Observer‐oriented approach improves species distribution models from citizen science data

Citizen science platforms are increasingly growing, and, storing a huge amount of data on species locations, they provide researchers with essential information to develop sound strategies for species conservation. However, the lack of information on surveyed sites (i.e., where the observers did not record the target species) and sampling effort (e.g., the number of surveys at a given site, by how many observers, and for how much time) strongly limit the use of citizen science data. Thus, we examined the advantage of using an observer‐oriented approach (i.e., considering occurrences of species other than the target species collected by the observers of the target species as pseudo‐absences and additional predictors relative to the total number of observations, observers, and days in which locations were collected in a given sampling unit, as proxies of sampling effort) to develop species distribution models. Specifically, we considered 15 mammal species occurring in Italy and compared the predictive accuracy of the ensemble predictions of nine species distribution models carried out considering random pseudo‐absences versus observer‐oriented approach. Through cross‐validations, we found that the observer‐oriented approach improved species distribution models, providing a higher predictive accuracy than random pseudo‐absences. Our results showed that species distribution modeling developed using pseudo‐absences derived citizen science data outperform those carried out using random pseudo‐absences and thus improve the capacity of species distribution models to accurately predict the geographic range of species when deriving robust surrogate of sampling effort. We examined the advantages of using data available on Citizen science open‐access platforms through an observer‐oriented approach (i.e., considering occurrences of species other than the target species collected by the observers of the target species as pseudo‐absences and additional predictors relative to the total number of observations, observers, and days in which locations were collected in a given sampling unit, as proxies of sampling effort) to develop species distribution models.