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\begin{document}

\title{Scale-dependent niche segregation along dimensions of rocks, vegetation
and elevation in sympatric pikas of Ladakh}



\author[1]{Harsha Kumar}%
\author[1]{Dhanesh Ponnu}%
\author[1]{Senan D'souza}%
\author[1]{Dhiraj  Das}%
\author[1]{Skarma Thundup}%
\author[1]{Nandini  Rajamani}%
\affil[1]{Indian Institute of Science Education and Research Tirupati}%


\vspace{-1em}



  
  \date{May 29, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Microhabitat selection in patchy environments supports the co-existence
of closely related species competing for resources. We examined niche
partitioning in three sympatric species of pikas, \emph{Ochotona
macrotis, Ochotona nubrica,} and \emph{Ochotona ladacensis} from Ladakh,
India, that display contrasting lifestyles, social behavior and co-occur
at small spatial scales. We used a classical paired quadrat approach to
document biologically relevant vegetation and landscape features
(niches) likely to support the presence of different species. We used a
Bayesian framework to describe niche spaces , estimate niche widths and
overlaps between species pairs. In addition, we used a GLM framework to
identify factors that promote the presence of different species in the
landscape. The rock-dwelling \emph{Ochotona macrotis} was a specialist,
exclusively associated with microhabitats offering a good cover of
large-sized rocks and no shrubs. The social, \emph{Ochotona nubrica},
was a specialist found across elevations but associated exclusively with
mature stands of scrub vegetation (\emph{Caragana sp} in the South-East
and \emph{Hippophae sp} in the North-West) occupying a unique niche. The
social \emph{Ochotona ladacensis,} although an elevational specialist,
was likely to be found in microhabitats of other species characterised
by moderate rock cover and low-lying Caragana scrublands, in addition to
being found in alpine grasslands and meadows.%
\end{abstract}%



\sloppy


\textbf{Scale-dependent niche segregation along dimensions of rocks,
vegetation and elevation in sympatric pikas of Ladakh}

\textbf{Introduction}

The conceptualization of the fundamental and the realized niche
(Grinnell 1917, Hutchinson 1957) of species has been particularly
valuable to the growth of community ecology (Roughgarden 2009),
evolutionary biology (Sexton et al. 2017), and in understanding
distribution patterns across space to inform conservation planning
(Pulliam 2000, Ahmad et al. 2021, Dvo\selectlanguage{polish}ř\selectlanguage{ngerman}ák et al. 2022). The niche
mathematically represents an n-dimensional hypervolume that describes
the range of biotic and abiotic factors in which the animal can survive
and reproduce (Grinnell 1917, Hutchinson 1957). The width of the niche
and the degree to which it can be found in niches of other animals can
be used to assess species on the generalist-specialist continuum
(Fridley et al. 2007).

Species attributes that promote stable co-existence of ecologically
similar species in communities have intrigued researchers for a long
time, starting with competitive exclusion experiments in the 1930s
(Gause 1936, MacArthur 1958, Hardin 1960, Hutchinson 1961, Slatkin 1974,
Hanski and Ranta 1983, Nardone and Gherardi 1997, Sommer 1999).
Community assemblages or niches are known to be shaped by abiotic and
biotic filtering where physiological limits and biological interactions
such as competition for resources and predation limit species occurrence
and co-existence (Schoener 1974, Martin 1988, Dunson and Travis 1991,
Weiher and Keddy 1995, Weiher et al. 1998, MacRae and Jackson 2001,
Stoks and McPeek 2003, Cavender-Bares et al. 2004, Dayan and Simberloff
2005, Bøhn et al. 2008, Pfennig and Pfennig 2009, Colman et al.
2014)\emph{.} In this manuscript we describe the niche space, overlaps
and partitioning between three sympatric species of pikas
(\emph{Ochotonidae} ) from the high elevation trans-Himalayas
(3000m-6000m) of Ladakh, India.

