Citizen science is a powerful tool for SDM
Citizen science-based data (ART) were the most useful in predicting the distribution of pine trees in Fennoscandia fat observations were done across the entire region in ART are the factors that improved the model output. Harnessing citizen science for broad spatial and temporal ecological research will yield a better understanding of environmental processes that researchers can only tackle in relatively large chunks of time . The Swedish government in this line has taken an important step by passing a bill in 2020 that strengthens citizen science as a tool for tackling environmental and sustainability challenges .
The PLOT dataset, although having a smaller sample size, was also efficient in predicting pine distribution probably because observations were planned according to a latitude and an elevation gradient that yielded a wide variation in temperature and precipitation patterns. This result implies that even when not possessing a large dataset on the presence and absence of targeted species, robust SDM can be constructed by planning a limited collection of data across environmental gradients where the species occur.