Opportunistic reports of species occurrence have become a major source of biodiversity data, due in part to the popularity of citizen science species reporting programs as well as the digitization of historical data. Opportunistic species occurrence data have great potential to inform research into spatial and temporal patterns in species distributions. But the unsystematic way that these data are collected creates challenges for analysis. Opportunistic species occurrence data are presence-only, and are often characterized by unknown spatial, temporal, and taxonomic biases. Many recent studies have focused on improving analysis approaches for presence-only citizen science data. We use a systematic mapping approach to categorize methods used to analyze presence-only occurrence data, and we combine our systematic map with bibliometric analyses to characterize the connections between applications of these approaches in the literature.
Results/Conclusions
Our results suggest that, while much of the literature regarding presence-only data analysis methods has focused on data obtained through large, open-access databases such as GBIF, a substantial portion of presence-only data are obtained from small open databases and private data. We investigate the analyses applied to presence-only data from these sources, considering trends in study system, scale, location, and ecological subdiscipline. Further, our results suggest that many datasets analyzed using methods developed for opportunistic data were collected with some degree of structure. We report on the degree of structure among presence-only data analyzed in the literature and consider some common reasons for the application of opportunistic analysis approaches to these datasets. We expect that our results will be useful for researchers collecting or analyzing presence-only biodiversity data as well as for researchers developing new approaches for this type of data.