\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[ngerman,greek,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{Expanding forests in alpine regions: space-for-time indicates a
corresponding shift in belowground fungal communities}



\author[1]{Lea-Rebekka Tonjer}%
\author[1]{Ella Thoen}%
\author[1]{Luis Morgado}%
\author[2]{Synnøve Smebye Botnen}%
\author[1]{Sunil Mundra}%
\author[3]{Line Nybakken}%
\author[1]{Anders Bryn}%
\author[1]{Håvard Kauserud}%
\affil[1]{University in Oslo}%
\affil[2]{Universitetet i Oslo Det Matematisk-naturvitenskapelige Fakultet}%
\affil[3]{Norwegian University of Life Sciences}%


\vspace{-1em}



  
  \date{June 02, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Climate change causes upward shift of forest lines worldwide, with
consequences on soil biota and carbon (C). Using a space-for-time
approach, we analyse compositional changes in the soil biota across the
forest line ecotone, an important transition zone between different
ecosystems. We collected soil samples along transects stretching from
subalpine mountain birch forests to low-alpine vegetation. Soil fungi
and micro-eukaryotes were surveyed using DNA metabarcoding of the 18S
and ITS2 markers, while ergosterol was used to quantify fungal biomass.
We observed a strong shift in the soil biota across the forest line
ecotone: Below the forest line, there were higher proportions of
basidiomycetes and mucoromycetes, including ectomycorrhizal and
saprotrophic fungi. Above, we observed relatively more root-associated
ascomycetes, including Archaeorhizomycetes, ericoid mycorrhizal fungi
and dark septate endophytes. Ergosterol and percentage C content in soil
strongly and positively correlated with the abundance of root-associated
ascomycetes. The predominance of ectomycorrhizal and saprotrophic fungi
below the forest line likely promote high C turnover, while
root-associated ascomycetes above the forest line may enhance C
sequestration. With further rise in forest lines, there will be a
corresponding shift in the belowground biota linked to C sequestration
processes.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Ecosystems all over the world are affected by climate change (Parmesan,
2006; Pecl et al., 2017). High latitude and altitude ecosystems are
especially exposed because of arctic amplification and
elevation-dependent warming (Pepin et al., 2015; Serreze \& Barry,
2011). In temperate and boreal regions, one of the most striking
ecological transitions in high elevation areas is the change from boreal
lowland forests, to alpine highlands without forests (Fig. 1a). This
marked ecotone, termed the forest line (here) or treeline, varies in
altitude and species composition across the globe, and has in many
regions moved towards higher elevations during the last decades (Harsch,
Hulme, McGlone, \& Duncan, 2009). Although many mountain regions are
influenced by land use, the uppermost alpine forest lines are mainly
temperature driven (K\selectlanguage{ngerman}örner, 2012). Hence, as a response to climate
warming, changes in vegetation are predicted, including an upward shift
of the forest line into mountain ecosystems (Beckage et al., 2008; Chen,
Hill, Ohlemuller, Roy, \& Thomas, 2011). Recent studies have documented
positive climate feedbacks from expanding alpine forest lines (de Wit et
al., 2014) and an increase in the elevation of alpine forest lines will
probably increase the local temperature and therefore accelerate the
ongoing expansion further (Rydsaa, Stordal, Bryn, \& Tallaksen, 2017).

Large areas in Fennoscandia are covered by mountain birch (\emph{Betula
pubescens} ssp\emph{.} ~\emph{czerepanovii} ) forest, that also forms
the alpine forest lines in most areas in this region (Bryn \& Potthoff,
2018). At the forest-alpine ecotone, the vegetation is normally shrub
dominated in a transition zone, before shifting to vegetation dominated
by ericaceous plant species, commonly referred to as low-alpine
vegetation (Fig. 1b). In Norway, an upward shift in forest lines has
been observed, both due to climate warming and land use change (Bryn \&
Potthoff, 2018). The expansion of subalpine trees and shrubs leads to
increased primary production, and thus aboveground carbon (C) fixation.
However, studies from alpine and arctic regions have shown that soil
organic C content is significantly higher under alpine heaths than under
shrub and forest vegetation (Parker, Subke, \& Wookey, 2015; Sorensen et
al., 2018). Parker \emph{et al.} (Parker et al., 2015) showed that soil
C turnover was faster and belowground C pools smaller in the forest and
shrub vegetation compared to arctic heath. Similarly, Sørensen \emph{et
al.} (Sorensen et al., 2018) showed that C stocks were lowest beneath
shrub vegetation and significantly higher beneath ericaceous plants in
the alpine heath. Furthermore, it is documented that litter decomposes
faster in the shrub and mountain birch forest vegetation than in the
alpine heath, independent of the litters resistance to decomposition
(Parker et al., 2015). These differences in soil C pools may largely be
regulated by belowground microorganisms, including fungi, similar to in
lowland systems (Clemmensen et al., 2013; Frey, 2019). Improved
knowledge of these communities and their functional roles are important
for a deeper understanding of the C pool dynamics across the forest line
ecotone.

Belowground fungi contribute to soil C processes in various ways, and
can roughly be grouped into parasites, mutualistic symbionts and
saprotrophs, although there is a blurry transition between nutritional
strategies (Selosse, Schneider-Maunoury, \& Martos, 2018). Saprotrophic
fungi decompose and recycle dead organic matter, thus releasing C to the
atmosphere. Mycorrhizal fungi form mutualistic symbiosis with plant
roots, where they receive freshly fixed C from their plant host in
exchange for water and nutrients. Saprotrophic and mycorrhizal fungi are
thus crucial components of the global C cycle, as well as the nitrogen
(N) and phosphorus (P) cycles (Lindahl \& Tunlid, 2015; Smith \& Read,
2008; Talbot, Allison, \& Treseder, 2008). Mycorrhizal fungi can be
divided into functional groups depending on their plant hosts, structure
and function (Smith \& Read, 2008). The three main groups are arbuscular
mycorrhizal (AM), ectomycorrhizal (EcM), and ericoid mycorrhizal (ErM)
fungi, which establish symbiosis with mainly herbs and graminoids (AM),
trees and shrubs (EcM), and ericoid plants (ErM), respectively (Smith \&
Read, 2008). EcM fungi, which dominate in northern forest ecosystems,
can promote turnover of organic matter in some systems (Bodeker et al.,
2014; Frey, 2019; Lindahl \& Tunlid, 2015; Talbot et al., 2008) or C
sequestration in others (Averill, Turner, \& Finzi, 2014; Koide,
Fernandez, \& Malcolm, 2014; Orwin, Kirschbaum, St John, \& Dickie,
2011). Root-associated dark septate endophytes (DSE) are also common in
alpine and arctic vegetation and often found in the same environments as
ErM fungi (Newsham, Upson, \& Read, 2009; Olsrud, Michelsen, \&
Wallander, 2007). It has recently been shown that DSE can promote
nutrient uptake in plants (Hill et al., 2019). Taxonomically, it can be
hard to separate between fungi forming DSE and ErM since both groups are
dominated by ascomycetes, especially Leotiomycetes. Both DSEs and ErM
fungi have melanized hyphae resistant to decomposition, which has been
proposed to play a central role in soil C sequestration (Clemmensen et
al., 2015; Fernandez \& Koide, 2013). Above the forest line, ericoid
dwarf shrubs dominate the alpine heath. Thus, as the forest migrates
upwards, a shift in belowground dominating functional groups is
expected, potentially shifting current soil C dynamics. Other
micro-eukaryotes, including invertebrates and protists, are also
essential members of the soil biota and the soil food web (Geisen et
al., 2018; Phillips et al., 2019), although their contribution to
belowground C processes is not well studied.

