Crowdsourcing citizen science as a data source for the conservation of threatened species

For effective conservation of threatened species, accurate data is essential. Given the variable quality of Crowdsourcing Citizen Science (CCS) data, we tested its usability for conservation and implemented a filter to improve its quality. We used data from three threatened species sourced from two online platforms (eBird and WikiAves) and literature. The data were divided into three groups: control, locality, and cleaned. For the cleaned group, we refined the locality group data by removing records with environmental information that differed significantly from the remaining records. Models were built for each group and compared using Extent of Occurrence (EOO), Area of Occupancy (AOO), suitable area, and environmental space parameters. CCS provided most of the accurate data for the three species. Models from the locality group overestimated the environmental space and, consequently, the suitable area. This overestimation was reduced in the cleaned group. Overall, the proposed filter improved model quality. CCS data is crucial for the conservation planning of threatened species if carefully treated to avoid misinformation. The representativeness of CCS data varies with species familiarity and recording difficulty, making it especially important for poorly studied species. The tested filter is useful for data cleaning, complementing existing methods, and providing models for poorly known species.