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\begin{document}

\title{Extreme ecosystem engineering: The impact of marmots on vegetation cover
and plant nitrogen and phosphorus content in a cold, extremely arid
mountain environment}



\author[1]{Piotr Chibowski}%
\author[2]{Marcin Zegarek}%
\author[1]{Aleksandra Zarzycka}%
\author[1]{Małgorzata Suska-Malawska}%
\affil[1]{University of Warsaw}%
\affil[2]{Mammal Research Institute Polish Academy of Sciences}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\selectlanguage{english}
\begin{abstract}
We measured the content of nitrogen and phosphorus, as well as nitrogen
stable isotopes in plant biomass and marmot faeces in a distance
gradient up to 20 meters from marmot burrows in an extremely arid
glacier valley in Eastern Pamir, Tajikistan. The goal of this study was
to test well-known mechanisms of ecosystem engineering by burrowing
animals in previously unstudied conditions. We captured aerial images of
the area inhabited by marmots to study the spatial distribution of
vegetation. There was no relationship between the presence of burrows
and vegetation cover. Burrow mounds were not colonised by plants, as
opposed to other ecosystems, where mounds are often microhabitats that
enhance plant diversity. A significant increase in N and P in
aboveground green plant biomass in the proximity of burrows was found in
one out of six studied species. Stable N isotopes did not give further
insight into N routing, contrary to our expectations. We assume that
plant growth is strongly limited by water availability, which prevents
them from utilizing the local increase in nutrients, certainly provided
by marmot activity. The results are contrary to numerous studies which
showed that the role of burrowing animals as ecosystem engineers
increases with increasing abiotic stress, including aridity. This shows
a lack of this type of study in the end of the gradient of abiotic
factors.%
\end{abstract}%



\sloppy


\textbf{Extreme ecosystem engineering: The impact of marmots on
vegetation cover and plant nitrogen and phosphorus content in a cold,
extremely arid mountain environment.}

Piotr Chibowski\textsuperscript{1*}, Marcin Zegarek\textsuperscript{2},
Aleksandra Zarzycka\textsuperscript{1}, Ma\selectlanguage{polish}ł\selectlanguage{english}gorzata
Suska-Malawska\textsuperscript{1,3}

\textsuperscript{1}Faculty of Biology, Biological and Chemical Research
Centre, University of Warsaw, Warsaw, Poland *p.chibowski2@uw.edu.pl

\textsuperscript{2}Mammal Research Institute, Polish Academy of
Sciences, Bia\selectlanguage{polish}ł\selectlanguage{english}owie\selectlanguage{polish}ż\selectlanguage{english}a, Poland

\textsuperscript{3}International Platform for Dryland Research and
Education, Tottori University, Tottori, Japan\textbf{Abstract}

We measured the content of nitrogen and phosphorus, as well as nitrogen
stable isotopes in plant biomass and marmot faeces in a distance
gradient up to 20 meters from marmot burrows in an extremely arid
glacier valley in Eastern Pamir, Tajikistan. The goal of this study was
to test well-known mechanisms of ecosystem engineering by burrowing
animals in previously unstudied conditions. We captured aerial images of
the area inhabited by marmots to study the spatial distribution of
vegetation. There was no relationship between the presence of burrows
and vegetation cover. Burrow mounds were not colonised by plants, as
opposed to other ecosystems, where mounds are often microhabitats that
enhance plant diversity. A significant increase in N and P in
aboveground green plant biomass in the proximity of burrows was found in
one out of six studied species. Stable N isotopes did not give further
insight into N routing, contrary to our expectations. We assume that
plant growth is strongly limited by water availability, which prevents
them from utilizing the local increase in nutrients, certainly provided
by marmot activity. The results are contrary to numerous studies which
showed that the role of burrowing animals as ecosystem engineers
increases with increasing abiotic stress, including aridity. This shows
a lack of this type of study in the end of the gradient of abiotic
factors.

\textbf{Keywords} : long-tailed marmot, burrow, extreme, arid region,
Eastern Pamir, ecosystem engineering

\textbf{Cover letter}

Dear editor,

On behalf of my co-authors and myself, I am submitting the manuscript
entitled ``Extreme ecosystem engineering: The impact of marmots on
vegetation cover and plant nitrogen and phosphorus content in a cold,
extremely arid mountain environment.'' to Ecology and Evolution.

Burrowing animals influence their environment in several ways, one of
which is the impact on plants by fertilization via excretion on one hand
and herbivory on the other. Literature says, that the role of ecosystem
engineers increases with increasing abiotic stress. We studied the
impact of marmots on vegetation cover and nutrient content in very harsh
abiotic conditions in a high-mountain glacier valley in Eastern Pamir,
where plant life is supressed by cold and extreme aridity. Our results
contradict the abovementioned paradigm, as we found very little evidence
on the impact of the presence of marmot burrows on the studied
parameters. At the same time, it is hard to understand how plants
withstand the herbivore pressure and appear irrespectively of the
proximity of marmot burrows. We are trying to answer some of these
questions in our manuscript.

To our knowledge, the submitted manuscript is the first to study the
role of long-tailed marmots as ecosystem engineers, and to deal with the
impact of burrow-dwelling mammals on plants in such extreme abiotic
conditions. As alpine habitats are especially threatened by climate
change, and glaciers are retreating, revealing areas of bare soil, it is
important to study the mechanisms which shape life in this type of
ecosystem.

I find the submission guidelines and journal philosophy presented on the
website very author-friendly, which was a very important factor while
choosing Ecology and Evolution for the first submission of our
manuscript.