To improve our understanding of the belowground biota across the
mountain birch forest line ectone, we conducted a survey designed to
measure belowground compositional changes across the ecotone in nine
sites spread across southern Norway. Using DNA metabarcoding, we
targeted the ITS2 (fungi only) and 18S (all eukaryotes) regions of
ribosomal DNA from soil, and we measured ergosterol (a proxy for fungal
biomass) to obtain both qualitative and quantitative (fungi only)
information about the belowground biota. We aimed to couple these
results with both abiotic (soil edaphic factors, climate and bedrock)
and biotic factors (aboveground vegetation), to investigate the
relationships between above- and belowground changes across the ecotone.
More specifically, we wanted to investigate whether: (i) there is a
strong compositional change in the soil biota, both in terms of
taxonomic and functional groups, across the forest line ecotone; (ii)
the changes in taxonomic and functional groups of soil fungi are
strongly correlated with changes in aboveground vegetation, and (iii)
more fungal biomass is found above the forest line, which may be due to
the presence of more ErM/DSE fungi with recalcitrant mycelium. To assess
the generality of our observations, we analysed soil samples obtained
along ecotones in nine replicated sites spread across southern Norway.

\textbf{Materials and methods}

Study design and sampling

We conducted sampling in September/October 2017 at nine different
high-elevation forest line sites in south-central Norway (Fig. 1). Our
selection of sites was based on a forest line model for Norway (Bryn \&
Potthoff, 2018). Based on a wider set of \emph{a priori} candidate
sites, we selected nine sites with a clear elevation gradient, a
distinct shift from mountain birch forests to alpine vegetation and
located at least 15 km from each other (Fig. 1a). At each site, we
collected soil samples from plots located every 20 m along a 200 m
transect, stretching from 100 m distance below the mountain birch forest
line to 100 m distance above (i.e. 11 plots per transect and 99 plots
all together; Fig. 1c). At each plot, five soil cores were collected in
each orientation (i.e. North, East, South, West) and the centre within a
circle with 1.5 m radius using a 3.8 cm in diameter soil corer, and
pooled to one representative soil sample, excluding the mineral soil
layer. Most often, there was no clear division between the litter and
organic layers and we therefore analysed them together. We removed
living green plants and mosses. Soil samples were kept at -20°C during
fieldwork and later stored at -80 °C before further processing. Within
each plot, we recorded the understory vegetation to species level for
lichens, mosses and higher plants and categorized the abundance of each
species into three levels: rare (1), common (2) and dominant (3). Based
on this information, we calculated the proportion of each plant species
per plot, as well as the overall proportion of the following plant
groups: ErM plants, EcM plants, AM plants, lichens, mosses
and\emph{Pyrola} species, the latter making arbutoid mycorrhiza.

We obtained the following site-specific environmental variables from a
published data (Horvath et al., 2019): aspect, annual precipitation,
mean temperature, bedrock and slope. The three types of bedrock provided
in this dataset (``nutrient-poor bedrock'', ``nutrient-average
bedrock'', ``nutrient-rich bedrock'') were ranked on a 1-3 scale. All
environmental and climatic variables were then zero skewness transformed
and standardized.

Soil analyses

Soil samples were thawed and handled further in a laminar flow hood,
where plants and coarse (\textgreater{}2mm) pieces of wood and roots
were removed. Approximately 70 g of soil sample was freeze-dried
(Labconco corporation, Kansas City, MO, USA) in falcon tubes for 36 h
and then pulverised using a FastPrep-24 beadbeater (M.P. Biomedicals,
CA, USA) in two rounds of 20 sec at 25 MHz and subsampled for the
different analyses. For soil pH measurements, we diluted 0.5 g of
freeze-dried soil in 5 mL dH\textsubscript{2}O for one hour, and
measured pH using a LAQUA-TWIN-11 pH Meter (Horiba Scientific, Kyoto,
Japan) following the manufacturers protocol. Soil C and N concentration
was determined, using 0.5 g of freeze-dried soil, by a flash elemental
analyzer (Thermo Finnigan Flash EA 1112, ThermoFisher Scientific,
Waltham, USA). Soil P concentration was determined by a segmented flow
analyzer (SEAL AA3 HR AutoAnalyse, SEAL Analytical Ltd, Southampton,
UK). We used approximately 200 mg of freeze-dried soil for measuring
free and total soil ergosterol concentrations (mg
g\textsuperscript{-1}DW) using a similar protocol as in (Ransedokken,
Asplund, Ohlson, \& Nybakken, 2019).

\par\null

Molecular methods

We extracted DNA using a CTAB-Chloroform DNA extraction protocol
followed by a column based DNA purification using the E.Z.N.A.® Soil DNA
Kit following the manufacturers protocol (Omega Bio-tek, Norcross, USA).
Technical replicates and extraction negatives (negative controls) were
introduced during DNA extraction, while mock communities (positive
controls) were introduced during the PCR step. We PCR amplified both the
rDNA ITS2 and 18S regions. The primers gITS7 (forward) and ITS4
(reverse) (Ihrmark et al., 2012) were used for amplifying the ITS2
region, and TAReuk454FWD1 (forward) and TAReukREV3 (reverse) (Stoeck et
al., 2010) for 18S. All primers were tagged with unique molecular
identifiers (MID). Each PCR reaction consisted of 1 \selectlanguage{greek}µ\selectlanguage{english}l DNA template and
24 \selectlanguage{greek}µ\selectlanguage{english}l master mix: 15.7 \selectlanguage{greek}µ\selectlanguage{english}l dH\textsubscript{2}O, 2.5 \selectlanguage{greek}µ\selectlanguage{english}l Gold Buffer, 2.5
\selectlanguage{greek}µ\selectlanguage{english}l Gold MgCl\textsubscript{2}, 1 \selectlanguage{greek}µ\selectlanguage{english}l 20mg/ml BSA, 0.2 \selectlanguage{greek}µ\selectlanguage{english}l dNTPs, 0.1 \selectlanguage{greek}µ\selectlanguage{english}l
AmpliTaq Gold, 1.5 \selectlanguage{greek}µ\selectlanguage{english}l 10 \selectlanguage{greek}µ\selectlanguage{english}M forward primer and 1.5 \selectlanguage{greek}µ\selectlanguage{english}l 10 \selectlanguage{greek}µ\selectlanguage{english}M reverse
primer. We run PCR reactions for ITS2 with initial denaturation at 95\selectlanguage{ngerman}°C
for 5 min, followed by 32 cycles of denaturation at 95°C for 30 sec,
primer annealing at 55°C for 30 sec and elongation at 72°C for 1 min. We
added a final elongation step at 72°C for 7 min, before cooling down to
4°C. The protocol for 18S was slightly different: PCR reactions were run
with initial denaturation at 98°C for 7 min, followed by 32 cycles of
denaturation at 98°C for 30 sec, primer annealing at 53°C for 30 sec and
elongation at 72°C for 45 sec. A final elongation step was included at
72°C for 10 min, before cooling down to 4°C. We controlled each PCR
product for positive amplification with gel electrophoresis using a 2\%
agarose gel, before individual clean-up and purification of the
amplicons with ZR-96 DNA Clean \& Concentrator-5 kit (Zymo Research,
California, USA). DNA concentrations for each sample were measured with
the Qubit 2 fluorometer dsDNA BR Assay Kit (Thermo Fisher Scientific,
Waltham, MA, USA) and pooled to equimolar concentration into four pools.
Each pool was cleaned and concentrated with DNA Clean \& Concentrator-5
(Zymo Research, California, USA). The four libraries (two for each
targeted DNA region) were sequenced by Fasteris SA (Switzerland) in two
runs using Illumina MiSeq with a 250 bp paired-end (PE) with V3
chemistry. A ligation protocol, specifically designed to minimize
tag-jumping, was used to ligate the amplicons with the Miseq flow-cell
adapters.

