Monitoring threatened species is crucial to effective conservation, but requires rigorous collection of long-term data at large spatial scales, which is both time-consuming and expensive. The global increase of data collected and shared through citizen/community science (CS) programmes-like such as iNaturalist-may help this process.
In this study, researchers assessed the risks and rewards of using CS data for threatened species monitoring. They downloaded all verified, research-grade iNaturalist observations shared through GBIF, then extracted threatened species records using the IUCN Red List.
In terms of risks, the resulting dataset revealed evidence of taxonomic bias towards birds, plants and mammals. However, compared to professionally collected datasets, the CS data had a high percentage of “less charismatic” species. Along with dramatic seasonal peaks in observations, the authors noted that CS observations tended to cluster in easily accessible locations in urban and crop-land environments, and nearly 60 per cent of all records came from just five countries (USA, Canada, Mexico, Russia and New Zealand). The authors also noted dramatic seasonal peaks in observations.
On the positive side, CS data matched or even exceeded threatened species richness in some regions, recording up to four times more vertebrate species than previously known. Thirty per cent of observations took place in areas presumed as largely private, not immediately accessible to public monitoring. Overall, more than 20 per cent of all iNaturalist users contributed data about at least one threatened species.
In conclusion, the authors suggested that capitalizing on CS data might substantially shift the global conservation landscape provided that risks are sufficiently mitigated. They recommended that practitioners incorporate rigorous data quality controls while improving CS data through training, engagement and empowerment of volunteers and their local communities.