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 (Field & Barros, 2014; Rantanen et al., 2022; Zhu et al., 2016). Shifts in climatic patterns enable the expansion of temperature-limited shrub vegetation at a global scale to higher latitudes and elevations(Asmus et al., 2018; Holtmeier & Broll, 2005; Kruse et al., 2019; Myers-Smith et al., 2020; Pearson et al., 2013; Rees et al., 2020; Rupp et al., 2001; Sweet et al., 2015) to ensure optimal abiotic and biotic conditions to develop (Dial et al., 2022; MacDonald et al., 1993; Reich et al., 2022). These spatial shifts in vegetation distribution can also lead to cascading effects on other trophic levels and ecosystem processes (Bjorkman et al., 2018; Dollery et al., 2006; Elmendorf et al., 2012; Sjursen et al., 2005). 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. Warmer temperatures release the nutrients trapped in cold soils, allowing optimal establishment and development of trees (Hobbie & Chapin, 1998; Sullivan et al., 2015). 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 (Rees et al., 2020).
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 (Kullman, 2002, 2007). Birch trees are intermediate light-demanding deciduous species and shed their leaves during winter to prevent moisture loss. Their leaves are palatable for herbivores, yet they tolerate browsing due to their ability to regrow after tissue damage (Maes et al., 2019; Persson et al., 2007). Birch has a shallow but wide root system that enables the tree to secure resources in a wide variety of soil types and mitigates flooding. Birch when experiencing strong top-down and bottom-up control often develops in a shrub form whereas a tree form is favoured when the control is relaxed. The evergreen trees 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 (Ottander et al., 1995), high plasticity in root architecture to secure water and nutrients in situations of drought and depleted soils (Moser et al., 2015) and possessing recalcitrant needles that are barely palatable for herbivores (Maes et al., 2019; Ramirez, Jansen, den Ouden, Moktan, et al., 2021). Evergreen trees, on the other hand, cannot grow in shaded areas because they are light-demanding (Niinemets, 2010) and they have low tolerance to tissue damage by herbivores due to their strong apex shoot dominance (Aarssen, 1995). These set of unique traits allow evergreen and broadleaf trees to grow at their minimum temperature range while forming treelines in the Fennoscandian Arctic (Kullman, 2007). The extent to which evergreen and broadleaf 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 the main tree species (pine = Pinus sylvestris, spruce = Picea abies, birch = Betula pendula) in the treeline across Fennoscandia by drawing from three distinctive datasets. The first dataset includes observations of trees made only by me (a researcher) across an elevation gradient in four distinct locations in Sweden and Norway (only for pine). 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 (only for pine). 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 (for pine, spruce and birch). 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 aim, I raised two questions: (i) Which of the three methods used to collect data is better at predicting the distribution of Pinus sylvestris to climate change? (ii) How will the distribution of the treeline (i.e., pine, spruce, birch) responds to climate change in the next 50 years? This is done by employing five independent species distribution models (SDM), one for each dataset. (I) I predict that 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 (Richardson & Whittaker, 2010). 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. (II) Treelines composed of pine, spruce and birch will only expand to higher elevations and latitudes as climate change releases the temperature limitation in tree performance.