\par\null

Bioinformatics

We performed all bioinformatics analyses on the Abel high-performance
computer cluster at the University of Oslo. The raw PE reads were
demultiplexed separately with simultaneous tags and primers removal
using CUTADAPT (Martin, 2011). No miss-match with primer and MID tags
were allowed. Further processing of the data was performed with the
DADA2 pipeline (Callahan et al., 2016) using the statistical
environment\emph{R} version 3.6.0 (R Core Team, 2014). We generated
sequence quality profiles and used this information to decide parameters
for filtering low quality reads. A truncate length of 200 bp was set for
the 18S sequence reads (hereafter called reads). Due to natural length
variability of the ITS2 marker, no truncation was imposed. In both
datasets, maximum expected error was set to 2.5. We then independently
corrected forward and reverse reads using the DADA2 machine-learning
algorithm that estimates correction parameters from the data itself. The
default settings were used for 18S, while for ITS2 the band size was set
to 32 due to the higher INDEL rate of this marker. The corrected forward
and reverse reads were merged. We used a minimum overlap of 50 and 8
nucleotides when merging ITS2 and 18S reads, respectively. Chimeras were
checked and removed with a denovo approach using the DADA2 bimera
algorithm with default settings. We then constructed an amplicon
sequence variant (ASV) table with the chimera-free reads. For the ITS2
data, we used ITSx (Bengtsson-Palme et al., 2013) for extracting the
ITS2 region and filtering of non-fungal reads. Due to widespread
intraspecific variation in ITS2, an additional clustering step with 97\%
similarity was performed using VSEARCH (Rognes, Flouri, Nichols, Quince,
\& Mahe, 2016) and singleton sequences were discarded. Finally, to
adjust for over-splitting of Operational Taxonomic Units (OTUs), we
performed post clustering curation using the LULU algorithm (Froslev et
al., 2017) for both datasets.

For taxonomic annotation, the ITS2 dataset was query searched using
VSEARCH global against UNITE v8.0 (Nilsson, Larsson, et al., 2019),
whereas the 18S dataset was annotated using the Eukref/PR2 database (del
Campo et al., 2018). We used FunGuild (Nguyen et al., 2016) for
functional annotation of the ITS2 dataset, where we annotated the fungi
into the following functional groups: Saprotrophs, ectomycorrhizal fungi
(EcM), lichens, yeasts, pathotrophs, root-associated ascomycetes
(including DSEs and ErM fungi) and other ascomycetes. Notably, these
groups should be regarded tentative (i.e. as hypotheses), since we still
lack solid evidence for most fungi's ecologies and functions.

In the ITS2 dataset, we removed one sample due to low read number (286
reads). Number of reads per sample was rarefied to the lowest sample
read number (6317 reads). The final ITS2 dataset consisted of 98 samples
and 3090 OTUs. In the 18S dataset, we removed six samples due to low
read number (\textless{} 9). Plant OTUs were removed, and number of
reads per sample was then rarefied to lowest sample read number (2486
reads). The final 18S dataset consisted of 93 samples and 4595 OTUs.

\par\null

Statistics

We performed all statistical analyses in R (R Core Team,
2014).\emph{ggplot2} was used for graphical plotting (Wickham \& Chang,
2016); the \emph{vegan} package for community composition analyses,
i.e., rarefaction, OTU count data transformation, ordinations, variation
partitioning, environmental correlations with ordination analyses
(Oksanen et al., 2007) and the \emph{nlme} (Pinheiro et al., 2017) as
well as \emph{MuMIn} (Barton, 2019) packages for modelling correlations.

To investigate structure in community composition, we constructed
ordination diagrams using global non-metric multidimensional scaling
(GNMDS) with the ``metaMDS'' function on the rarefied OTU matrix, using
settings as recommended by Liu \emph{et al.} (Liu et al., 2008). GNMDS
and detrended correspondence analysis (DCA), using default settings,
were run in parallel to assess the correlation between GNMDS and DCA
axes. As shown by Son and Halvorsen (van Son \& Halvorsen, 2014), a good
correspondence between these methods can be interpreted as robust
ecological gradients. We fitted the environmental variables to the
ordinations with the ``envfit'' function, and plot isolines were fitted
with the ``ordisurf'' function. In order to investigate the proportion
of variation explained by different environmental variables, variation
partitioning of the ITS2 and 18S data was performed using canonical
correspondence analysis (CCA) function in the \emph{vegan} package. We
ran a forward model selection until no more significant variables could
be added. Correlations between soil C and ergosterol and DSEs were
modelled as linear mixed models with sites as a random factor using the
``lme'' function, and r squared values extracted from the models using
the ``r.squaredGLMM'' function.

\textbf{Results}

Distribution of taxonomic and functional groups across the ecotone

In the 18S dataset, Fungi was the overall richest group (1083 OTUs;
85,101 reads), followed by Metazoa, which included the highest number of
reads (1015 OTUs; 94,166 reads). These two groups dominated the soil
with about 80\% of all reads at kingdom level. We observed a slight
increase in the proportion of Metazoa from the forest to the alpine
heath vegetation (Fig 2a). At phylum level, Nematoda (422 OTUs; 29,096
reads) and Filosa-Sarcomonadea (411 OTUs; 13,278 reads) included highest
richness, while Annelida (52 OTUs; 38,922 reads) possessed the highest
number of reads. We observed no clear trends for the distribution of
Annelida and Nematoda at phylum level, but the amount of reads belonging
to Arthropoda and Rotifera increased slightly towards the low-alpine
vegetation (Fig. 2b). At both kingdom and phylum level, none of the
protists, represented by Ciliophora, Cercozoa, Apicomplexa, Lobosa and
Conosa, showed any clear distribution patterns across the ecotone (Fig.
2a).

Only assessing fungi in the 18S data, the majority of OTUs belonged to
the phylum Cryptomycota (318 OTUs; 5804 reads), followed by
Basidiomycota (226 OTUs; 16,810 reads). However, the majority of reads
belonged to Mucoromycota (47 OTUs; 30,137 reads) and Ascomycota (181
OTUs; 26,096 reads), together making up about 60\% of the fungal reads
(Fig. 2b and c). We observed that the relative abundance of Ascomycota
increased from the mountain birch forest to the low-alpine vegetation,
while we saw an opposite trend for Mucoromycota (Fig. 2b). The relative
abundance of Archaeorhizomycetes, which belongs to Ascomycota, increased
distinctly towards the low-alpine vegetation, with an opposite trend for
Mucoromycetes (Fig. 2c). We observed no clear trend for the distribution
of Agaricomycetes (Basidiomycota).

The ITS2 dataset (Fig. 2d) was dominated by the phyla Ascomycota (1522
OTUs, 380 072 reads) and Basidiomycota (903 OTUs, 200 356 reads) and
showed a different pattern in relative abundance, compared to the 18S
data. In the ITS2 data, the proportion of Basidiomycetes (especially
Agaricomycetes) decreased along the ecotone towards the low alpine
vegetation, while Ascomycetes (especially Leotiomycetes and
Eurotiomycetes) showed an opposite trend (Fig. 2d). Leotiomycetes made
up the highest proportion of Ascomycetes at class level. In contrast to
the 18S data, Archaeorhizomycetes and Mortierellomycetes/Mucoromycetes
only made up a small proportion of the ITS2 reads.

For the fungal ITS2 dataset, we assigned OTUs to functional guilds (Fig.
3a), which revealed that saprotrophs made up the largest group (815
OTUs, 130 986 reads), followed by unassigned ascomycetes (626 OTUs, 148
884 reads), EcM fungi (321 OTUs, 137 428 reads), yeasts (149 OTUs, 43
932 reads), pathotrophs (135 OTUs, 7612 reads), root-associated
ascomycetes (115 OTUs, 94 454 reads) and lichens (108 OTUs, 20 586
reads). About half of the 40 most common OTUs were root-associated
ascomycetes and most of the other were EcM fungi. The abundance of EcM
and saprotrophs decreased towards the low-alpine vegetation, while
root-associated ascomycetes showed an opposite trend of being far more
abundant in the low-alpine vegetation compared to the subalpine mountain
birch forest (Fig. 3a). Furthermore, we observed a strong correlation
between the percent C content in the dry mass of the samples and
ergosterol (R\textsuperscript{2}=0.73), and percent C content and read
abundance of root-associated ascomycetes (R\textsuperscript{2}=0.45;
Fig. 4) across the ecotone.

