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Aim: In many species, density‐dependent effects on reproduction are an important driver of population dynamics. However, it is rarely considered that the direction of density dependence is expected to vary over space and time depending on anti‐predator behaviour and predator community. Aggregation may allow for effective group mobbing against avian nest predators while aggregation may also attract mammalian predators, causing negative density dependence. We aim to quantify spatial variation in the effect of conspecific breeding density on nest survival in a mobbing bird species (Eurasian oystercatcher; Haematopus ostralegus) and identify whether this variation in density dependence can be explained by the predator community. Location: Country‐wide (The Netherlands). Methods: We integrated reproductive data with breeding territory maps of Eurasian oystercatchers and occupancy maps of avian and mammalian predator species across the Netherlands for a 10‐year period. Results: Spatial variation in the composition of the predator community explained the effects of neighbour density, showing decreasing nest survival when both conspecific density and mammalian dominance increased. Also, heterospecific density (from breeding godwits and lapwing) has an additional effect on the oystercatcher nest survival. Strikingly, this pattern did not extend to mammal‐free island populations. Main conclusions: Our study provides evidence that both the strength and sign of density dependence can vary spatially within species, implying that it is dangerous to generalize results from a single local population to large‐scale management implications and modelling exercises. The study also suggests that conservation actions that aim to attract breeding birds should be prioritized in areas with fewer mammalian predators, but this idea requires further testing on island populations. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]