Interspecific competition is known to alter the niche width of species
and affect community structuring through population sizes of different
species (MacArthur 1958, Connell 1961, Mac Nally and Timewell 2005,
Luiselli 2006, Bolnick et al. 2010, Stellati et al. 2021). Spatially
heterogeneous/patchy environments allow for the co-existence of
ecologically/closely related species by alleviating competition through
ecological specialization (Hanski 1983, Hanski and Ranta 1983, Holt
1987, Chesson 2000, Amarasekare 2003). This is facilitated by
differential habitat/microhabitat selection or use at spatial (Chipps et
al. 1994, Satoh and Hori 2005, Malavasi et al. 2007, Tamme et al. 2010,
Sillero et al. 2020, Gurvich et al. 2022) and temporal scales (Townsend
and Hildrew 1979, Faria and Almada 2001, Castro-Arellano and Lacher
2009, Lea et al. 2020). Although microhabitat selection between closely
related species can reduce competition (Majumder et al. in press,
Goulart et al. 2009, Lea et al. 2020), it is often hard to establish
that competition has driven this selection over and above other biotic
and abiotic filtering mechanisms (Shanker 2001, Zhang et al. 2006,
Afonso and Eterovick 2007, Crow et al. 2010).

The trans-Himalayas of Ladakh, India, are home to asocial rock-dwelling
(\emph{Ochotona macrotis: OM} ) and social burrowing species
(\emph{Ochotona ladacensis: OL, Ochotona nubrica: ON} ). While
\emph{OM}is found in rocky talus and scree slopes in alpine deserts
(2300m-6000m), \emph{ON} is a burrowing species that uses thorny
vegetation, rocks when availbale (2800m-5300m) and with (Pfister 2004,
Wilson and Mittermeier 2016, Smith et al. 2018). \emph{OL,} on the other
hand\emph{,} is associated with barren xeric alpine valleys at high
elevations characterised by cushion plants and sedges (Pfister 2004,
Wilson and Mittermeier 2016, Smith et al. 2018) (4200m-5400m). A
photographic representation of these microhabitats reveals differences
and similarities of niches of different species (Figure 1).
While\emph{OM} and \emph{OL} (clade \emph{Conothoa)} have diverged
\textasciitilde{}5mya, these species have diverged from \emph{ON}
(clade\emph{Ochotona} ) 13mya (Wang et al. 2020, Lissovsky et al. 2022).
Assuming that closely related species share similar physiological
requirements, we would expect that \emph{OM} and\emph{OL} use similar
microhabitats when compared to \emph{ON}(Dahal et al. 2020).

In this study, we describe the ecological niche space of different
species of sympatric pikas in Ladakh and address if they use different
microhabitats. In addition, we examine how various ecological factors
drive pika presence at high elevations in Ladakh, India, by documenting
microhabitat level features (variables related to rocks and vegetation)
and topographic features (elevation, slope, aspect) at different spatial
scales. Studies on Neotropical snakes (Corrêa Nogueira et al. 2019) and
Austalian songbirds (Harmá\selectlanguage{polish}č\selectlanguage{english}kov\selectlanguage{ngerman}á et al. 2019) found evidence for
scale-dependent phenomena of niche spaces and overlaps between species
but such studies of niches at different spatial scales are generally
rare. A well-defined study at varying spatial scales would not only
provide a framework to describe niches of species, address evolutionary
drivers of such choice, but also allow for exploration of proximate
factors that drive presence and directly feed back into conservation
planning (Lian and Jianping 2005, Edgel et al. 2014). Pikas are
high-elevation specialists under threat of extirpation due to climate
change, and describing their niches will provide baseline data for
effective policy-making in the trans-Himalayas (Beever et al. 2010,
2011, Dahal et al. 2020).

\textbf{Methods}

\textbf{2.1 Determining pika occurrence} - Appropriate microhabitats
were visually inspected for signs of pika activity (fecal pellets,
haypiles, and burrows). A 10m*10m quadrat was laid centered around each
identified site. In each quadrat, variables about rocks, vegetation, and
topographic features were documented (Table S1). In addition to each
occurrence plot, a random paired plot was laid 50m away (compass
direction determined by a uniformly generated random number bound
between 1 and 360). For social pikas, colony boundaries were identified
using outermost burrows, and paired plots were laid outside the colony.

