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.