Drivers of community composition

GNMDS analyses on both datasets demonstrated a clear gradient across the
ecotone in community composition of all micro-eukaryotes (18S dataset,
Fig. 5a) and fungi alone (ITS2 dataset, Fig. 5b). In both diagrams, the
first ordination axis (GNMDS1) identified the ecotone stretching from
subalpine mountain birch forest to low-alpine vegetation as the main
gradient driving the compositional changes in soil communities. Soil
edaphic factors, together with the plant groups, were largely structured
along the same main gradient as the soil biota (Fig. 3b and c, Fig. 5a
and b). Percent C in dry mass soil and ergosterol content increased from
the birch forest to the low-alpine vegetation, while pH and P content
decreased (Fig. 3b). We observed no systematic trend for \% total N
content in the soil samples. The amount of ErM forming plants increased
above the forest line, while AM plants decreased (Fig. 3c). By
definition, the EcM forming \emph{Betula pubescens} was only present
below the forest line, while there was a slight increase in other
understory EcM plants towards the low-alpine vegetation (Fig. 3c). In
both datasets, the second axes (GNMDS2) were structured by the
site-specific environmental variables (Fig. 5a, b), in addition to soil
N in the ITS2 diagram (Fig. 5b).

The CCA analyses demonstrated that site-specific factors, which account
for regional (between-site) variability, explained 4.69\% of the
compositional variation in the 18S dataset, and 13.68\% in the ITS2
dataset (Table 1). Plot-specific factors, which also account for
variability within the individual gradients, explained 11.27\% in the
18S dataset and 13.44\% in the ITS2 dataset, respectively. Thus, site-
and plot-specific variables were about equally important in explaining
variation in community composition in the ITS2 dataset. Interaction
effects, both explained at site and plot level, were 3.3 (18S) and 4.2\%
(ITS2). Total variation explained, as a fraction of total variation, was
25.96\% in the 18S dataset, and 27.12\% in the ITS2 dataset.

Species score GNMDS ordination of ITS2 OTUs (Fig. 5c) revealed the same
trends as outlined above, where OTUs distributed along the ecotone with
relatively more root-associated ascomycetes and lichen-forming fungi in
the low-alpine vegetation and relatively more EcM in the birch forest.
The largest proportion of root-associated ascomycetes was associated
with the heath part of the low-alpine vegetation.

\textbf{Discussion}

We found that the ecotone, stretching from subalpine mountain birch
forest to treeless low-alpine vegetation, was the primary structuring
gradient shaping the soil biota. Both the fungal communities, as well as
the overall micro-eukaryotic communities, were structured primarily
along the ecotone. Variation in community composition between sites was
largely accounted for by regional variation in climate, as well as other
site-specific factors, such as slope, aspect and bedrock, the latter
affecting nutrient content.

We observed that the belowground community composition largely reflected
the aboveground shift in vegetation. However, for fungal communities,
the 18S and ITS2 markers showed substantial differences in abundance and
distribution of fungal groups across the ecotone. Most noteworthy,
Archaeorhizomycetes (Ascomycota) and Mucoromycota dominated in the 18S
dataset, and Chytridiomycota and Cryptomycota were also abundant across
the entire ecotone. In contrast, these groups were poorly represented in
the ITS2 dataset. On the contrary, the subphylum Pezizomycotina,
represented mainly by Leotiomycetes, Eurotiomycetes, Lecanoromycetes and
Dothideomycetes, was largely absent in the 18S dataset, but made up a
large proportion of the ITS2 dataset. We suggest that these contrasting
results can largely be explained by primer biases (Nilsson, Anslan, et
al., 2019; Rosling et al., 2011; Tedersoo et al., 2015). The employed
ITS primers have been shown to discriminate against e.g.
Archaeorhizomycetes (Ihrmark et al., 2012; Rosling et al., 2011;
Tedersoo et al., 2015) and they also seemingly amplify Mucoromycota,
Chytridiomycota and Cryptomycota poorly. The 18S primers we used in this
study, was expected to amplify all eukaryotes (Hadziavdic et al., 2014)
and provide a more general overview of the fungal community composition
compared to the ITS2 dataset. However, the low amount of Pezizomycotina
in the 18S data indicated that primer biases may be present. Indeed,
after a closer inspection, a mismatch in the reverse primer was observed
in numerous Pezizomycotina lineages. Irrespective of the biases, there
was a general trend of more Ascomycota above the forest line, most
notably Archaeorhizomycota (18S data), and Leotimycetes and
Eurotiomycetes (ITS2 data).

Several groups of Ascomycota in the ITS2 data represented plant
root-associated fungi. For example, the overall most common OTU showed
high sequence similarity to \emph{Pezoloma ericae} , a common ErM fungus
(Smith \& Read, 2008) . In the species ordination plot, most of the
root-associated ascomycetes, representing ErM and DSE fungi, clustered
in the alpine heath along the ecotone, where ericaceous plants,
especially \emph{Empetrum nigrum} , dominated. The gradual increase of
Leotiomycetes and Eurotiomycetes towards the low alpine vegetation (ITS2
data) is in line with the pattern showed in Tedersoo \emph{et
al.}(Tedersoo et al., 2014), where these groups had highest relative
abundances in arctic tundra. ErM and DSE fungi living under stressful
conditions typically have melanized hyphae. Melanin is a recalcitrant
polymer of the hyphal cell wall that may protect fungi against
desiccation (Fernandez \& Koide, 2013). Melanin induces slower
decomposition and the retention time of highly melanized fungi in the
soil may thus be longer, in comparison with non-melanized fungi
(Fernandez, Heckman, Kolka, \& Kennedy, 2019; Fernandez \& Koide, 2014).
Fungal necromass (i.e. dead biomass) is an important part of boreal soil
carbon pools (Clemmensen et al., 2015), and the longer retention time of
melanised mycelia may explain the strong positive correlation between
root-associated ascomycetes and percent C content in this study. We also
observed an increase of Archaeorhizomycetes towards the alpine heath.
Little is known about the ecology of the Archaeorhizomycetes (Rosling et
al., 2011), but is has been hypothesised that some Archaeorhizomycetes
may be root-associated mutualists (Menkis, Urbina, James, \& Rosling,
2014). The high abundance of Archaeorhizomycetes in the low alpine
vegetation may indicate that they are linked to ericaceous plants, along
with ErM fungi and DSEs. Other studies confirm that Archaeorhizomycetes
are relatively abundant in stressful environments (Sterkenburg, Bahr,
Durling, Clemmensen, \& Lindahl, 2015), e.g. in high altitude and
latitude ecosystems (Pinto-Figueroa et al., 2019; Schadt, Martin,
Lipson, \& Schmidt, 2003).

In parallel to the root-associated ascomycetes, we observed that percent
soil C and ergosterol content had a strong positive correlation towards
the low-alpine vegetation, in line with our hypotheses 3.
Clemmensen\emph{et al.} (Clemmensen et al., 2015) showed a similar
correlation between ergosterol and total soil C stock, and argued that
this relationship is due to slower mycelial turnover rate leading to a
long-term build up and increased soil C sequestration (Clemmensen et
al., 2013; Clemmensen et al., 2015; Hagenbo et al., 2017; Kyaschenko,
Clemmensen, Karltun, \& Lindahl, 2017). These processes may also account
for the high C and ergosterol content in the low-alpine vegetation.
Percent C content correlated strongly with the community composition in
the ordination diagrams, but with respect to decomposition and cycling
processes, C content is not regarded as a driver, rather a consequence
of the fungal community composition (Clemmensen et al., 2015). However,
it must be emphasised that in this study we only have data on percent C
content and not overall C stock measurements. Hence, to conclude about
overall C stock dynamics, additional data must be collected.

In accordance with hypothesis 2, the distribution of EcM fungi across
the ecotone reflected their host plants distributions, with higher
abundances of EcM fungi below the mountain birch forest line. Our
observations are in line with what observed by Va\selectlanguage{polish}š\selectlanguage{english}utov\selectlanguage{ngerman}á \emph{et al} .
(Vasutova, Edwards-Jonasova, Baldrian, Cermak, \& Cudlin, 2017), where
EcM fungi declined with altitude. Many EcM fungi are associated with
relatively fast soil organic matter turnover (Bodeker et al., 2014;
Clemmensen et al., 2013; Lindahl \& Tunlid, 2015) and may, together with
the saprotrophic fungi, account for the lower amount of soil C observed
below the forest line in our study.

