Conceptualizing and quantifying body condition using structural equation modelling: A user guide. Issue 11 (6th September 2021)

• Record Type: Journal Article
• Authors: Frauendorf, Magali; Allen, Andrew M.; Verhulst, Simon; Jongejans, Eelke; Ens, Bruno J.; van der Kolk, Henk‐Jan; de Kroon, Hans; Nienhuis, Jeroen; van de Pol, Martijn
• Is Part Of: Journal of animal ecology. Volume 90:Issue 11(2021)
• Page Start: 2478
• Page End: 2496
• Publication Date: 2021-09-06
• Subjects: body condition index -- composite variable -- fitness component -- latent variable -- multiple regression -- multiple‐indicator multiple‐cause model -- path analysis -- principal component analysis; Animal ecology -- Periodicals; 591.7
• DOI: 10.1111/1365-2656.13578
• Languages: English
• ISSNs: 0021-8790

Abstract

Abstract: Body condition is an important concept in behaviour, evolution and conservation, commonly used as a proxy of an individual's performance, for example in the assessment of environmental impacts. Although body condition potentially encompasses a wide range of health state dimensions (nutritional, immune or hormonal status), in practice most studies operationalize body condition using a single (univariate) measure, such as fat storage. One reason for excluding additional axes of variation may be that multivariate descriptors of body condition impose statistical and analytical challenges. Structural equation modelling (SEM) is used in many fields to study questions relating multidimensional concepts, and we here explain how SEM is a useful analytical tool to describe the multivariate nature of body condition. In this 'Research Methods Guide' paper, we show how SEM can be used to resolve different challenges in analysing the multivariate nature of body condition, such as (a) variable reduction and conceptualization, (b) specifying the relationship of condition to performance metrics, (c) comparing competing causal hypothesis and (d) including many pathways in a single model to avoid stepwise modelling approaches. We illustrated the use of SEM on a real‐world case study and provided R‐code of worked examples as a learning tool. We compared the predictive power of SEM with conventional statistical approaches that integrate multiple variables into one condition variable: multiple regression and principal component analyses. We show that model performance on our dataset is higher when using SEM and led to more accurate and precise estimates compared to conventional approaches. We encourage researchers to consider SEM as a flexible framework to describe the multivariate nature of body condition and thus understand how it affects biological processes, thereby improving the value of body condition proxies for predicting organismal performance. Finally, we highlight that it can be useful for other multidimensional ecological concepts as well, such as immunocompetence, oxidative stress and environmental conditions. Abstract : Structural equation modelling (SEM) is a powerful and flexible statistical tool that can lead to models of higher predictive power and with more accurate as well as precise estimates compared to conventional approaches. The authors encourage researchers to consider SEM as a flexible framework to quantify the multivariate nature of body condition and thus understand how it affects biological processes.