The number of science projects with citizen (volunteer) participants is increasing nowadays. Despite some advantages citizen science have which directly influence science fields, the largest weakness of citizen science is the issue of the reliability of data collected by non-specialists. In this study, we assess the reliability of data collected by citizens and identify the factors that impede the credibility. We selected two species, Pelophylax chosenicus and Dryophytes suweonensis, which have specific habitat, breeding and brumation positions. The location data for both species were collected by the global citizen engagement project “iNaturalist”. As a result, 89.3% for P. chosenicus and 37.1% for D. suweonensis of the location data were estimated to be erroneous. No difference was observed in the data accuracy between experts and citizens for D. suweonensis because the mating call is the main classification key for the species. On the other hand, a significant difference was confirmed in the case of P. chosenicus, whose external characteristics are its classification key. Our study shows that efforts to improve reliability, including appropriate species selection and survey methods, are necessary to use citizen participation data in science research.

Globally, an increase in the transportation and expansion of the pet market is the most important cause of the invasion of non-native species. Invasion of non-native species disturbs native ecosystems and leads to socio-economic problems. The pet trade involving turtles has been globally recognized as the route through which non-native species enter ecosystems. As a result, the invasion of non-native turtles worldwide is causing problems such as competition, predation, transmission of parasites, and hybridization with native turtles. Every year, both the number of non-native turtles imported as pets in the Republic of Korea and the number of introduced species found in the wild is increasing. However, the current status of non-native turtles in the wild is not well known, posing major challenges to their management. In this study, we aimed to determine the current status of non-native turtles introduced into the wild in Korea. We analyzed the factors associated with the detection and distribution of non-native turtles. In total, 1587 of non-native turtles (three families, six genera, and 13 species including subspecies) were found in 648 sites in Korea: Chelydra serpentina, Mauremys sinensis, Chrysemys picta bellii, Graptemys ouachitensis, G. pseudogeographica pseudogeographica, G. p. kohni, Pseudemys concinna, P. nelsoni, P. peninsularis, P. rubriventris, Trachemys scripta elegans, T. s. scripta, and T. s. troostii. There was relationship between the distribution of non-native turtles and environmental factors such as precipitation and temperature. Moreover, human factors such as number of human populations and size of region were significantly related with the distribution and number of non-native turtles. In conclusion, it is likely that human factors are associated with the influx of invasive turtles to the natural habitat, while the possibility of survival and adaption for the turtles is associated mainly with environmental factors. Our result will be an essential guideline not only for understanding the current status of non-native turtles in Korea, but also for establishing strategies for management and control.

It is important to understand the dynamics of population size to accurately assess threats and implement conservation activities when required. However, inaccurate estimates are harming both the threat estimation process, and the resulting conservation actions. Here, we address the extinction threats to Scincella huanrenensis, a species described in the People’s Republic of China, but also occurring on the Korean peninsula. Estimating the threats to the species is not an easy task due to its unknown population status in the Democratic People’s Republic of Korea. Here we analysed the literature to acquire the known presence point for the species, along with datapoints originating from opportunistic field surveys, and employed habitat suitability models to estimate the range of the species. We then followed the categories and criteria of the IUCN Red List of Threatened Species to assess the extinction risk of the species. We found the species not to be fitting the threatened category at the global scale based on the range size, the only category for which enough data was available. We recommend the status of the species on the IUCN Red List of Threatened Species to be updated as it is now listed as critically endangered (CR), a listing fitting a national assessment for the People’s Republic (PR) of China. While this species is possibly less threatened than currently listed, this is not a genuine improvement, and specific conservation aspects should not be neglected due to its specialisation to medium to high elevation habitat.

The lack of knowledge about geographic distribution and environmental preference can hinder conservation efforts for rare and threatened species. Open‐source databases provide an opportunity to address these knowledge gaps through the geographic information they hold on species worldwide. However, to protect rare and endangered species, open‐source databases often assign locations that do not match the original locations, which introduce inaccuracies in occurrence records (e.g., the “obscured” function in iNaturalist replaces the original location with a random location in a 0.2 × 0.2° cell). We tested the efficacy of the iNaturalist's obscured function in concealing geographic information and the function's impact on the species distribution modeling of 3 endangered species in South Korea: gold‐spotted pond frogs (Pelophylax chosenicus), Reeves’ turtles (Mauremys reevesii), and Mongolia racerunner (Eremias argus). We collected occurrence data (orginal data) for these 3 species and uploaded the data to iNaturalist. We then compared location, elevation, and habitat area in the original data set with these data in the obscured data set. To investigate the differences in species distribution, we ran species distribution models with both data sets. We also assessed the awareness of obscured function in peer‐reviewed articles for which occurrence records from iNaturalist were used. The locations assigned by the obscured function significantly altered the geographic information of the species, including elevational range, habitat type, and environmental variables relevant to species distribution. Potential distributions estimated using locations assigned under the obscured function were different from those estimated using the original data. Only 4 out of 170 peer‐reviewed articles acknowledged the presence of obscured data in iNaturalist, suggesting that most researchers are unaware of this issue. The locations assigned by the obscured function can cause serious problems in species distribution modeling and thus may negatively affect conservation of endangered species. We encourage researchers to thoroughly vet data obtained from open‐source databases and urge database platforms to make it clear when data have been obscured.