spatiotemporal scales (Miller et al, 2019; Zipkin et al, 2021; Frost et al, 2022). Second, the outputs from models were used to estimate abundance and density predictions for Chilean dolphins in the entire NCP.…”