In general, we observed a higher proportion of saprotrophic fungi in the
alpine birch forests compared to the heath, which may account for the
lower fraction of soil C observed here, due to faster decomposition.
Based on the 18S dataset, Mucoromycetes dominated the fungal community
below the forest line. These fungi are foremost known as saprotrophs,
and there is likely a higher abundance of available litter in the forest
compared to the alpine heath. However, it has also been observed that
Mucoromycotina, as fine root endophytes, are important symbionts with
cryptogams (Field et al., 2015; Hoysted et al., 2019), receiving plant C
in exchange of soil N. Hence, Mucoromycetes may play different
functional roles, which may explain the high relative abundance of
Mucoromycota across the entire ecotone.

Although our study was mainly focused on fungi, we found that,Metazoa
was about equally abundant as fungi in the soil measured as sequence
reads. As pinpointed earlier, some fungi Ascomycota might have been
poorly amplified by the 18S marker, so it remains unclear whether the
proportions reflect an unbiased picture of the belowground biota. The
two most abundant Metazoa groups, Annelida and Nematoda, were common
across the entire ecotone, whereas Arthropoda and Rotifera were
relatively more abundant above the forest line. Other studies have shown
that annelids and nematodes typically are more abundant at high
latitudes and altitudes (Phillips et al., 2019; Procter, 1990). This is
in line with the result of this study, where annelids and nematodes are
the dominant groups. Rotifera has also been shown to be abundant in
soils at high latitudes and altitudes, and are often present in mosses
and lichens (Biela\selectlanguage{polish}ń\selectlanguage{english}ska-Grajner, Ejsmont-Karabin, \& Yakovenko, 2011;
Fontaneto \& Ricci, 2006), which corresponds well with their relatively
higher abundance above the forest line. Studies have shown that soil
moisture strongly influence protist communities (Bates et al., 2013; de
Araujo et al., 2018; Stefana, Cornelia, Jorg, \& Michael, 2014), while
others regard soil pH as more important (Heger, Derungs, Theurillat, \&
Mitchell, 2016; Shen et al., 2014). Heger \emph{et al.} (Heger et al.,
2016) also showed a correlation between protist communities and
altitude. In general, Cercozoa is commonly observed in various soil
types, whereas Ciliophora and Apicomplexa have been shown to be
relatively more abundant in humid soils (Bates et al., 2013). Due to the
parasitic lifestyle of Apicomplexa, their distribution is likely largely
dependent on the presence of host species (Arthropodes) (Mahe et al.,
2017; Seppey et al., 2020). However, at kingdom and phylum level, none
of the protists showed any clear distribution pattern across the
ecotone.

The ecotone, stretching from subalpine mountain birch forest to treeless
low-alpine vegetation, represents a corresponding shift in belowground
fungal communities, from soils dominated by EcM and saprotrophic fungi
to soils dominated by root-associated ascomycetes, respectively. This
study has shown there is a parallel shift in belowground ergosterol
content across the ecotone, which is strongly associated with the
abundance of root-associated ascomycetes in the low-alpine vegetation.
Our findings corroborate the view that despite higher aboveground
productivity, shrubification and raise in forest line in northern
ecosystems may lead to soil C loss because of higher soil respiration
and C cycling (Parker et al., 2015; Sjogersten \& Wookey, 2009; Sorensen
et al., 2018), through corresponding shifts in fungal communities.
However, C stock data is needed to conclude on this matter. On a
technical note, our study also underlines that in future community
studies of fungi, a wider set of DNA-markers and primers should be
considered to obtain a comprehensive picture of soil fungal communities.

\textbf{Acknowledgements}

We acknowledge the University of Oslo for financial support and Peter
Horvath at the Natural History Museum (UiO) for providing metadata
through access to high-resolution environmental GIS-layers.

\textbf{References}

Averill, C., Turner, B. L., \& Finzi, A. C. (2014). Mycorrhiza-mediated
competition between plants and decomposers drives soil carbon
storage.\emph{Nature, 505} (7484), 543-+. doi:10.1038/nature12901

Barton, K. (2019). Package `MuMIn'. R package version.

Bates, S. T., Clemente, J. C., Flores, G. E., Walters, W. A., Parfrey,
L. W., Knight, R., \& Fierer, N. (2013). Global biogeography of highly
diverse protistan communities in soil. \emph{Isme Journal, 7} (3),
652-659. doi:10.1038/ismej.2012.147

Beckage, B., Osborne, B., Gavin, D. G., Pucko, C., Siccama, T., \&
Perkins, T. (2008). A rapid upward shift of a forest ecotone during 40
years of warming in the Green Mountains of Vermont. \emph{Proceedings of
the National Academy of Sciences of the United States of America, 105}
(11), 4197-4202. doi:10.1073/pnas.0708921105

Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z.,
Godhe, A., . . . Nilsson, R. H. (2013). Improved software detection and
extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and
other eukaryotes for analysis of environmental sequencing
data.\emph{Methods in Ecology and Evolution, 4} (10), 914-919.
doi:10.1111/2041-210x.12073

Biela\selectlanguage{polish}ń\selectlanguage{english}ska-Grajner, I., Ejsmont-Karabin, J., \& Yakovenko, N. (2011).
Bdelloidea (Rotifera) from the Tatra National Park, with some species
newly found in Poland. \emph{Fragmenta Faunistica, 52} (2), 103-111.

Bodeker, I. T. M., Clemmensen, K. E., de Boer, W., Martin, F., Olson,
A., \& Lindahl, B. D. (2014). Ectomycorrhizal Cortinarius species
participate in enzymatic oxidation of humus in northern forest
ecosystems. \emph{New Phytologist, 203} (1), 245-256.
doi:10.1111/nph.12791

Bryn, A., \& Potthoff, K. (2018). Elevational treeline and forest line
dynamics in Norwegian mountain areas - a review. \emph{Landscape
Ecology, 33} (8), 1225-1245. doi:10.1007/s10980-018-0670-8

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A.
J. A., \& Holmes, S. P. (2016). DADA2: High-resolution sample inference
from Illumina amplicon data. \emph{Nature Methods, 13} (7), 581-+.
doi:10.1038/Nmeth.3869

Chen, I. C., Hill, J. K., Ohlemuller, R., Roy, D. B., \& Thomas, C. D.
(2011). Rapid Range Shifts of Species Associated with High Levels of
Climate Warming. \emph{Science, 333} (6045), 1024-1026.
doi:10.1126/science.1206432

Clemmensen, K. E., Bahr, A., Ovaskainen, O., Dahlberg, A., Ekblad, A.,
Wallander, H., . . . Lindahl, B. D. (2013). Roots and Associated Fungi
Drive Long-Term Carbon Sequestration in Boreal Forest. \emph{Science,
339} (6127), 1615-1618. doi:10.1126/science.1231923

Clemmensen, K. E., Finlay, R. D., Dahlberg, A., Stenlid, J., Wardle, D.
A., \& Lindahl, B. D. (2015). Carbon sequestration is related to
mycorrhizal fungal community shifts during long-term succession in
boreal forests. \emph{New Phytologist, 205} (4), 1525-1536.
doi:10.1111/nph.13208

de Araujo, A. S. F., Mendes, L. W., Lemos, L. N., Antunes, J. E. L.,
Beserra, J. E. A., de Lyra, M. D. C. P., . . . Geisen, S. (2018).
Protist species richness and soil microbiome complexity increase towards
climax vegetation in the Brazilian Cerrado. \emph{Communications
Biology, 1} . doi:ARTN 135

10.1038/s42003-018-0129-0

de Wit, H. A., Bryn, A., Hofgaard, A., Karstensen, J., Kvalevag, M. M.,
\& Peters, G. P. (2014). Climate warming feedback from mountain birch
forest expansion: reduced albedo dominates carbon uptake. \emph{Global
Change Biology, 20} (7), 2344-2355. doi:10.1111/gcb.12483

del Campo, J., Kolisko, M., Boscaro, V., Santoferrara, L. F., Nenarokov,
S., Massana, R., . . . Parfrey, L. W. (2018). EukRef: Phylogenetic
curation of ribosomal RNA to enhance understanding of eukaryotic
diversity and distribution. \emph{Plos Biology, 16} (9). doi:ARTN
e2005849