\textbf{2.2 Extraction of high-resolution topographic information from
remotely sensed data} - For mapping altitude, a Digital Elevation Model
(30m) for the tiles of our interest was downloaded from the Bhuvan,
Indian Geo Platform of ISRO (https://bhuvan.nrsc.gov.in/home/index.php).
The raster was further processed in ArcMap 10.5 (ESRI 2022) to give
slope and aspect, which represent relevant ecological attributes of a
landscape that animals are known to use to select sites for residence.
NDVI values (30m) for each plot location were obtained for the months of
July-August (Summer) and November-December (Winter) dating back nine
years (2013 to 2021), from Landsat 8 imagery (USGS) with the least cloud
cover, using Google Earth Engine (Gorelick et al. 2017). These were
further processed to provide additional variables concerning NDVI (Table
S1).

\textbf{2.3 Sampling sites} - Based on logistics and pilot surveys,
sampling locations were chosen across elevational distribution ranges of
the three pika species (Figure 2, Figure 3). Since we aimed to
understand ecological factors that shape pika presence at a broad scale,
plots were laid in different locations for each species (Figure 2)
(South-East Ladakh - 88 plots; North-West Ladakh - 66). Sampling was
done in zones of sympatry and supplemented with sampling in zones of
allopatry to capture the niche breadth of all species. The regions
sampled included South-East Ladakh (part of the Qinghai-Tibetan plateau;
4000kmsq) and North-West Ladakh (Ladakh and Zanskar ranges; 8000kmsq).

\textbf{2.4 Generalised Linear Models (GLMs)} - To identify ecological
factors that support the presence of different species of pikas at a
broader scale, quadrats sampled across species and sites (n=148) were
pooled and scored for a binary presence/absence variable for each
species (for instance presence and absence plots of \emph{ON}
and\emph{OL} were treated as absence points for \emph{OM} ). Such an
approach is helpful as it is informative regarding where a species is
likely or unlikely to be present. We modeled the presence of pikas at
sites using logistic regressions with plot variables (Table S1) as
predictors using the `glm' function in the R package `arm' (R core team
2021, Gelman and Su 2021).

Landscape features and vegetation structure were noticeably different
across sites (Figure 9), with huge-sized boulder rocks at low elevations
in North-West Ladakh (Thanh et al. 2010), including sea buckthorn along
rivers and in South-East Ladakh, the extensive cover of \emph{Caragana}
, cushion plants, and small-sized scree rocks at high elevations. We,
therefore, modeled the presence of these species at three different
spatial extents: a) South-East and North-West Ladakh (controlling for
differences in landscape and vegetation), b) Ladakh (across the entire
landscape). Since only OM and ON are found in North-West Ladakh, only
the presence of these species was modeled at this spatial scale. At the
South-East Ladakh scale, the occurrence of all three species was modeled
as they co-occur in this landscape.

Due to problems associated with sample size, all variables additively
could not be subject to a stepwise/backward selection algorithm. To
resolve this problem, we use a modified version of the purposeful
selection framework (Bursac et al. 2008, Zhang 2016). The workflow used
is depicted below:

\begin{enumerate}
\tightlist
\item
  Predictor variables that affected presence were identified using
  univariate logistic regressions and plots (one predictor and one
  binary response variable). All variables that explained the occurrence
  of a species in these models (p\textless{}0.05) were additively used
  to model occurrence (Model 1: Statistical model). In addition, all
  variables with a p-value cut-off of 0.25 and other variables that were
  biologically relevant (based on field observations) were additively
  used to model the occurrence (Model 2: Purposeful model) following
  Zhang (2016). Finally, a third model was constructed with predictors
  (p-value \textless{} 0.25 in uni-variate regressions) binned into
  different ecological classes (Table S2) to depict the different
  landscape-level features (Model 3: sub-model) that could influence
  pika presence.
\item
  A stepwise regression (StepAIC) was run on all three models to pick
  models that best explain the occurrence (based on the three species of
  interest). When AIC values varied marginally across sub-models in
  ecological classes, all predictors across models were considered for
  further analysis.
\item
  For the sub-model alone, variables from all the best sub-models across
  different ecological classes were additively used to model presence
  using a stepwise regression for the second time. While doing so, care
  was taken to add/replace biologically relevant variables (based on
  field experience and literature) even if they were dropped during the
  sub-modeling process.
\item
  Best models across modeling approaches were compared and analyzed.
  Finally, biologically relevant interactions (based on field
  observations) were evaluated based on AIC scores. Goodness of fit
  tests (Hosmer and Lemeshow 1989) were performed to assess model fits.
\end{enumerate}

