Abundance and space use are key population-level parameters used to inform management and conservation decisions of rare and elusive species, for which monitoring resources can be limited, potentially affecting quality of model-based inference. Recently-developed methods that integrate multiple data sources arising from the same ecological process have typically been focused on data from well-defined sampling protocols, i.e. structured data sets. Despite a rapid increase in availability of large datasets, the value of unstructured or opportunistic data to improve inference about spatial ecological processes is, however, unclear. Using spatial capture-recapture (SCR) methods, we jointly analyze opportunistic recovery of biological samples, traditional SCR data resulting from systematic sampling of hair traps and rub trees, and satellite telemetry data, collected on a reintroduced brown bear population in the central Alps. We compared the precision of sex-specific estimates of density and space use derived from models using combinations of data sources ranging from traditional SCR to a fully integrated SCR model that includes both telemetry and opportunistic data. Estimates of density and space use were more precise when unstructured data were added compared to estimates from a classical SCR model. Our results demonstrate that citizen science data lend itself naturally to integration with in the SCR framework and highlight the value of opportunistic data for improving inference about space use, and in turn, of abundance and density. When individual identity and location can be obtained from opportunistic observations, such data are informative about space use and thus have the potential to improve estimates of movement and density using SCR methods. This is particularly relevant in studies of rare or elusive species, where the amount of SCR encounters is usually small, but also budget restrictions and the difficulty of collaring animals limit the number of individuals for which telemetry information is available. Spatially-referenced opportunistic data thus potentially increase both the geographic extent of a study and the number of individuals with available spatial information, providing an improved understanding of how individuals are distributed and how they use space -- fundamental components for calibrating conservation management actions.