1. Inference about demographic parameters of animal and plant natural populations is important to evaluate the consequences of global changes on populations. Investigating the factors driving their variation over space and time allows evaluating the relative importance of biotic and abiotic variables in shaping the dynamics of a population. Although numerous studies have identified the factors possibly affecting population dynamics, they have barely formally determined the routes by which these different factors are related to demographic parameters.
2. We focus on mark–recapture (MR) models that provide unbiased estimators of demographic parameters, while explicitly coping with imperfect detection inherent to wild populations. MR models allow estimating the effect of covariates on demographic parameters and testing their significance in a regression-like framework. However, these models can only detect correlations and do not inform on causal pathways (e.g. direct vs. indirect effects) in the relationships between demographic parameters and the factors possibly explaining their variability.
3. We develop an integrated model to perform path analysis (PA) of MR data, to examine causal relationships among several (including demographic) variables. This approach is implemented in a Bayesian framework using Markov chain Monte Carlo.
4. To motivate our developments, we analyse 17 years of mark–recapture data from Atlantic puffins (Fratercula arctica), to investigate the mechanisms through which environmental conditions have an impact on puffins’ adult survival. Using our PA-based MR modelling approach, we found that local climatic conditions had an indirect and lagged impact on puffin survival through their influence on local abundance of herring. Besides, we found no evidence for any lagged effect through an alternative unknown pathway (e.g. abundance of another resource).
5. Our method allows elucidating pathways through which environmental, trophic or density-dependent factors influence demographic parameters, while accounting for detectability <1. This is a critical step to understand the interactions of a species with its environment and to predict the impacts of global change on its viability.