Magali Frauendorf

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.

The wild boar population has increased enormously in all of Europe over the last decades and caused problems like crop damage, transmission of diseases, and vehicle accidents. Therefore, it is necessary to investigate the underlying causes of this increase in order to be able to manage populations effectively. The purpose of this study was to analyse how environmental (food and climate) and physiological factors (maternal weight and age) as well as hunting and population density influence the litter size of wild boar populations in Northern Germany. The mean litter size in the studied population for the whole period was 6.6 (range 1–12), which is one of the highest in all of Europe. Litter size was positively influenced by maternal body weight, higher mast yield of oak as well as higher temperature in combination with higher precipitation in summer. Only higher temperature or only higher precipitation in summer however had a negative effect on litter size production. Probably, weather and food conditions act via maternal body weight on the litter size variation in wild boar. Hunting as well as population density did not affect the litter size variation in this study which might indicate that wild boar population did not reach carrying capacity yet.