With the internet, a massive amount of information on species abundance can be collected under citizen science programs. However, these data are often difficult to use directly in statistical inference, as the data collection is generally opportunistic under such programs, and the distribution of the sampling effort is often not known. In this paper, we developed a statistical framework to combine such ''opportunistic data'' with data collected using schemes characterized by a known sampling effort. We illustrated the framework with typical bird datasets from the Aquitaine region, south-western France. We demonstrated that such a framework can provide estimates that are always more precise than the ones obtained from the dataset with a known sampling effort alone. The gain in precision may be considerable if the opportunistic data are abundant. We also show that estimates could be obtained even for species recorded only in the opportunistic scheme. Opportunistic data combined with a relatively small amount of data collected with a known effort may thus provide access to precise estimations of quantitative changes in abundance. This should significantly change the organisation of large scale monitoring schemes, particularly for the rarer species. The framework can be readily used to monitor temporal changes but with more restrictive conditions for monitoring spatial changes. The framework presented in this paper will be improved in the future to allow a more easy application to the estimation of the spatial distribution of a species.