Kindest regards,

Piotr Chibowski

\textbf{Introduction}

Burrowing mammals play an important role in their ecosystem (Beca et
al., 2021; Davidson et al., 2012; Root-Bernstein \& Ebensperger, 2013),
and have been described as keystone species (Whitford \& Steinberger,
2010) and ecosystem engineers (Wesche et al., 2007). Burrows form
microhabitats that are used as shelter by other vertebrates (Ewacha et
al., 2016; Murdoch et al., 2009) and invertebrates (Sorokina \& Pont,
2011; Yoshihara et al., 2010). Burrowing mammals can occur in high
densities, therefore they also impact their environment through
non-engineering effects (Prugh \& Brashares, 2012) and can be important
prey for predators (Davidson et al., 2012). But probably the most
affected group of organisms are plants. The forming of mounds of burrow
material and exposing soils through trampling and herbivory alters the
competition between plants, promoting pioneer species. Additionally,
mounds mainly consist of soil from beyond the surface, which has
different chemical properties. Thus, it promotes different plant species
than in the surrounding area. Hence, an increase in plant biodiversity
and species richness can occur (Ballov\selectlanguage{ngerman}á et al., 2019; La et al., 2003;
Lindtner et al., 2018; Niu et al., 2020; Valkó et al., 2020). Colonies
and single burrows offer varied microhabitats and each can support a
different plant community (Ballová et al., 2019; Sasaki et al., 2013).
Faeces, urine and animal carcasses (in the case of burrows of predators)
increase soil nutrient content around the burrow. The vegetation around
the burrow profits from this, increasing its nutritional status. In the
proximity of burrows, the content of nitrogen and phosphorus in plant
biomass is usually 20-30\% higher than in the surrounding area
(Villarreal et al., 2008; Whicker \& Detling, 1988), but some studies
show an increase of up to 100\% in N and 200\% in P (Van Staalduinen \&
Werger, 2007). On the other hand, the positive effect on plant growth
fades above some level of burrow density (Guo et al., 2012), as the high
level of disturbance (grazing, digging and trampling) results in the
creation of bare soil patches with no vegetation, which are more prone
to water evaporation and N loss (Pang et al., 2020). Therefore, the
relationship between burrowing animal activity and vegetation cover can
be manifold. In habitats where undisturbed areas are completely covered
with vegetation, the presence of burrows and mounds obviously decreases
total vegetation cover (Louw et al., 2019). However, burrowing animals
can also cause an increase in vegetation cover, but it is limited to a
certain magnitude of disturbance, above which cover drops (Tang et al.,
2019). This type of unimodal relationship is also found for the impact
of burrowing animals on species richness or aboveground biomass (English
\& Bowers, 1994; Guo et al., 2012; Pang \& Guo, 2017; Whicker \&
Detling, 1988) and is considered a classical example to support the
intermediate disturbance hypothesis (Connell, 1978; Grime, 1973).
Because of the reasons mentioned above, burrowing animals (mainly
mammals) were often considered and treated as pests: an approach which
has been criticized by scientists due to the need for biodiversity
preservation and the role of burrowing species in the environment
(Delibes-Mateos et al., 2011; Smith \& Foggin, 1999), but also because
their negative impact on plant yield and pasture productivity is
doubtful (Bagchi et al., 2006; Guri\selectlanguage{polish}č\selectlanguage{english}eva, 1985).

Studies on the impact of burrowing animals on vegetation are numerous,
but the majority of them were conducted on steppes, arid and semi-arid
plains, including pastures or alpine meadows, as well as hot deserts
(Beca et al., 2021; Davidson et al., 2012; Mallen-Cooper et al., 2019;
Platt et al., 2016; Root-Bernstein \& Ebensperger, 2013). Almost no
studies show the ecological roles of burrowing mammals in extremely arid
high-altitude habitats in an early stage of soil formation and plant
succession, where plant productivity is limited by water shortage and
low temperatures, rather than nutrient availability and competition for
space. High-altitude habitats are among the regions where climate change
is the most rapid (Kohler et al., 2010). Melting glaciers are exposing
mineral soils, which are slowly being colonised by microorganisms and
plants (Egli et al., 2012). Whether this process can be facilitated by
burrowing ecosystem engineers is unknown. The first step to answering
this question should be a study of the impact of burrowing mammals on
plant communities already established in glacier forelands.

We studied the response of vegetation to the activity of long-tailed
marmots (\emph{Marmota caudata,} hereafter referred to as marmots, as
they are the only species from this genus in the study area) inhabiting
the valley of the Koksoy river, which flows from the Ujsu glacier in the
Eastern Pamir mountains -- an extremely dry habitat with hampered soil
development and plant succession (Kabala et al., 2021). We used an
unmanned aerial vehicle to take images of the area and map vegetation.
Given the initial character of our study, this method would allow us to
preliminarily assess the relationship between the presence of marmot
burrows and vegetation cover in a high number of replicates (burrows),
without indicating the precise cause of this relationship. We also
measured N and P content in aboveground green plant biomass in a
distance gradient from burrow entrances, to check whether plants are
utilizing the nutrient input from marmot faeces. We also used stable
isotope composition as a marker of animal-derived N. Preliminary field
observations of vegetation in the study area suggested, that the impact
of marmots on plants is not pronounced, as there was no visible increase
in vegetation properties around burrows (Suska-Malawska et al. unpubl.),
unlike in many other studies (Ballov\selectlanguage{ngerman}á et al., 2019; Fafard et al.,
2019). Also, faeces were seen mainly on mounds, close to the burrow
entrance. Therefore, we planned our study on a small scale, in 20-meter
buffers around burrow entrances.

\textbf{Materials and methods}

Burrow survey was done in July 2018. Sample collection, aerial images
and additional burrow search were done in July 2019.

\emph{Study area}

The Koksoy river valley is located in Eastern Pamir in Tajikistan
(39°19'15''N 73°13'32''E) at altitudes between 4100 and 4400 m a.s.l.
The valley is ca. 16 km long and up to 1.5 km wide (Fig. 1A) and
oriented west-northwest, with the river flowing along the southern
slope. Monthly averaged air temperatures in the valley are between
-20\textsuperscript{o}C in January and 5\textsuperscript{o}C in July and
August (Kabala et al., 2021). The mean annual precipitation (MAP) in the
nearest meteorological station close to Lake Karakul is ca. 80 mm
(Mischke et al., 2010). Previous research showed that soil development
and plant succession in this area are extremely slow, and organic soil
carbon pools are among the lowest reported in glacier forelands (Kabala
et al., 2021). Marmot burrows are concentrated in two locations: (1) on
the highest out of three river terraces, which has an area of about 1
km\textsuperscript{2}, and on an old alluvial fen adjacent to the
terrace. Burrows are located mainly along the northern edge of the
floodplain between 1 and 3 km from the glacier's front (the high
cluster, see Fig. 1A) (2) along the northern slope of the valley, at a
length of about 2.6 km, approximately in the middle of the valley,
between 6 and 8.4 km from the glacier terminus. Burrows are located on
flat ground on the lowest river terrace, and on the slope of the valley
(the low cluster, see Fig. 1A). The soil in both locations is very
poorly developed, but there are some differences in the upper layer
(0-10 cm), caused by different ages of the river terraces. The soil on
the highest, oldest terrace has a lower skeleton content and a higher
silt, sand and clay content. It also has a higher total N and P (in
Mehlich-3 extract) content than the soil on the lowest terrace.
Vegetation in the valley is sparse. In the area of the high cluster,
cushion plants and perennial forbs dominate, and the average vegetation
cover is 41.2\%. Dwarf shrubs, mainly \emph{Krascheninnikovia
ceratoides} and cushion plants dominate in the area of the low cluster,
and the average vegetation cover is 21.4\% (Kabala et al., 2021). The
long-tailed marmot is a burrow-dwelling rodent that inhabits a large
part of Central Asia. It is a large rodent, typically between 1.5 and
7.3 kg heavy, and is known to occupy a wider range of habitats than
related marmot species, occurring from 600 to 5200 m a.s.l and being
relatively tolerant of aridity (Krystufek \& Vohralik, 2013).
Long-tailed marmots consume mainly plants, foraging closely to their
burrows. They hibernate from September to April -- May (Blumstein \&
Arnold, 1998).

