Introduction
The effects of climate change in the Arctic are particularly prominent, as temperatures have risen nearly three to four times as fast as in the rest of the planet . Shifts in climatic patterns enable the expansion of temperature-limited shrub vegetation at a global scale to higher latitudes and elevations . This phenomenon is not only particular to shrubs but also to treelines that occur at the edge of the forest-tundra habitat, where temperature-limited trees are barely capable of growing. Treelines shadow climate patterns by expanding or shrinking their distribution across large spatial scales to ensure optimal abiotic and biotic conditions to develop . Warmer temperatures release the nutrients trapped in cold soils, allowing optimal establishment and development of trees . Treelines are expanding in range across the planet due to warmer ambient temperatures . The response of treelines to climate change is tree species-specific, since the set of traits particular to each tree species is what determines its ability to establish, develop, reproduce, and disperse to novel sites. Studies in Fennoscandia however have forecasted the distribution of broadleaf trees and overseen the distribution of conifer trees .
The treelines in Fennoscandia consist mainly of birch (Betula pubescens ) and in less proportion of pine (Pinus sylvestris ) or spruce (Picea abies ) and occasionally mix-species treelines are found . The evergreen pine trees, the target species of this study, are generally known to have a unique set of traits that allows them to adapt to unique conditions such as saving energy during winter by not shedding their needles , high plasticity in root architecture to secure water and nutrients in situations of drought and depleted soils and possessing recalcitrant needles that are barely palatable for herbivores . Pine trees on the other hand, cannot grow in shaded areas because they are light-demanding and they have low tolerance to tissue damage by herbivores due to their strong apex shoot dominance . These set of unique traits allow pine trees to grow at their minimum temperature range while forming treelines in the Fennoscandian Arctic . The extent to which pine trees will shift their spatial distribution as a response to future environmental conditions due to climate change remains largely unknown.
The purpose of this study is to predict the distribution of pine trees in the Fennoscandian Arctic by drawing from three datasets that implemented distinctive methods during data collection. This is done by employing three independent species distribution models (SDM), one for each of the datasets. The first dataset includes observations of the presence and absence of pine made only by me (a researcher) across an elevation gradient in four distinct locations in Sweden and Norway. The second dataset belongs to the Swedish National Forest Inventory and includes a wide network of permanent plots across Sweden to estimate forest metrics and track these metrics over time. The third dataset belongs to the Artdatabanken which is an online platform that gathers observations reported by citizens, nature officers, and researchers from across all regions in Sweden. Understanding the implications of how different data collection methods have on the predictions yielded by SDM provides researchers with the necessary input to choose the best experimental design for their study.
To address this research objective, I raised two questions: (i) How will the distribution of pine trees respond to climate change in the next 50 years? (ii) Which method used to collect data is better at predicting the distribution of pine trees as a response to climate change? (I) I predict that the pine distribution will only expand to higher elevations and latitudes as climate change releases the temperature limitation in pine performance. (ii) Citizen science data will be best suited to predict the distribution of pine trees because this method yields a greater number of observations that are distributed across an entire elevation gradient . Although my dataset compiles pine observations along an elevation gradient, it falls short in the number of observations due to the limitation in manpower. The NFI compiles a great number of pine observations, but these do not cover the entire elevation gradient.