  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 their collection is
generally opportunistic, and the distribution of the sampling effort is often
not known. In this paper, we develop a general statistical framework to combine
such "opportunistic data" with data collected using schemes characterized by a
known sampling effort. Under some structural assumptions regarding the sampling
effort and detectability, our approach allows to estimate the relative
abundance of several species in different sites. It can be implemented through
a simple generalized linear model. We illustrate the framework with typical
bird datasets from the Aquitaine region, south-western France. We show that,
under some assumptions, our approach provides estimates that are more precise
than the ones obtained from the dataset with a known sampling effort alone.
When the opportunistic data are abundant, the gain in precision may be
considerable, especially for the rare species. We also show that estimates can
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 accurate and precise estimates
of quantitative changes in relative abundance over space and/or time.