\emph{Preliminary field inspection and searching for burrows.}

During the field inspection, we searched the valley for marmot burrows
and recorded their location using a GPS device. We treated every
entrance as a separate burrow unless several entrance corridors were
clearly merging into one corridor. We noted the presence or absence of
plants on the mound. A common method to check the activity status of a
burrow is checking for the presence of fresh faeces (Karels et al.,
2004). We suspected, that due to the type of environment fresh faeces
dry out very quickly, but then remain intact for a long time. Therefore,
we counted all burrows which had any faeces on them. Burrows without
faeces were abandoned and usually damaged and we did not record them.
The area we searched was 17.18 km\textsuperscript{2} (Fig. 1A).

\emph{Aerial images}

We used an unmanned aerial vehicle (DJI Phantom 3 advanced) to capture
aerial images of the main part of the high cluster (Fig. 1A). Images
were captured in similar lightning conditions, at 45 m above ground,
with ISO speed 100, F-stop 4.5, auto-shutter and white balance set as
cloudy. The images were processed using Agisoft Metashape Professional
v.1.5.5 and converted into an orthophotomosaic with a 4-cm pixel size.
During image acquisition, it became clear that not all vegetation can be
seen on images, even if captured from low heights. Most plant species
grow only in-between polygons shaped by frost (Kabala et al., 2021),
mainly as single individuals and their colour is greyish-green. In turn,
patches of cushion plants (\emph{Acantholimon} sp., \emph{Androsace} sp.
and \emph{Oxytropis} sp.) were clearly visible. Therefore, we decided to
use cushion plants as a proxy for all vegetation. Due to long periods of
strong wind, we did not manage to take photographs of the low cluster.

\emph{Collection of plant biomass and faecal pellets}

For the analysis of nutrients and stable isotopes, we selected 7 burrows
with visible signs of recent marmot presence (fresh faeces) to collect
plant biomass samples and faecal pellets: 4 burrows from the high
cluster and 3 from the low cluster. To exclude the possibility of
cumulative influence of burrows, we chose burrows that were further than
50 meters from the entrance of the closest burrow with fresh faeces.
Around each burrow, we selected 2-4 dominant plant species/genera, and
we collected about 10 samples of aboveground green biomass from each
(Table 1). Samples were collected at different distances from the burrow
entrance, up to 20 m (Fig. 1C). We collected 171 biomass samples from 8
plant species/genera in 20 combinations of burrow and plant. From each
mound of the selected burrows, we collected 10 randomly selected fresh
faecal pellets.

\emph{Laboratory analysis}

Plant and faecal samples were air-dried on the spot, and later
oven-dried at 40\textsuperscript{o}C for 24 hours. Plant biomass samples
were then milled to a fine powder in liquid nitrogen using a pestle and
mortar. From each burrow, we randomly selected 3 sets of 3 faecal
pellets. We milled each faecal pellet set together in liquid nitrogen to
form 3 mixed samples from each burrow. From each sample (plant or
faeces) we weighted approx. 5 mg into a tin capsule and measured total N
(expressed as \% of dry mass) using a Flash 2000 Elemental Analyser. To
measure P content approx. 100 mg of each sample (plant or faeces) was
microwave digested in nitric acid. The P content was measured using the
molybdate method on a continuous flow San++ Skalar analyser. Between 1
and 3 mg of each plant or faeces sample (depending on N content) was
weighed into a tin capsule and measured for nitrogen stable isotope
composition using a Delta V Plus Isotope Ratio Mass Spectrometer,
coupled with a Flash 2000 Elemental Analyser via continuous flow. Stable
isotope ratios were expressed as \selectlanguage{greek}δ, \selectlanguage{english}i.e. as the deviation in per-mille
(%0) from the international standard, atmospheric nitrogen, in line with
the equation: \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{sample} =
(R\textsubscript{sample}/\textsubscript{Rstandard} - 1) x 1000, where R
is the isotopic ratio, \textsuperscript{15}N/\textsuperscript{14}N.
International standards were measured for calibration and measurement
precision, which was \textless{}0.1%0. All chemical laboratory analyses
were done in the Laboratory of Biogeochemistry and Environmental
Protection, Biological and Chemical Research Centre, University of
Warsaw.

\emph{Data analysis}

It was difficult to calculate the burrow density, as the area is a
mosaic of different geological forms (Kabala et al., 2021). Some parts
of the valley could be unsuitable for burrow forming, or avoided by
marmots for other reasons, affecting burrow density. Therefore, we had
to determine the area for burrow density measurement arbitrarily. We
also calculated the mean distance from each burrow to the nearest burrow
and from the five nearest burrows.

We excluded burrows that were located on the edge of the highest river
terrace from spatial analysis of aerial images. For the remaining 60
burrows, we analysed land cover in round buffers with a radius of 20 m
around the burrow entrance. We divided the buffer area of the selected
burrows into hexagons with a short diagonal of 25 cm, corresponding to
an area of 0.054 m\textsuperscript{2}. We assigned each hexagon to one
of four categories: burrow entrance, burrow mound, bare soil or
vegetation. The categories reflected the dominant type of cover
(\textgreater{}50\% area) within the hexagon. There was only one burrow
entrance hexagon per burrow. Burrow mounds were easily distinguishable
due to their grey colour and determined through visual inspection of the
orthophotomosaic. Vegetation was identified using raster analysis with
the Red-Green-Blue Vegetation Index (Bendig et al., 2015). All hexagons
that did not fall in the three mentioned categories were described as
bare soil. Distance measurements were done between centroids of
hexagons. For each buffer around a burrow, we measured the following
parameters: (1) total vegetation cover of the area free of mounds as:
vegetation hexagons/(vegetation hexagons + bare soil hexagons) (2) mound
area (3) mean and standard deviation of the distance of soil and
vegetation hexagons from the burrow entrance. For the analysis of the
relationship between vegetation cover and the distance from the nearest
burrow entrance, we selected burrows with a total vegetation cover
greater than 2\%. From the remaining 47 burrows, each was analysed
separately. In each buffer around a burrow, we divided hexagons into 20
equal intervals based on their distance from the nearest burrow
entrance, each interval 1 m wide. Due to the small number of hexagons in
the first interval, we merged it with the second interval, giving a
total of 19 intervals. In each interval, we calculated the vegetation
cover of the area free of mounds. We identified outliers based on the
z-score, which was calculated as: z = (value -- mean value from the
buffer)/SD from the buffer. Data points with a z-score below -3 or above
3 were excluded from further analysis. We used a binomial linear model
to calculate the adjusted R\textsuperscript{2} value for the
relationship and plot the data. Having four values for every buffer
(total vegetation cover, the mean distance of vegetation hexagons from
the nearest burrow entrance, mound area and
R\textsuperscript{2}\textsubscript{adjusted} for the relationship
between the vegetation cover and the distance from the nearest burrow
entrance), we calculated the Pearson correlation coefficient (r) between
each pair of parameters.

