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.