10.1371/journal.pbio.2005849

Fernandez, C. W., Heckman, K., Kolka, R., \& Kennedy, P. G. (2019).
Melanin mitigates the accelerated decay of mycorrhizal necromass with
peatland warming. \emph{Ecology Letters, 22} (3), 498-505.
doi:10.1111/ele.13209

Fernandez, C. W., \& Koide, R. T. (2013). The function of melanin in the
ectomycorrhizal fungus Cenococcum geophilum under water
stress.\emph{Fungal Ecology, 6} (6), 479-486.
doi:10.1016/j.funeco.2013.08.004

Fernandez, C. W., \& Koide, R. T. (2014). Initial melanin and nitrogen
concentrations control the decomposition of ectomycorrhizal fungal
litter. \emph{Soil Biology \& Biochemistry, 77} , 150-157.
doi:10.1016/j.soilbio.2014.06.026

Field, K. J., Rimington, W. R., Bidartondo, M. I., Allinson, K. E.,
Beerling, D. J., Cameron, D. D., . . . Pressel, S. (2015). First
evidence of mutualism between ancient plant lineages (Haplomitriopsida
liverworts) and Mucoromycotina fungi and its response to simulated
Palaeozoic changes in atmospheric CO2. \emph{New Phytologist, 205} (2),
743-756. doi:10.1111/nph.13024

Fontaneto, D., \& Ricci, C. (2006). Spatial gradients in species
diversity of microscopic animals: the case of bdelloid rotifers at high
altitude. \emph{Journal of Biogeography, 33} (7), 1305-1313.
doi:10.1111/j.1365-2699.2006.01502.x

Frey, S. D. (2019). Mycorrhizal Fungi as Mediators of Soil Organic
Matter Dynamics. \emph{Annual Review of Ecology, Evolution, and
Systematics, Vol 50, 50} , 237-259.
doi:10.1146/annurev-ecolsys-110617-062331

Froslev, T. G., Kjoller, R., Bruun, H. H., Ejrnaes, R., Brunbjerg, A.
K., Pietroni, C., \& Hansen, A. J. (2017). Algorithm for post-clustering
curation of DNA amplicon data yields reliable biodiversity
estimates.\emph{Nature Communications, 8} . doi:ARTN 1188

10.1038/s41467-017-01312-x

Geisen, S., Mitchell, E. A. D., Adl, S., Bonkowski, M., Dunthorn, M.,
Ekelund, F., . . . Lara, E. (2018). Soil protists: a fertile frontier in
soil biology research. \emph{Fems Microbiology Reviews, 42} (3),
293-323. doi:ARTN fuy006

10.1093/femsre/fuy006

Hadziavdic, K., Lekang, K., Lanzen, A., Jonassen, I., Thompson, E. M.,
\& Troedsson, C. (2014). Characterization of the 18S rRNA Gene for
Designing Universal Eukaryote Specific Primers. \emph{Plos One, 9} (2).
doi:ARTN e87624

10.1371/journal.pone.0087624

Hagenbo, A., Clemmensen, K. E., Finlay, R. D., Kyaschenko, J., Lindahl,
B. D., Fransson, P., \& Ekblad, A. (2017). Changes in turnover rather
than production regulate biomass of ectomycorrhizal fungal mycelium
across a Pinus sylvestris chronosequence. \emph{New Phytologist, 214}
(1), 424-431. doi:10.1111/nph.14379

Harsch, M. A., Hulme, P. E., McGlone, M. S., \& Duncan, R. P. (2009).
Are treelines advancing? A global meta-analysis of treeline response to
climate warming. \emph{Ecology Letters, 12} (10), 1040-1049.
doi:10.1111/j.1461-0248.2009.01355.x

Heger, T. J., Derungs, N., Theurillat, J. P., \& Mitchell, E. A. D.
(2016). Testate Amoebae Like It Hot: Species Richness Decreases Along a
Subalpine-Alpine Altitudinal Gradient in Both Natural Calluna vulgaris
Litter and Transplanted Minuartia sedoides Cushions. \emph{Microbial
Ecology, 71} (3), 725-734. doi:10.1007/s00248-015-0687-3

Hill, P. W., Broughton, R., Bougoure, J., Havelange, W., Newsham, K. K.,
Grant, H., . . . Jones, D. L. (2019). Angiosperm symbioses with
non-mycorrhizal fungal partners enhance N acquisition from ancient
organic matter in a warming maritime Antarctic. \emph{Ecology Letters,
22} (12), 2111-2119. doi:10.1111/ele.13399

Horvath, P., Halvorsen, R., Stordal, F., Tallaksen, L. M., Tang, H., \&
Bryn, A. (2019). Distribution modelling of vegetation types based on
area frame survey data. \emph{Applied Vegetation Science, 22} (4),
547-560. doi:10.1111/avsc.12451

Hoysted, G. A., Jacob, A. S., Kowal, J., Giesemann, P., Bidartondo, M.
I., Duckett, J. G., . . . Field, K. J. (2019). Mucoromycotina Fine Root
Endophyte Fungi Form Nutritional Mutualisms with Vascular
Plants.\emph{Plant Physiology, 181} (2), 565-577.
doi:10.1104/pp.19.00729

Ihrmark, K., Bodeker, I. T. M., Cruz-Martinez, K., Friberg, H.,
Kubartova, A., Schenck, J., . . . Lindahl, B. D. (2012). New primers to
amplify the fungal ITS2 region - evaluation by 454-sequencing of
artificial and natural communities. \emph{Fems Microbiology Ecology, 82}
(3), 666-677. doi:10.1111/j.1574-6941.2012.01437.x

Koide, R. T., Fernandez, C., \& Malcolm, G. (2014). Determining place
and process: functional traits of ectomycorrhizal fungi that affect both
community structure and ecosystem function. \emph{New Phytologist, 201}
(2), 433-439. doi:10.1111/nph.12538

Kyaschenko, J., Clemmensen, K. E., Karltun, E., \& Lindahl, B. D.
(2017). Below-ground organic matter accumulation along a boreal forest
fertility gradient relates to guild interaction within fungal
communities. \emph{Ecology Letters, 20} (12), 1546-1555.
doi:10.1111/ele.12862

K\selectlanguage{ngerman}örner, C. (2012). \emph{Alpine treelines: Functional ecology of the
global high elevation tree limits} : Springer Science \& Business Media.

Lindahl, B. D., \& Tunlid, A. (2015). Ectomycorrhizal fungi - potential
organic matter decomposers, yet not saprotrophs. \emph{New Phytologist,
205} (4), 1443-1447. doi:10.1111/nph.13201

Liu, H., Økland, T., Halvorsen, R., Gao, J., Liu, Q., Eilertsen, O., \&
Bratli, H. (2008). Gradients analyses of forests ground vegetation and
its relationships to environmental variables in five subtropical forest
areas, S and SW China. \emph{Sommerfeltia, 32} (1), 3-196.

Mahe, F., de Vargas, C., Bass, D., Czech, L., Stamatakis, A., Lara, E.,
. . . Dunthorn, M. (2017). Parasites dominate hyperdiverse soil protist
communities in Neotropical rainforests. \emph{Nature Ecology \&
Evolution, 1} (4). doi:ARTN 0091

10.1038/s41559-017-00911

Martin, M. (2011). Cutadapt removes adapter sequences from
high-throughput sequencing reads. \emph{EMBnet Journal, 17} (1).