Before statistical analysis of the results of chemical analyses, we
checked whether the data met the requirements of parametric tests:
normal distribution (using the Shapiro-Wilk test) and equality of
variances (using the F-test for two groups and the Bartlett test for
multiple groups). We performed the Kruskal-Wallis test to compare the
chemical parameters of plant species and the post-hoc Dunn's all-pairs
test with Holm adjustment of p-values. We used the two-sided\emph{t}
-test (for normally distributed data with equal variances), the Welch's
\emph{t} -test (for normally distributed data with unequal variances) or
the Wilcoxon signed-rank test (for non-normally distributed data) to
check for differences in the abovementioned parameters between plants of
the same species from the high and low cluster, and between faeces from
the high and low cluster. We calculated the Spearman's rank correlation
coefficient (rho) between the measured parameters in biomass and the
distance of the plant from the burrow entrance, separately for each
plant species. Based on N and P content in plant biomass, we calculated
the N:P ratio.

We used QGIS v. 3.10.14 software for spatial analysis (QGIS Development
Team) and map preparation, and R v.4.0.5 software (R Core Team 2021) for
statistical analysis, using the build-in `stats' package. We used the
`ggplot2' package (Wickham, 2016) to prepare graphs.

\textbf{Results}

\emph{Burrow distribution and density}

We found 374 burrows: 241 within the high cluster, 127 within the low
cluster and 6 on other locations around the valley (Fig. 1A). The burrow
density in the whole searched area was 0.22 burrows/ha, on the high
cluster 0.82 burrows/ha or 1.15 burrows/ha when the middle part of the
third terrace was excluded. The density on the low cluster was 0.97
burrows/ha. The mean distance from a given burrow to another nearest
burrow in the high cluster was 32.09 \selectlanguage{ngerman}± 35.63 m (range 1.9 -- 382.06 m)
and from five nearest burrows 61.28 ± 50.57 m (9.65 -- 431.42). In the
low cluster, those values are 37.01 ± 34.47 (1.9 -- 188.98) from the
nearest burrow and 69.15 ± 47.87 (13.48 -- 243.83) from the five nearest
burrows. Most marmot faeces were placed in stacks of several pellets on
the mounds, only seldom in the surrounding area. We found no signs of
plant succession on any burrow mound.

\emph{Spatial data from aerial images}

Within the part of the valley which was covered by aerial images, the
highest vegetation cover was found in the south-eastern part, in long
patches along the elevation gradient of the studied valley (Fig. 1B).
The area of mounds varied between 0.87 and 82.81
m\textsuperscript{2}(mean 25.21 ± 17.80). If extrapolated to the whole
area of the cluster and all burrows there, mounds cover between 0.06 and
0.35\% of the high cluster and between 0.07 and 0.42\% of the low
cluster. The total vegetation cover in a 20 m buffer around a burrow
varied between 0.27 and 34.64\% (mean 8.63 ± 8.42\%) (Fig. 2B). The mean
distance from the closest burrow entrance of bare soil hexagons was
similar in all buffers and varied between 13.24 and 14.07 m (mean 13.58
± 0.19). The mean distance of vegetation hexagons was more variable,
between 9.66 and 18.48 m (mean 13.42 ± 1.67). In 17 burrows vegetation
hexagons were more than 1 meter closer to the nearest burrow than bare
soil hexagons (mean) and in 14 burrows more than 1 meter further (Fig.
2A). The adjusted R\textsuperscript{2} values of the relationship
between vegetation cover and the distance from the closest burrow
entrance, based on linear models measured for 47 buffers varied between
0.13 and 0.96 (mean 0.54 ± 0.3) (Fig. 2C). We visually identified four
types of relationship: negative linear (18 buffers), positive linear
(14), unimodal, with the highest values of vegetation cover at
intermediate distances from the burrow entrance (10) and no relationship
(R\textsuperscript{2}\textless{} 0.1) (Fig. 1D, Fig. 3). We found no
significant correlation between any of the parameters measured for each
burrow (total vegetation cover, mound area, mean distance of vegetation
hexagons, adj R\textsuperscript{2} from the linear model).

\emph{Chemical parameters of aboveground plant biomass}

Plant species differed significantly in all parameters which were
measured in aboveground biomass (Table 1). \emph{Acantholimon} sp.
and\emph{Androsace} sp. had the lowest N (2.70 ± 0.31\% and 1.99 ±
0.30\%, respectively) and P (0.09 ± 0.03\% and 0.07 ± 0.02\%,
respectively) content, and the highest N:P ratios (32.46 ± 11.94 and
31.38 ± 6.43, respectively). \emph{Erysimum altaicum} had the highest
content of both N (4.98 ± 0.88\%) and P (0.28 ± 0.12\%). The high and
low cluster varied in several measured parameters, depending on the
plant species (Appendix 1). Most notably, \emph{Braya pamirica} and
\emph{Oxytropis} sp. had higher N and P content in the high cluster than
in the low cluster. Most parameters in most species were not
significantly correlated with the distance from the burrow entrance
(Table 2). We found significant correlations in \emph{Erysimum altaicum}
with total N (rho= -0.64, S = 1872.6, p \textless{} 0.05), total P (rho
= -0.61, S = 1839.6, p \textless{} 0.05) content and N:P ratio (rho =
0.54, S = 521.46, p \textless{} 0.05) (Fig. 4).

Faeces from burrows from the high cluster had higher content of N (4.2\%
± 0.28) and lower \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values (0.07%0 \selectlanguage{ngerman}± 0.42) than
faeces from the low cluster (3.03\% ± 0.71 and 1.54\selectlanguage{english}%0 +- 1.22,
respectively). The content of P was similar in both locations (0.7\% +-
0.17 and 0.65 +- 0.19 on the high and low cluster, respectively). Out of
20 faeces -- plant species pairs, the \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values
differed in 7. In three cases plants had higher values than faeces and
in four cases lower (Fig. 5.)

\textbf{Discussion}

Contrastingly to the common paradigm that the role of ecosystem
engineers is most pronounced in harsh abiotic conditions (Crain and
Bertness 2006), our results generally show alimited impact of
long-tailed marmots on N and P content in plant biomass and no impact on
vegetation cover in an extremely arid, cold ecosystem.

