Capitalising on Opportunistic Data for Monitoring Species Relative Abundances

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.