Menkis, A., Urbina, H., James, T. Y., \& Rosling, A. (2014).
Archaeorhizomyces borealis sp nov and a sequence-based classification of
related soil fungal species. \emph{Fungal Biology, 118} (12), 943-955.
doi:10.1016/j.funbio.2014.08.005

Newsham, K. K., Upson, R., \& Read, D. J. (2009). Mycorrhizas and dark
septate root endophytes in polar regions. \emph{Fungal Ecology, 2} ,
10-20. doi:10.1016/j.funeco.2008.10.005

Nguyen, N. H., Song, Z. W., Bates, S. T., Branco, S., Tedersoo, L.,
Menke, J., . . . Kennedy, P. G. (2016). FUNGuild: An open annotation
tool for parsing fungal community datasets by ecological
guild.\emph{Fungal Ecology, 20} , 241-248.
doi:10.1016/j.funeco.2015.06.006

Nilsson, R. H., Anslan, S., Bahram, M., Wurzbacher, C., Baldrian, P., \&
Tedersoo, L. (2019). Mycobiome diversity: high-throughput sequencing and
identification of fungi. \emph{Nature Reviews Microbiology, 17} (2),
95-109. doi:10.1038/s41579-018-0116-y

Nilsson, R. H., Larsson, K. H., Taylor, A. F. S., Bengtsson-Palme, J.,
Jeppesen, T. S., Schigel, D., . . . Abarenkov, K. (2019). The UNITE
database for molecular identification of fungi: handling dark taxa and
parallel taxonomic classifications. \emph{Nucleic Acids Research, 47}
(D1), D259-D264. doi:10.1093/nar/gky1022

Oksanen, J., Kindt, R., Legendre, P., O'Hara, B., Stevens, M. H. H.,
Oksanen, M. J., \& Suggests, M. A. S. S. (2007). The vegan package.
Community ecology package.

Olsrud, M., Michelsen, A., \& Wallander, H. (2007). Ergosterol content
in ericaceous hair roots correlates with dark septate endophytes but not
with ericold mycorrhizal colonization. \emph{Soil Biology \&
Biochemistry, 39} (5), 1218-1221. doi:10.1016/j.soilbio.2006.11.018

Orwin, K. H., Kirschbaum, M. U. F., St John, M. G., \& Dickie, I. A.
(2011). Organic nutrient uptake by mycorrhizal fungi enhances ecosystem
carbon storage: a model-based assessment. \emph{Ecology Letters, 14}
(5), 493-502. doi:10.1111/j.1461-0248.2011.01611.x

Parker, T. C., Subke, J. A., \& Wookey, P. A. (2015). Rapid carbon
turnover beneath shrub and tree vegetation is associated with low soil
carbon stocks at a subarctic treeline. \emph{Global Change Biology, 21}
(5), 2070-2081. doi:10.1111/gcb.12793

Parmesan, C. (2006). Ecological and evolutionary responses to recent
climate change. \emph{Annual Review of Ecology Evolution and
Systematics, 37} , 637-669. doi:10.1146/annurev.ecolsys.37.091305.110100

Pecl, G. T., Araujo, M. B., Bell, J. D., Blanchard, J., Bonebrake, T.
C., Chen, I. C., . . . Williams, S. E. (2017). Biodiversity
redistribution under climate change: Impacts on ecosystems and human
well-being. \emph{Science, 355} (6332). doi:ARTN eaai9214

10.1126/science.aai9214

Pepin, N., Bradley, R. S., Diaz, H. F., Baraer, M., Caceres, E. B.,
Forsythe, N., . . . Grp, M. R. I. E. W. (2015). Elevation-dependent
warming in mountain regions of the world. \emph{Nature Climate Change,
5} (5), 424-430. doi:10.1038/Nclimate2563

Phillips, H. R. P., Guerra, C. A., Bartz, M. L. C., Briones, M. J. I.,
Brown, G., Crowther, T. W., . . . Eisenhauer, N. (2019). Global
distribution of earthworm diversity. \emph{Science, 366} (6464), 480-+.
doi:10.1126/science.aax4851

Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., Heisterkamp, S., Van
Willigen, B., \& Maintainer, R. (2017). Package `nlme'. Linear and
Nonlinear Mixed Effects Models (Version 3).

Pinto-Figueroa, E. A., Seddon, E., Yashiro, E., Buri, A.,
Niculita-Hirzel, H., van der Meer, J. R., \& Guisan, A. (2019).
Archaeorhizomycetes Spatial Distribution in Soils Along Wide Elevational
and Environmental Gradients Reveal Co-abundance Patterns With Other
Fungal Saprobes and Potential Weathering Capacities. \emph{Frontiers in
Microbiology, 10} . doi:ARTN 656

10.3389/fmicb.2019.00656

Procter, D. L. C. (1990). Global Overview of the Functional Roles of
Soil-Living Nematodes in Terrestrial Communities and
Ecosystems.\emph{Journal of Nematology, 22} (1), 1-7. Retrieved from
\textless{}Go to ISI\textgreater{}://WOS:A1990CL83300001

R Core Team. (2014). \emph{R: A language and environment for statistical
computing} . Vienna, Austria: R Foundation for Statistical Computing.

Ransedokken, Y., Asplund, J., Ohlson, M., \& Nybakken, L. (2019).
Vertical distribution of soil carbon in boreal forest under European
beech and Norway spruce. \emph{European Journal of Forest Research, 138}
(2), 353-361. doi:10.1007/s10342-019-01176-4

Rognes, T., Flouri, T., Nichols, B., Quince, C., \& Mahe, F. (2016).
VSEARCH: a versatile open source tool for metagenomics. \emph{Peerj, 4}
. doi:ARTN e2584

10.7717/peerj.2584

Rosling, A., Cox, F., Cruz-Martinez, K., Ihrmark, K., Grelet, G. A.,
Lindahl, B. D., . . . James, T. Y. (2011). Archaeorhizomycetes:
Unearthing an Ancient Class of Ubiquitous Soil Fungi. \emph{Science,
333} (6044), 876-879. doi:10.1126/science.1206958

Rydsaa, J. H., Stordal, F., Bryn, A., \& Tallaksen, L. M. (2017).
Effects of shrub and tree cover increase on the near-surface atmosphere
in northern Fennoscandia. \emph{Biogeosciences, 14} (18), 4209-4227.
doi:10.5194/bg-14-4209-2017

Schadt, C. W., Martin, A. P., Lipson, D. A., \& Schmidt, S. K. (2003).
Seasonal dynamics of previously unknown fungal lineages in tundra
soils.\emph{Science, 301} (5638), 1359-1361. doi:DOI
10.1126/science.1086940

Selosse, M. A., Schneider-Maunoury, L., \& Martos, F. (2018). Time to
re-think fungal ecology? Fungal ecological niches are often
prejudged.\emph{New Phytologist, 217} (3), 968-972. Retrieved from
\textless{}Go to ISI\textgreater{}://WOS:000424541600002

Seppey, C. V. W., Broennimann, O., Buri, A., Yashiro, E.,
Pinto-Figueroa, E., Singer, D., . . . Lara, E. (2020). Soil protist
diversity in the Swiss western Alps is better predicted by topo-climatic
than by edaphic variables. \emph{Journal of Biogeography, 47} (4),
866-878. doi:10.1111/jbi.13755

Serreze, M. C., \& Barry, R. G. (2011). Processes and impacts of Arctic
amplification: A research synthesis. \emph{Global and Planetary Change,
77} (1-2), 85-96. doi:10.1016/j.gloplacha.2011.03.004

Shen, C. C., Liang, W. J., Shi, Y., Lin, X. G., Zhang, H. Y., Wu, X., .
. . Chu, H. Y. (2014). Contrasting elevational diversity patterns
between eukaryotic soil microbes and plants. \emph{Ecology, 95} (11),
3190-3202. Retrieved from \textless{}Go to
ISI\textgreater{}://WOS:000345326000019

Sjogersten, S., \& Wookey, P. A. (2009). The Impact of Climate Change on
Ecosystem Carbon Dynamics at the Scandinavian Mountain Birch
Forest-Tundra Heath Ecotone. \emph{Ambio, 38} (1), 2-10. Retrieved from
\textless{}Go to ISI\textgreater{}://WOS:000263083500002

Smith, S., \& Read, D. (2008). \emph{Mycorrhizal Symbiosis} : Academic
Press.