A decrease in vegetation cover due to burrowing animal activity is
mostly caused by the presence of mounds close to the burrow entrance,
which have not been colonised by plants. (Dotter, 2009; Louw et al.,
2019). Our results showed no possibility of plant regrowth on mounds in
the present conditions in the studied area. Therefore, we excluded
mounds from the analysis of vegetation cover and tried to answer the
question, of whether marmots modify plant recruitment on actually
inhabitable soil. We found high variability in the parameters we
measured using aerial images, which is the reason why we decided to
perform the analysis on a small scale and numerous repetitions,
analysing each 20 m buffer around a burrow separately. Based on our
results, the response of vegetation cover to the proximity of burrows
could be positive, negative, binomial (with higher cover at highest and
lowest distances from the burrow entrance) or non-existing (Fig. 2).
This could suggest that those types occur depending on the balance
between herbivory and nutrient supplementing through excretion. However,
the relationship is probably best analysed by visual inspection of the
distribution of burrows relative to vegetation cover (Fig. 1): patches
of high cover occur along the slope of the river terrace, and probably
reflect past paths of water flow. Marmot burrows were located both on
and off these patches, and there are large areas with high vegetation
cover and no burrows. The type of relationship between the distance from
the nearest burrow and vegetation cover we found using the linear model
depends probably on the location of the burrow relative to a vegetation
patch. If it is located outside the patch, but the patch falls within
the 20 buffer, then the trend is negative. When the burrow is in the
patch, but the buffer reaches an area of bare soil, the trend is
positive (see Fig. 1D). The edges of the vegetation patches are sharp
and seem unaltered by burrow presence. The distribution of burrows is
clearly not random across the searched part of the valley (Fig 1A) and
can be an effect of selectivity towards physical soil properties, which
we cannot account for. Marmots seem to avoid alluvial fans, especially
active areas, a large part of the second river terrace and the middle
part of the third terrace. Based on this, we assumed that there is no
clear relationship between burrow presence and vegetation cover.

Given the limited effect of the additional nutrients on plants (see
below), the slow growth of plants and the fact that marmots graze almost
exclusively in the burrow area (Blumstein, 1998), it is puzzling that
vegetation cover does not seem impacted by burrow proximity. Some
burrow-dwelling mammals are known to create large patches of bare soil
due to herbivory and trampling (Davidson et al., 2012; Harris et al.,
2015; J. Zhao et al., 2021). We cannot explain this result, as we do not
know the primary production of plants, the population size of marmots
and their feeding requirements, as well as the time spent in different
burrows. The only study on the impact of long-tailed marmot (or any
other burrow-dwelling species) activity on plants conducted in a
comparable environment in Eastern Pamir, found a small impact of the
presence of marmot burrows on plant species composition, and the results
varied among study sites. Generally, dwarf shrubs occurred more often in
disturbed areas than in undisturbed areas, as opposed to herbs (Dotter,
2009), which suggest that browsing does impact the plant community, as
shrubs are more resistant to herbivory.

Our results on the relationship between the distance from the burrow and
N and P content in plant biomass are ambiguous. Only one out of six
species, \emph{E.altaicum} , showed an increase in biomass N and P
content with increasing proximity of the burrow entrance. However, the
effect size in this species is enormous, with N content increasing by
80\% and P content by 250\% with decreasing distance from the burrow
(Fig. 4). This implies that there is a nutrient input caused by marmots,
and so do the result of analyses of soil sampled within the high cluster
(Fig. 1A). It had almost 100 times higher P (0.31 mg/kg) 10 times higher
and N (0.169\%) content than from other parts of the valley (0.47 --
0.125 mg/kg and 0.01 -- 0.06\%, respectively), which the authors of this
study attribute to marmot activity (Kabala et al., 2021), as the sample
was collected within the high cluster, close to several burrows (Fig.
1D). This allows us to treat the mound area as a natural fertilization
experiment and interpret it as such, with necessary caution. The limited
impact of nutrient addition on vegetation cover and biomass N and P
content suggests, that plants are not limited by nutrient availability,
but probably by abiotic factors, mainly water scarcity, as they simply
lack the necessary moisture to utilize the additional nutrients (He \&
Dijkstra, 2014), and the effect of nitrogen addition was proven to be
correlated with precipitation (Yahdjian et al., 2011). Burrowing animals
are known to increase water availability for plants, as their
constructions promote water infiltration and prevent water runoff and
evaporation (Laundre, 1993; Whitford \& Kay, 1999). However, this
probably does not occur in our study area. Most of the precipitation is
presumably instantly lost to evaporation, irrespective of the presence
or absence of marmots. In turn, plants are adapted to these conditions
and have deep roots, up to 50 cm (Kabala et al., 2021), to utilize any
soil moisture. Even if there was an increase in water infiltration in
the surface layer of the soil, it could probably not be used by plants.
The response of \emph{E.altaicum} to the marmot-derived nutrient input
implies that this species, apart from being capable to utilize the
additional N and P, is more efficient at water uptake and/or preventing
water loss than other species. However, we lack information about the
functional traits of plant species from our study, so we are not able to
confirm this hypothesis.

The results of N:P stoichiometry in the distance gradient from the
burrow entrance show a shift from P limitation at higher distances to N
limitation close to the burrow. The use of the N:P ratio to identify the
limiting nutrient, where N:P \textless{} 14 indicates N limitation, N:P
\textless{} 16 P limitation and values in between indicate co-limitation
has been prosed for wetlands (Koerselman \& Meuleman, 1996) and proven
useful in grasslands (Craine \& Jackson, 2010; G. Zhao et al., 2017). We
are using this tool with caution, because the high N concertation in
plants inhabiting alpine habitats, and the resulting high N:P ratios
(such as those we found in most species) are mainly caused by slow
growth and adaptations to low temperatures, not by P limitation (K\selectlanguage{ngerman}örner,
2003). Those adaptations are species-specific and most probably
site-specific, therefore we are only looking at values within \emph{E.
altaicum} , and they indicate, that faeces are a more important source
of P than N. Faeces have similar N content as plant biomass, whereas the
P content is almost three times higher, indicating that they are a
better source of P than plant litter. Soil P content can be also
increased by the presence of marmot bones, which we have observed on
several mounds. Up to 30\% of marmots fail to survive the hibernation
period (Blumstein \& Arnold, 1998), their remains are probably ejected
on the surface after hibernation or during burrow maintenance. Plants
growing on initial soils are usually not P-limited, as it is abundant
from weathering minerals, as opposed to N (Walker \& Syers, 1976).
However, in high mountain continental areas, the low temperature and
precipitation can hamper weathering to the point that plans are limited
by P shortage (Darcy et al., 2018).