\textbf{2.5 Comparison of niche overlaps and niche width between species
at broad and regional scales} - A dataset was constructed by filtering
out all absence points and including only variables that were both
biologically (based on natural history observations) and statistically
important (based on univariate regressions for each species) for the
presence of each species (OM - variables related to rocks and geography;
ON - variables related to vegetation and shrubs in particular; OL -
variables related to vegetation, elevation) (variables highlighted in
blue on Table S2). This dataset was subjected to a PCA analysis to
reduce dimensionality and to visualize the variation along eigenvectors.
The PCA revealed the separation of broad niches along different axes of
rocks, vegetation, elevation, and NDVI. Variables related to vegetation
(perc\_vegetation, height\_tallgrass\_reeds, height\_shrub, average NDVI
in summer) and rocks (perc\_rocks) loaded onto PC1, while PC2 and PC3
were driven byloaded on by only one variable, elevation and variance in
winter NDVI respectively (cos2 \textgreater{}0.5).

Since three PC axes explained 55\% of the total variance in the dataset,
only three dimensions of the PC transformed points were considered for
further analysis of niche overlaps and niche width. We used the
NicheRover package on R using a bayesian based probabilistic estimation
of niche space, overlaps, and width (Swanson et al. 2015). Directional
overlaps were computed by a specified number of Monte Carlo draws from
the parameter list (\selectlanguage{greek}α \selectlanguage{english}= 0.95) that indicated the probability of finding
`species A' in the niche space of `species B .'The niche width was
obtainedcalculated by calculating the posterior distribution of niche
size by species (niche size for every posterior sample drawn) (Swanson
et al. 2015). Niche estimation following the methods described was
performed at two scales (Broad scale: All of Ladakh, three species
representing three niches) and (Regional scales: North-West Ladakh and
South-East Ladakh representing five niches).

\textbf{Results}

\textbf{Comparison of niche overlaps and niche width between species at
broad and regional scales}

\emph{Broad scale:} At the larger spatial scale of Ladakh, the
elliptical projections of niche space indicated that the three species
separated best along the first two dimensions, albeit with significant
overlaps (Figure 4). The niche overlap analysis suggested that OL was
very likely to be found in the microhabitats that are inhabited
by\emph{ON} (mean = 40 \%) and \emph{OM} (mean= 50\%) (Figure
5)\emph{.}Both OM and ON were less likely to be found in microhabitats
inhabited by other species (mean \textless{} 15\%) (Figure 5). The
n-dimensional niche width constructed was different across species
(Pr(\textgreater{}F) = \textless{}2e-16 ***, F value = 6541, df = 2),
with OL having the smallest niche, OM having an intermediate-sized
niche, and ON having the largest niche as evidenced by Turkey HSD tests
(Figure 6).

\emph{Regional scales:} When the species were subdivided based on
location (OL South-East Ladakh, ON North-West Ladakh, ON South-East
Ladakh, OM North-West Ladakh, OM South-East Ladakh), elliptical
projection of niche spaces indicated significant overlaps between niches
of ON and OL in the South-East, the niche of OM in the South-East and
the North-West, and ON in North-West Ladakh being distinct with no
significant overlaps with any other niche (Figure 7). The niche overlap
analysis suggested that all species across geographic areas had unique
niches and were unlikely to be found in microhabitats of other species
(Figure 8). The niche width of species across geographic areas was
different at the regional scale (Pr(\textgreater{}F) = \textless{}2e-16
***, F value = 9570, df = 4) and revealed contrasting patterns to those
recovered at the broad scale. Tukey HSD tests suggested that the niche
width of OM in the North-West was the largest, followed by ON and OL in
the South-East, having the second and third largest niches, respectively
(Figure 9). The niche width of ON and OM in the South-East did not
differ (diff = 2.68, p adj = 0.089) and were among the smallest niches
in comparison (Figure 8).