Sorensen, M. V., Strimbeck, R., Nystuen, K. O., Kapas, R. E., Enquist,
B. J., \& Graae, B. J. (2018). Draining the Pool? Carbon Storage and
Fluxes in Three Alpine Plant Communities. \emph{Ecosystems, 21} (2),
316-330. doi:10.1007/s10021-017-0158-4

Stefana, G., Cornelia, B., Jorg, R., \& Michael, B. (2014). Soil water
availability strongly alters the community composition of soil
protists.\emph{Pedobiologia, 57} (4-6), 205-213.
doi:10.1016/j.pedobi.2014.10.001

Sterkenburg, E., Bahr, A., Durling, M. B., Clemmensen, K. E., \&
Lindahl, B. D. (2015). Changes in fungal communities along a boreal
forest soil fertility gradient. \emph{New Phytologist, 207} (4),
1145-1158. doi:10.1111/nph.13426

Stoeck, T., Bass, D., Nebel, M., Christen, R., Jones, M. D. M., Breiner,
H. W., \& Richards, T. A. (2010). Multiple marker parallel tag
environmental DNA sequencing reveals a highly complex eukaryotic
community in marine anoxic water. \emph{Molecular Ecology, 19} , 21-31.
doi:10.1111/j.1365-294X.2009.04480.x

Talbot, J. M., Allison, S. D., \& Treseder, K. K. (2008). Decomposers in
disguise: mycorrhizal fungi as regulators of soil C dynamics in
ecosystems under global change. \emph{Functional Ecology, 22} (6),
955-963. doi:10.1111/j.1365-2435.2008.01402.x

Tedersoo, L., Anslan, S., Bahram, M., Polme, S., Riit, T., Liiv, I., . .
. Abarenkov, K. (2015). Shotgun metagenomes and multiple primer
pair-barcode combinations of amplicons reveal biases in metabarcoding
analyses of fungi. \emph{Mycokeys} (10), 1-43.
doi:10.3897/mycokeys.10.4852

Tedersoo, L., Bahram, M., Polme, S., Koljalg, U., Yorou, N. S.,
Wijesundera, R., . . . Abarenkov, K. (2014). Global diversity and
geography of soil fungi. \emph{Science, 346} (6213), 1078-+. doi:ARTN
1256688

10.1126/science.1256688

van Son, T. C., \& Halvorsen, R. (2014). Multiple parallel ordinations:
the importance of choice of ordination method and weighting of species
abundance data. \emph{Sommerfeltia, 37} (1), 1-37.

Vasutova, M., Edwards-Jonasova, M., Baldrian, P., Cermak, M., \& Cudlin,
P. (2017). Distinct environmental variables drive the community
composition of mycorrhizal and saprotrophic fungi at the alpine treeline
ecotone. \emph{Fungal Ecology, 27} , 116-124.
doi:10.1016/j.funeco.2016.08.010

Wickham, H., \& Chang, W. (2016). Package `ggplot2'. Create Elegant Data
Visualisations Using the Grammar of Graphics (Version 2).



\textbf{Data accessibility}

All data that support the findings of this study are openly available in
Dryad.org at http://doi.org/{[}doi{]}, {[}reference number{]}.

\textbf{Author Contributions}

This study represent a revised version of the MSc thesis of LRT. LRT,
ET, LM, AB and HK designed the research and LRT, ET, LM and HK conducted
fieldwork. LRT, LM, ET, SM and LN did lab work and LRT, LM, SM and SB
analysed the data. LRT wrote the manuscript in collaboration with HK and
ET and all other co-authors edited and commented.





\textbf{Figure legends}

Figure 1. (a) The nine sampled sites in south-central Norway, from south
to north: Haglebu, Ustevatn (Hardangervidda), Strandavatnet, Skyrvedalen
(Hemsedal), Storlifjell (Vestre Slidre), Bessheim (Jotunheimen),
Lemonsjøen (Vågå), Sel, and Dombås. The map was drawn in QGIS version
3.4.14, with map data from geonorge.no. (b) Photo of the abrupt
vegetation change at the forest line at the Lemonsjøen site. (c)
Schematic view of the studied transects stretching across the mountain
birch forest line. Numbers 1 to 11 represents the plots where soil
samples were collected every 20 meters along the 200 m transect
stretching from 100 meters below to 100 meters above the mountain birch
forest line.

Figure 2. Barplots illustrating the relative abundances of taxonomic
groups across the ecotone based on the number of reads. (a) Kingdom and
(b) phylum level distribution of the 18S data. (c) Class level
distribution of the fungal 18S data. (d) Distribution of fungal classes
based on the ITS2 dataset. Plot number 01 is the lowermost plot in the
boreal mountain birch forest, while plot number 11 is the uppermost in
the low-alpine vegetation.

Figure 3. Boxplots illustrating how (a) the tree dominant fungal guilds,
(b) soil edaphic factors and ergosterol, and (c) plant groups based on
mycorrhizal status vary across the ecotone. The bold lines in the boxes
show the median, and the upper and lower part of the box represents the
25th and 75th percentiles. Outliers are represented by black dots. Plot
number 01 is the lowermost plot in the boreal mountain birch forest,
while plot number 11 is the uppermost in the low-alpine vegetation.

Figure 4. Scatter plots showing the relationship between (a) ergosterol
and soil carbon content and (b) proportional abundance of dark septate
endophytes (number of reads) and soil carbon content. The relationships
were modelled using mixed models with sites as a random factor, from
which the R squared values shown were extracted. The lines represent
least squares regression lines.

Figure 5. GNMDS ordination plots of (a) the 18S dataset and (b) ITS2
dataset based on rarefied OTU matrices. Each point represents one soil
sample. Plots belonging to the low-alpine vegetation are colored
red/yellow, while plots belonging to the boreal mountain birch forest
are green (see legend for details). Variables that significantly
correlated with the community structure (p\textless{}0.05) are shown as
arrows in the diagrams. Dark arrows represent edaphic factors, dark
green arrows site-specific factors, while light green arrows illustrates
the distribution of mycorrhizal plant types. (c) GNMDS ordination plot
of the ITS2 dataset based on species scores. Each point represents one
OTU. Only the 40 most common OTUs are visualized. Isolines are numbered
according to the plot-numbers ranging from 01 (low-alpine vegetation) to
11 (boreal mountain birch forest). Colors indicate functional groups
(see legend), and size of circles are proportional to the number of
reads. (1) \emph{Pezoloma ericae} , (2) \emph{Lecanorales} sp.,
(3)\emph{Helotiales} sp., (4) \emph{Cortinarius caperatus} ,
(5)\emph{Piloderma} sp., (6) \emph{Chaetothyriales} sp.,
(7)\emph{Cenococcum} sp., (8) \emph{Chaetothyriales} sp.,
(9)\emph{Cortinarius armillatus} , (10) \emph{Mortierella humilis} ,
(11)\emph{Herpotrichiellaceae} sp., (12) \emph{Cortinarius
pseudocandelaris} , (13) \emph{Cortinarius alpinus} ,
(14),\emph{Venturiales} sp., (15) \emph{Herpotrichiellaceae} sp.,
(16)\emph{Pseudotomentella tristis} , (17) \emph{Meliniomyces sp} .,
(18)\emph{Amanita} sp., (19) \emph{Helotiales} sp.,
(20)\emph{Herpotrichiellaceae} sp., (21) \emph{Solicoccozyma terricola}
, (22) \emph{Piloderma bicolor} , (23) \emph{Phialocephala} sp.,
(24)\emph{Helotiales} sp., (25) \emph{Luellia} sp.,
(26)\emph{Chaetothyriales} sp., (27) \emph{Hygrophorus albicastaneus} ,
(28)\emph{Mucor abundans} , (29) \emph{Agaricales} sp.,
(30)\emph{Helotiales} sp., (31) \emph{Meliniomyces} sp. 2,
(32)\emph{Meliniomyces} sp. 3, (33) \emph{Herpotrichiellaceae} sp.,
(34)\emph{Meliniomyces} sp. 4, (35) \emph{Hyaloscypha} sp.,
(36)\emph{Leohumicola minima} , (37) \emph{Hygrophorus} sp.,
(38)\emph{Meliniomyces bicolor} , (39) \emph{Muriformistrickeria rosae}
, (40)\emph{Cortinarius talus} .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig1/Fig1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig2/Fig2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3/Fig3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig4/Fig4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig5/Fig5}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