Increased N and P content in plant biomass close to burrows are mostly
caused by the input of organic matter by the burrowing animal, but it
can be also an indirect effect of animal activity: (1) Disturbed soil
has higher temperatures and is more susceptible to N mineralization,
increasing the amount of plant-available soil N (Whicker \& Detling,
1988) (2) Defoliation (including grazing) may increase N uptake by
plants (Jaramillo \& Detling, 1988; Yan \& Lu, 2020). Therefore, we
decided to use stable isotope composition as a marker of animal-derived
N use by plants. Faeces of rodents are enriched in \textsuperscript{15}N
compared to diet by 1.4 -- 2.5\selectlanguage{english}%0 (Hwang et al., 2007; Sare et al., 2005)
due to its discrimination in metabolic pathways. Given that long-tailed
marmots mainly stay close to the burrow while foraging (Blumstein,
1998), we hypothesised, that the elevated \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values
in faeces and the enhanced N cycling in burrow proximity would result in
higher \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N in plants growing closer to the burrow
entrance. The lack of difference between \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values
of plants and faeces in most cases (Fig. 5) implies little isotopic
enrichment of the latter. Results on isotopic enrichment (trophic
discrimination) in faeces are based on feeding experiments, where food
is usually provided \emph{ad libitum.} Fasting and nutritional stress,
which is frequent in the natural environment, is known to affect
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values of animal tissues (Doi et al., 2017; Hertz
et al., 2015) and could also alter the metabolic pathways leading to
faeces enrichment in \textsuperscript{15}N. Previous studies found no
clear pattern of stable isotope ratios in diet and faeces of burrowing
animals. Plains vizcacha (\emph{Lagostomus maximus} ) faeces had lower
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values by approx. 4%0 than soils and shrubs at
burrow systems, with no interpretation by authors (Villarreal et al.,
2008). Grasses close to arctic fox (\emph{Vulpes lagopus} ) dens had
similar \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values to grasses from control sites,
despite the significant impact of fox burrows on the available N pool in
soils (Gharajehdaghipour et al., 2016). Authors attributed the loss of
animal-derived \textsuperscript{15}N to discrimination during
mineralization and plant uptake (where high N availability could further
increase discrimination). Other studies showed an increase in
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N in plants at burrows, proving the fertilizing
effect of animal faeces (Ben-David et al., 1998; Garc\selectlanguage{ngerman}ía et al., 2002).
However, those studies were done on burrows of predators, which have
higher \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values in tissues and faeces due to longer
trophic chains (Fry, 2006). In such a case, when groups involved in the
relationship are separated by several trophic steps, each with its
specific isotopic enrichment (Vander Zanden \& Rasmussen, 2001), the
isotopic difference and the resulting method resolution is high enough.

A mechanism typical for the interaction between burrow-dwelling animals
and plants which does not occur under the circumstances we studied is
the role of burrow mounds. They consist of subsurface soil and are
initially bare, creating a new microhabitat for plants. They differ in
physicochemical properties from the surrounding soil, promoting pioneer
(Semenov et al., 2001; Swihart, 1991; Van Staalduinen \& Werger, 2007)
and nitrophile plants (Ballov\selectlanguage{ngerman}á et al., 2019; La et al., 2003). Mounds on
our study sites consisted mainly of stones larger than 2 cm, which make
up most of the subsurface soil layers (Kabala et al., 2021). Fine soil
particles, as well as soil moisture ejected by marmots from deeper
layers are probably removed by wind, like it has been found in desert
environments (Sharma \& Birla, 1975) long before it can be utilized by
plants. Also, there is no evidence for the presence of deep organic
matter in our study area (Kabala et al., 2021). Those are probably the
reasons for the lack of plant succession on mounds we found, similarly
to another study in Eastern Pamir (Dotter, 2009). Furthermore, marmot
activity causes a loss of inhabitable sites for plants, probably for
decades.

Researchers studying ecosystem engineering by burrowing animals have
identified the crucial role of water availability in the interaction
between those animals and plants. In arid regions, burrows and their
inhabitants have a larger impact on plant species richness (Romero et
al., 2015), soil nutrients (Decker, Eldridge, et al., 2019;
Mallen-Cooper et al., 2019), decomposition rate (Decker, Leonard, et
al., 2019) as compared to more arid environments. However, these
comparisons may be biased, as there is simply more burrowing animals and
more studies on ecosystem engineers in arid regions (Coggan et al.,
2018). Other authors refused to identify any factor which could drive
the effect size of burrow-dwelling ecosystem engineers, as it is solely
site- and species-specific (Louw et al., 2019; Root-Bernstein \&
Ebensperger, 2013), and our results seem to fit this description. In
humid, productive ecosystems where plants are mainly limited by biotic
factors, such as herbivory and competition, the effect of ecosystem
engineering is small (Crain \& Bertness, 2006). In arid ecosystems,
burrowing animals enhance nutrient cycling, which is hampered by water
shortage, thus locally increasing productivity (Gharajehdaghipour et
al., 2016; Yu et al., 2017). However, below a certain level of water
availability, as drought directly limits plant growth, this effect
decreases as it probably occurred in this study. Our results do not
undermine the paradigm on the relationship between abiotic factors and
the effect size of ecosystem engineers, but they lengthen the abiotic
gradient by extreme habitats which are strongly understudied.

Eastern Pamir, similarly to other mountain regions, is particularly at
risk due to climate change (Kohler et al., 2010). As temperatures are
increasing, melting glaciers will continue to reveal patches of bare
soil (M\selectlanguage{polish}ę\selectlanguage{english}trak et al., 2015). But as precipitation in Eastern Pamir is
expected to decrease even further (Lioubimtseva \& Henebry, 2009;
Normatov \& Normatov, 2020), the succession of these areas will probably
remain extremely slow and the impact of marmots on plants minimal.
Rising temperatures may increase the rate of mineral weathering
(Gislason et al., 2009), further diminishing the role of animal-derived
nutrients for plants.

Despite the relatively small impact on plant nutritional status and
vegetation cover, long-tailed marmots inhabiting extremely dry mountain
habitats, most probably have a crucial impact on the ecosystem. Given
the burrow density, marmots are probably the most abundant animal
species in terms of biomass. This can make them an important food source
for predators inhabiting our study area: wolves (\emph{Canis lupus} )
red foxes (\emph{Vulpes vulpes} ), and snow leopards (\emph{Uncia uncia}
), which are known to prey on marmots, especially the two latter species
(Blumstein \& Robertson, 1995; Jumabay-Uulu et al., 2014; Khatoon et
al., 2017). Marmot burrows can provide unique refuges of stable
temperature and high humidity, crucial for the development of insects,
especially in arid environments (Pike \& Mitchell, 2013; Whitford \&
Kay, 1999). Marmot burrows can be used by other animals as the only
shelter from strong winds and predators. Due to the unique character of
the studied environment, the mechanisms described in this paragraph,
similarly to our results on animal-plant interactions, can happen
differently than in grasslands, steppes and mountain meadows. We think,
that a lot of questions in this area are open to further research.

\textbf{Data Accessibility Statement}

If accepted, the data used to write this article will be published in an
open data repository.

\textbf{Conflict of interest}

The authors declare that they have no competing interests.