\emph{\textbf{Ochotona macrotis}}

\emph{Univariate models} \textbf{-} Comparison of univariate models at
different scales prior to model building revealed that the variables low
percentage cover of vegetation, greater percentage of rocks, greater
percentage of large sized rocks, and more number of large-sized rocks
were important for predicting the presence of OM across all scales
(Table C1). It also indicated unique variables that were important at
each spatial scale (Table C1):

\begin{itemize}
\tightlist
\item
  At the Ladakh scale, presence was associated with a greater slope.
\item
  At the South-East Ladakh scale, presence was associated with more
  extensive coverage of class 2 rocks (+).
\item
  At the North-West Ladakh scale, presence was associated with
  consistently lower NDVI.
\end{itemize}

All the univariate models run are presented here (Table S3, Table S4,
Table S5).

Multivariate models - At each spatial scale, models constructed were
subjected to a stepwise regression analysis to pick the best additive
models using different methods. The purposeful modeling approach (Model
2) consistently produced the best models across spatial scales. It
included variables significantly associated with presence in univariate
regressions at regional scales, such as the percentage cover of
large-sized rocks and the number of large-sized rocks (Table 1). At the
Ladakh spatial scale, the presence of OM was driven by a higher cover of
large rocks, a greater number of large-sized rocks, and lower NDVI in
summer (Table 1). At the South-East Ladakh spatial scale, the occurrence
was primarily driven by a higher cover of large rocks, a greater number
of large-sized rocks, higher elevation, lower cover of shrubs, and
consistently low NDVI in winter (Table 1). At the North-West Ladakh
scale, the occurrence was driven by many variables, with no variables
related to rocks, vegetation, or geographic features strongly
influencing the presence of the species (Table 1).

Ochotona nubrica

Univariate models - Comparison of univariate models at different scales
prior to model building revealed that more relative percentage of bush
cover, more number of shrubs, a greater height of shrubs, and large
mature stands of tall grass and reeds were significant for predicting
the presence of ON across all scales (Table C2). At the larger Ladakh
scale, variables related to rocks (negatively) and NDVI across seasons
(positively) were important in explaining the presence of\emph{ON}
(Table C2). In the North-West, presence was associated with NDVI
(positively) and reflected greener habitats that the species occupies.
Details of univariate models run can be found in Supplementary material
(Table S6, Table S7, Table S8).

\emph{\textbf{Multivariate models} -} At the Ladakh scale, the
purposeful model (Model 2) produced the best results; the sub-modeling
approach (Model 3) was best for the South-East, while at the West-Ladakh
scale, all three modeling approaches produced equally good results
(Table 2). At the Ladakh spatial scale, variables that predicted the
absence of the species included cover and number of large-sized rocks,
and variables that predicted the presence of the species, such as the
cover of shrubs and NDVI, were important (Table 2). In the South-East,
only variables related to vegetation and mainly shrubs were important in
explaining the presence of the species (Table 2). In the North-West,
different NDVI variables (for different modeling approaches) and mature
stands of tall grass and reeds were necessary to explain the species'
presence, although the species was restricted to large mature stands of
Sea buckthorn (Table 2).

Ochotona ladacensis

Univariate models - At the Ladakh spatial scale, many variables related
to rocks (lower percentage cover of rocks, lower percentage of
large-sized rocks, lower number of large-sized rocks), heights of plants
(low herb and forb height, tall cushion plants) were important in
determining the presence of the species (Table C3). At the South-East
Ladakh scale, only topographic variables such as elevation and slope
significantly predicted presence (Table C3). All univariate models run
are presented in Supplementary information (Table S9, Table S10).

Multivariate models - At both spatial scales, the purposeful modeling
approach best predicted the occurrence of the species (Table 3).
Variables that strongly predicted the absence of the species and those
that predicted their presence were important in explaining where the
species was most likely to be found. At both scales, the presence of OL
was associated with a lower cover of rocks of different sizes, a lower
number of large-sized rocks, a higher relative percentage of cushion
plants, lower heights of shortgrass-sedges and shrubs, high elevation,
and a more prominent aspect.

