1. Being a non-invasive and inexpensive method, the analysis of faecal corticosteroid metabolites (FCM) is increasingly being applied in wildlife research. Various environmental factors have been shown to influence FCM levels, but most studies did not account for inter-individual variance, which we hypothesized may substantially affect the results. 2. We combined FCM analysis with genetic analysis to identify the sex and individual of samples collected in three consecutive winters, with repeated samples per individual, across the entire range of an endangered population of capercaillie (Tetrao urogallus) in Southwestern Germany. Using generalized additive mixed models, we modelled FCM levels as a function of sex, season and environmental covariates at two spatial scales (sampling location and home range scales). We compared two models: one including information on the individual animal, and the other excluding this information (i.e. naïve model) to assess the influence of individual effects on the results obtained. 3. Most of the variance (44.0% and 45.1% at the sampling and home range scale, respectively) was explained by the inter-individual differences, and only very little (4.0% and 5.1%, respectively) by the environmental predictors. When ignoring individual effects, the model results changed considerably, with other, previously non-informative predictors, becoming significant. 4. In the full models, accounting for inter-individual variance, no effect was found of weather conditions, at either scale. FCM levels were negatively correlated with habitat quality, at the sampling location, whereas human recreation at the home range scale led to elevated FCM levels. In the naïve models, two additional predictors appeared significant: one weather variable at local scales and two at home range scale. In all models, seasonal FCM patterns differed significantly between males and females. 5. Synthesis and applications. Our results highlight the importance of considering the effects of individual heterogeneity when studying FCM in wildlife research, as ignoring information on the individual might lead to erroneous conclusions. Combining FCM analyses with genetic analyses can be an efficient approach to adequately address this issue.21-Feb-2018