\textbf{Author contribution}

Piotr Chibowski: Conceptualization (equal); data curation (equal);
formal analysis (lead); investigation (equal); methodology (equal);
visualization (lead); writing -- original draft (lead); writing --
review and editing (equal). Marcin Zegarek: Conceptualization (equal);
data curation (equal); formal analysis (supporting); investigation
(equal); methodology (equal); visualisation (supporting); writing --
review and editing (equal). Aleksandra Zarzycka: Conceptualization
(equal); methodology (equal); investigation (equal); writing -- review
and editing (equal). Ma\selectlanguage{polish}ł\selectlanguage{english}gorzata Suska-Malawska: Conceptualization
(equal); funding acquisition (lead); investigation (supporting);
methodology (equal); resources (lead); writing -- review and editing
(equal).

\textbf{Acknowledgements}

We thank Cezary Kaba\selectlanguage{polish}ł\selectlanguage{english}a, Monika M\selectlanguage{polish}ę\selectlanguage{english}trak, Elizabeth Le Roux and Marcin
Sulwi\selectlanguage{polish}ń\selectlanguage{english}ski for help with the interpretation of our results. This work was
financially supported by the National Science Centre of Poland
(https://www.ncn.gov.pl/), grant number 2017/25/B/ST10/00468



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Table 1. Plant species collected for nutrient analysis, total number of
samples, burrows at which the species were collected (number of samples
in brackets) and the content of N and P and stable nitrogen isotope
ratios (mean \selectlanguage{ngerman}± SD). Kruskall-Wallis test results are given for the
comparisons between species. Letters next to values indicate plant
species, that differed significantly based on pairwise comparison using
the Dunn's all-pairs test with Holm adjustment of p-values.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Plant species/genus & N & Burrows (n) & Total N (\%) & Total P (\%) &
N:P & \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N (%0)\tabularnewline
\midrule
\endhead
\emph{Acantholimon} sp.\textsuperscript{a} & 33 & 1(10), 3(9), 7(9),
6(5) & 2.7 \selectlanguage{ngerman}± 0.31\textsuperscript{c, e, f, g, h} & 0.09 \selectlanguage{ngerman}±
0.03\textsuperscript{c, e, f, g, h} & 32.46 \selectlanguage{ngerman}± 11.94\textsuperscript{c,
d, e, f, g} & 1.12 \selectlanguage{ngerman}± 1.71\textsuperscript{g}\tabularnewline
\emph{Androsace} sp.\textsuperscript{b} & 6 & 2(6) & 1.99 \selectlanguage{ngerman}±
0.3\textsuperscript{b, e, f, g, h} & 0.07 \selectlanguage{ngerman}± 0.02\textsuperscript{c, e,
f, g, h} & 31.38 \selectlanguage{ngerman}± 6.43\textsuperscript{c, d, e, f} & -0.81 \selectlanguage{ngerman}±
0.56\textsuperscript{f}\tabularnewline
\emph{Braya pamirica}\textsuperscript{c} & 12 & 2(6), 5(6) & 3.85 \selectlanguage{ngerman}±
0.79\textsuperscript{a, b, e} & 0.22 \selectlanguage{ngerman}± 0.07\textsuperscript{a, b} &
18.54 \selectlanguage{ngerman}± 4.68\textsuperscript{a, b} & 1.04 \selectlanguage{ngerman}±
1.93\textsuperscript{g}\tabularnewline
\emph{Ephedra} sp.\textsuperscript{d} & 5 & 2(5) & 2.89 \selectlanguage{ngerman}±
0.35\textsuperscript{e, f, h} & 0.21 \selectlanguage{ngerman}± 0.12 & 16.41 \selectlanguage{ngerman}±
4.31\textsuperscript{a, b} & 1.11 \selectlanguage{ngerman}± 0.48\tabularnewline
\emph{Erysimum altaicum}\textsuperscript{e} & 19 & 3(9), 4(10) & 4.98 \selectlanguage{ngerman}±
0.88\textsuperscript{a, b, c, d, f, g} & 0.28 \selectlanguage{ngerman}± 0.12\textsuperscript{a,
b} & 19.91 \selectlanguage{ngerman}± 5.84\textsuperscript{a, b} & 1.51 \selectlanguage{ngerman}±
2.38\textsuperscript{g}\tabularnewline
\emph{Krascheninnikovia ceratoides}\textsuperscript{f} & 30 & 7(10),
5(10), 6(10) & 4.05 \selectlanguage{ngerman}± 0.69\textsuperscript{a, b, e} & 0.23 \selectlanguage{ngerman}±
0.07\textsuperscript{a, b, g} & 18.31 \selectlanguage{ngerman}± 4.2\textsuperscript{a, b, h} &
1.87 \selectlanguage{ngerman}± 2.1\textsuperscript{b, g}\tabularnewline
\emph{Oxytropis} sp.\textsuperscript{g} & 46 & 2(12), 3(9), 4(10),
5(10), 6(5) & 3.68 \selectlanguage{ngerman}± 0.54\textsuperscript{a, b, e, h} & 0.17 \selectlanguage{ngerman}±
0.04\textsuperscript{a, b, e, f} & 22.03 \selectlanguage{ngerman}± 3.55\textsuperscript{a} &
-1.01 \selectlanguage{ngerman}± 1.22\textsuperscript{a, c, e, f, h}\tabularnewline
\emph{Leiospora eriocalyx}\textsuperscript{h} & 20 & 1(10), 5(10) & 4.44
\selectlanguage{ngerman}± 0.52\textsuperscript{a, b, d, g} & 0.18 \selectlanguage{ngerman}± 0.07\textsuperscript{a, b} &
26.04 \selectlanguage{ngerman}± 6.54\textsuperscript{c, f} & 1.05 \selectlanguage{ngerman}±
1.88\textsuperscript{g}\tabularnewline
& & H & 105.48 & 98.79 & 68.27 & 65.83\tabularnewline
& & df & 7 & 7 & 7 & 7\tabularnewline
& & p & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 &
\textless{}0.0001\tabularnewline
\bottomrule
\end{longtable}

Table 2. Spearman's rank correlation coefficient (rho) between the
distance from the burrow entrance and all measured parameters in plant
biomass. Bold values indicate significant correlations
(p\textless{}0.05). Plants with a sample number below 10
(\emph{Androsace} sp., \emph{Ephedra} sp.), were excluded from the
analysis. See Table 1 for full species names.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Plant species & Total N (\%) & Total P (\%) & N:P &
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N (%0)\tabularnewline
\midrule
\endhead
\emph{Acantholimon} sp. & -0.13 & -0.12 & 0.07 & -0.16\tabularnewline
\emph{B. pamirica} & -0.37 & -0.06 & -0.16 & 0.23\tabularnewline
\emph{E. altaicum} & \textbf{-0.64} & \textbf{-0.61} & \textbf{0.54} &
-0.02\tabularnewline
\emph{K. ceratoides} & -0.07 & -0.14 & 0.05 & -0.29\tabularnewline
\emph{Oxytropis} sp. & -0.11 & -0.18 & 0.16 & 0.09\tabularnewline
\emph{L. eriocalyx} & -0.16 & -0.3 & 0.23 & -0.14\tabularnewline
\bottomrule
\end{longtable}