\textbf{Discussion}

The study set out to describe ecological niche spaces of three sympatric
species of pika (\emph{Ochotona macrotis:} OM, \emph{Ochotona
nubrica:}ON, and \emph{Ochotona ladacensis:} OL), examine niche overlaps
and widths in the context of ecological specialization under
interspecific competition. In addition, it aimed to understand
ecological factors that support the presence of these species at
different spatial scales in the Ladakh Trans-Himalayas (3100m - 5200m)
to inform conservation policy making.

\textbf{Comparison of niche overlaps and niche width between species at
broad and regional scales}

At the broad scale (All of Ladakh, three species, three niches), OL
shared a substantial portion of its niche (\textgreater{}\%40) with OM
and ON but had a small niche width given that it was elevational
restricted. ON and OM, however, shared very little of their niche with
other species and had large niche widths, given that they were found in
a diversity of microhabitats.

At the regional scale (South-East and North-West Ladakh, three species,
five niches), species shared very little of their niche with others,
suggesting that at smaller spatial scales, all species were unique in
the microhabitats they utilized, mirroring findings in Australian
songbirds (Harm\selectlanguage{ngerman}á\selectlanguage{polish}č\selectlanguage{english}kov\selectlanguage{ngerman}á et al. 2019) and sharp-nosed snakes (Corrêa
Nogueira et al. 2019). OM had the largest niche width at the broad scale
(generalist) and some of the smallest niche widths in the South-East
(specialists). This is likely to have occurred due to natural variation
in topography and landscape features across sites, with the species
being found in a wide variety of microhabitats in the North-West
compared to the South-East. ON had intermediate niche width at the broad
scale, while at the regional scale, it had intermediate (North-West) and
small (South-East) niche widths. This is reflective of the exclusive
association of the species with tall mature stands of \emph{Caragana
sp.} exclusively in South-East Ladakh and with \emph{Hippophae sp.} in
the North-West, but inhabiting two very different microhabitats at the
broad scale (also explains slight deviation from multivariate normal
distributions in density plots at the broad scale). On the other hand,
OL had the smallest niche width at the broad scale (high elevation
specialist) and an intermediate niche width at regional scales. The
species is restricted in elevational distribution but occurs in
different microhabitats such as alpine meadows/grasslands and Caragana
scrublands at these elevations.

\textbf{Identifying important ecological factors drive presence of
different species -} Ecological factors contributing to different
species' niches were used to model presence. At different spatial
scales, different variables were significant in predicting the presence
of a species. Such scale-dependent phenomena have been poorly explored
in describing niche spaces and overlaps between species (Corrêa Nogueira
et al. 2019).

At the Ladakh (broad) scale, OM was found in places with a good cover of
large-sized rocks, a high number of large-sized rocks, and a low cover
of tallgrass-reeds (Table 4).

The model suggested that presence was also driven by elevation and cover
of rocks of class size two but not those of class size three (Table 4).
This is likely an artifact of our sampling design (species found in the
elevation bands 3000m-6000m) or a problem of finely dissecting
ecological variables which doesn't majorly affect our understanding of
how species partition microhabitats. It is worth noting that low cover
of tallgrass-reeds was the only variable related to vegetation that
appeared in the best model. This suggests that refuges are more critical
than resources for microhabitat selection for the species and agrees
with the literature on other rock-dwelling species \emph{O.
roylei}(Bhattacharyya et al. 2015) and \emph{O. princeps} (Hall et al.
2016)\emph{.} However, it remains to be explored if refuges associated
with large-sized rocks offer thermoregulatory benefits to animals across
seasons, as shown for \emph{O. princeps}(Hall et al. 2016).

At the same scale, ON was likely to occur in areas with high shrub
cover, a higher number of shrubs, and a greater height of shrubs and
other vegetation while actively avoiding areas with high rock cover
(Table 4). Our field observations indicated that ON exclusively
associates with areas with mature stands of scrub
vegetation\emph{Caragana} sp. at high elevations and sea buckthorn
(\emph{Hippophae rhamnoides} ) at low elevations. While there are
reports of the species using rocks as refuges (Smith et al. 2010, Wilson
and Mittermeier 2016), we failed to observe such behavior and documented
the use of forms instead (unpublished data).

