Discussion
The current distribution of pine trees across Fennoscandia was predicted as a response to current temperature and precipitation by employing GLMs. The models' responses were then used to forecast the distribution of the pine trees using future environmental data. The same procedure was repeated three times with distinct datasets to understand which methodology used to collect data is better for predicting the spatial probabilities of pine. The probability of pine presence overall increases with temperature and decreases with precipitation. Therefore, the model thus forecasts that pine will expand in distribution to areas of higher elevation. The dataset sourced by citizen sciences was superior in predicting pine distribution.
 
The distribution of pine will expand to higher elevations
With future changes in temperature and precipitation patterns, it is expected that pine will maintain its distribution in lowland Fennoscandia, but the likelihood of having pine trees at high elevations in the Scandes will increase. These specific areas should have no light limitation in the understory, present an increase in temperature, and should remain relatively dry for pine to thrive (Moser et al., 2015; Niinemets, 2010). The current pine treelines present an island-like shape rather than a continuous shape, and therefore I expect pine will continue to expand abruptly, where one or several outposts of a pine population establish and develop at a higher elevation (Harsch & Bader, 2011). This patchy distribution will depend on the current facilitation and dieback processes remaining in the future (Wiegand et al., 2006). These future shifts in pine distribution imply that the associated food web of the treeline will likely shift in distribution as well. This shift in community composition can lead to cascading effects on important ecosystem processes (Schleuning et al., 2015) taking part in the Arctic like primary productivity (Reich, 2014), carbon storage (Manning et al., 2015), nutrient cycling (Lavorel & Garnier, 2002) and evapotranspiration (Beringer et al., 2005).
The previous result is confirmed by experiments conducted in this region where Open Top Chambers (OTCs), which simulate an increase in ambient temperature, facilitate the establishment and development of shrubs and trees outside their usual niche boundaries (Arft et al., 1999),  but also modulate the composition of other associated biodiversity such as invertebrates, mosses, and lichens (Dollery et al., 2006; Elmendorf et al., 2012; Sjursen et al., 2005). The expansion of the treeline not only responds to climatic patterns or so-called bottom-up control but also to top-down control. In this context, herbivory by large cervids plays a crucial role in the expansion of conifer treelines. Experimental work that set animal exclosures paired to a control plot in this same region confirms that herbivory limits the establishment and development of broadleaf and conifer trees on the treeline (Bognounou et al., 2018; Olofsson et al., 2001, 2009). The degree to which herbivory can limit the expansion of the treeline is dependent on the density of cervids and the composition since forest attributes decrease in a non-linear way with deer density (Ramirez, Jansen, den Ouden, Li, et al., 2021). The composition of the cervid guild, on the other hand, determines which vegetation type will more likely experience herbivory damage since deer species have different food preferences (Gill, 1992; Ramirez, 2021; Ramirez et al., 2023). Securing a large and diverse guild of cervids composed of browsers, intermediate feeders and grazers will likely maintain the current structure and composition of Arctic ecosystems by reducing the rate at which treelines are expanding across Fennoscandia (Olofsson & Post, 2018). Future studies can expand on treeline distribution –either at species or functional group level– by harnessing the results of this study and modeling it with other biological and environmental variables that shape treelines (Franklin, 2010; Midgley et al., 2006).
 
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 followed by experimental data (PLOT) and forest data (NFI). The inherent characteristic of the large sample size and the fact that 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 (Dickinson et al., 2012). 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 (Bína et al., 2021).
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.
 
Conclusion
SDMs are imperative tools to identify current areas prone to changes in the structure and composition of their ecosystems when faced with future variations in temperatures and precipitation patterns. This study highlights that the pine distribution will likely expand towards higher elevations in Fennoscandia where pine trees are not currently present. Databases sourced from citizen science are extremely useful for the development of robust models that forecast the spatial distribution of species due to their large sample size and the wide environmental gradients where data are collected.
 
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