Fig. 1. The study area in the North-Western part of the Koksoy river
valley. A: Overview of the study area, location of burrows, search area
and sampling sites. B: Part of the aerial image with the location of the
analysed burrows. The green channel is enhanced, so vegetation is
visible. Note, that this is not an exact visualisation of plant
occurrence, as it was not manually corrected and can be biased by
varying lighting conditions in different parts of the image. C: Example
of sampling spots of plant biomass around a burrow with 20 m buffer. D:
Group of 20 m buffers around burrows and the type of relationship
between the distance from the closest burrow entrance and the plant
cover (see Fig. 3). The aerial image is edited like in C. E: Location of
the study area in Tajikistan.

Fig. 2. The distribution of parameters within burrows: a) Mean distance
from the closest burrow entrance of vegetation hexagons and bare soil
hexagons. For clarity, SD is only shown for vegetation hexagons (error
bars) b) Total plant cover in buffers c) Adjusted R\textsuperscript{2}of
the linear binomial model of the relationship between the distance from
the closest burrow entrance and plant cover. The number of burrows is
smaller than in other parameters, as models were only done for burrows
with total plant cover above 2\%. d) The number of types of relationship
between the distance from the closest burrow entrance and plant cover,
based on the shape of the trend line. For plots a-c, the values are
sorted from smallest to largest. Colours indicate the type of
relationship between the distance from the closest burrow entrance and
plant cover (see Fig. 3)

Fig. 3. Examples of trend lines between the distance from the closest
burrow entrance and plant cover based on binomial linear models,
assigned to different types of relationship.

Fig. 4. The relationship between the distance from the burrow entrance
and parameters of plant samples. Only pairs with significant
relationship are shown. Plant species and Spearman's rho are given. The
dashed lines in the N:P plot indicate values 14, below which N
limitation can be assumed and 16, above which P limitation can be
assumed. See discussion for details on the constraints of this
inference.

Fig. 5. Stable nitrogen isotope ratios of plants and marmot faeces
collected on each burrow. Asterisks indicate significant differences
between \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values of faeces and plant species
sampled from one burrow, based on the Wilcoxon test (p\textless{}0.05).
The colour of the asterisk indicates the plant species.

Appendix 1. Comparison of nutrient content and stable isotope ratios
between the low and high cluster within plant species that were sampled
in both clusters. Pairs that are bold differ significantly
(p\textless{}0.05). TS -- test statistic value, t for \emph{t} -test, W
for Wilcoxon test. * - Welch's \emph{t} -test for unequal variances\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Species & Cluster & N & Total N (\%) & Total P (\%) & N:P &
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N (%0)\tabularnewline
\midrule
\endhead
\emph{Acantholimon} sp & High & 19 & 2.68 \selectlanguage{ngerman}± 0.32 & 0.1 \selectlanguage{ngerman}± 0.03 &
\selectlanguage{ngerman}\textbf{28.21 ± 27.92} & 1.07 \selectlanguage{ngerman}± 1.98\tabularnewline
& Low & 14 & 2.74 \selectlanguage{ngerman}± 0.3 & 0.08 \selectlanguage{ngerman}± 0.03 & \selectlanguage{ngerman}\textbf{38.23 ± 39.89} & 1.2 \selectlanguage{ngerman}±
1.27\tabularnewline
& & TS & t = 0.52 & t = 1.73 & t = -2.28 & t = -0.21\tabularnewline
& & df & 31 & 31 & 16.64 & 31\tabularnewline
& & p & \textgreater{}0.05 & \textgreater{}0.05 & \textless{}0.05* &
\textgreater{}0.05\tabularnewline
\emph{B. pamirica} & High & 6 & \selectlanguage{ngerman}\textbf{4.48 ± 0.37} & 0.22 \selectlanguage{ngerman}± 0.02 &
20.27 \selectlanguage{ngerman}± 19.79 & 0.57 \selectlanguage{ngerman}± 1.1\tabularnewline
& Low & 6 & \selectlanguage{ngerman}\textbf{3.21 ± 0.57} & 0.22 \selectlanguage{ngerman}± 0.1 & 16.81 \selectlanguage{ngerman}± 16.4 & 1.51 \selectlanguage{ngerman}±
2.51\tabularnewline
& & TS & t = 4.18 & W = 29 & t = 0.26 & t = -0.79\tabularnewline
& & df & 10 & - & 5.89 & 10\tabularnewline
& & p & \textless{}0.05 & \textgreater{}0.05 & \textgreater{}0.05* &
\textgreater{}0.05\tabularnewline
\emph{Oxytropis} sp & High & 31 & \selectlanguage{ngerman}\textbf{3.83 ± 0.55} & \selectlanguage{ngerman}\textbf{0.19 ±
0.03} & \selectlanguage{ngerman}\textbf{20.75 ± 21.97} & -0.97 \selectlanguage{ngerman}± 1.27\tabularnewline
& Low & 15 & \selectlanguage{ngerman}\textbf{3.38 ± 0.38} & \selectlanguage{ngerman}\textbf{0.14 ± 0.02} & \selectlanguage{ngerman}\textbf{24.67
± 25.86} & -1.08 \selectlanguage{ngerman}± 1.11\tabularnewline
& & TS & t = 2.80 & t = 5.66 & t = -4.02 & W = 272\tabularnewline
& & df & 44 & 44 & 44 & -\tabularnewline
& & p & \textless{}0.05 & \textless{}0.0001 & \textless{}0.001 &
\textgreater{}0.05\tabularnewline
\emph{L. eriocalyx} & High & 10 & 4.62 \selectlanguage{ngerman}± 0.39 & 0.18 \selectlanguage{ngerman}± 0.04 & 26.9 \selectlanguage{ngerman}±
24.84 & \selectlanguage{ngerman}\textbf{2.44 ± 2.07}\tabularnewline
& Low & 10 & 4.26 \selectlanguage{ngerman}± 0.58 & 0.19 \selectlanguage{ngerman}± 0.08 & 25.17 \selectlanguage{ngerman}± 26.77 & \selectlanguage{ngerman}\textbf{-0.35 ±
1.67}\tabularnewline
& & TS & W = 72 & W = 50 & t = 0.57 & t = 4.74\tabularnewline
& & df & - & - & 18 & 18\tabularnewline
& & p & \textgreater{}0.05 & \textgreater{}0.05 & \textgreater{}0.05 &
\textless{}0.001\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}

\end{center}
\end{figure}

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