OL was likely to occur at high elevations characterized by short plants
(stunted growth) on alpine meadows/grasslands, low-lying\emph{Caragana}
, and cushion plants while avoiding areas with a high cover of rocks
(Table 6).

This study describes, for the first time, niche spaces and overlaps
between three sympatric species of pika from Asia. It illustrates that
sympatric pika communities in Ladakh, India, segregate into different
niches along rocks, vegetation, and elevation axes. There is a lot to
gain from measuring niche space, overlaps, and widths at different
spatial scales since it helps us understand how competition shapes
niches and communities. This also provides opportunities for
conservation planning for pikas in Ladakh's rapidly developing
landscape.

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\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 1 : Typical microhabitats of different species of pikas at
Ladakh.

\emph{Ochotona macrotis} microhabitats\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\emph{Ochotona ladacensis} microhabitats\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\emph{Ochotona nubrica} microhabitats

Figure 2: Map of regions sampled in Trans-Himalayas of Ladakh UT (blue
polygons).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Figure 3 : Elevational distribution of presence and absence plots laid
in the study across species. OL - Ochotona ladacensis, OM - Ochotona
macrotis, ON - Ochotona nubrica.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Figure 4 : Ten random elliptical projections of niche space for each
species and pair of PC transformed axes (elliptical plots). Also
displayed are one-dimensional density plots (lines) and two-dimensional
scatterplots. Smoothed histograms (density plots) indicate a small
deviation from normality for the species \emph{O.nubrica} and \emph{O.
ladacensis.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

Figure 5. Posterior distribution of the probabilistic niche overlap
metric (\%) for a specified niche space of 95\%. The figure represents
the directional probability of finding species 1 (row) in the niche of
species 2 (column). The posterior means and 95\% credible intervals are
displayed in the figure.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

Figure 6 : Niche width of species (in this case, a 3 dimensional volume
- first 3 PC axes ) discerened from presence points of each species.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

Figure 7 : Ten random elliptical projections of niche space for each
species subdivided by location and pair of PC transformed axes
(elliptical plots). Also displayed are one-dimensional density plots
(lines) and two-dimensional scatterplots. Smoothed histograms (density
plots) indicate a small deviation from normality for the species

\emph{O. ladacensis.}

Legend : OL - \emph{O.ladacensis} , ON - \emph{O.nubrica} , OM
-\emph{O.macrotis} .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

Figure 8: Posterior distribution of the probabilistic niche overlap
metric (\%) for a specified niche space of 95\%. The figure represents
the directional probability of finding species 1 (row) in the niche of
species 2 (column). The posterior means and 95\% credible intervals are
displayed in the figure.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image13/image13}

\end{center}
\end{figure}

Figure 9 : Niche width of species subdivided by location (in this case,
a 3 dimensional volume - first 3 PC axes ) discerened from presence
points of each species in different areas of Ladakh.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image14/image14}

\end{center}
\end{figure}

Table 1: Best multivariate models found using the StepAIC function
across different modelling methods (see Model 1, Model 2 and Model 3 in
Methods) and spatial scales for \emph{Ochotona macrotis} . Within each
spatial level, the best model and the approach has been highlighted.

\textbf{Spatial Level: Ladakh}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Modelling framework}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Best model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{AIC}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Top contributors to model}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Statistical model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
macrotis \textasciitilde{} perc\_rocks + num\_4rocks + DEM.elevation + x
\selectlanguage{english}(NDVI\_Winter)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
94.25\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Purposeful model}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
macrotis \textasciitilde{} perc\_2rocks + perc\_4rocks + num\_4rocks +
perc\_herb\_forb + perc\_shrub + height\_tallgrass\_reeds + x
\selectlanguage{english}(NDVI\_Winter) + x \selectlanguage{english}(NDVI\_Summer) + stdev (NDVI\_Summer)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
89.41\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
perc\_4rocks num\_4rocks x \selectlanguage{english}(NDVI\_Summer)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Sub-model}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
macrotis \textasciitilde{} perc\_2rocks + perc\_4rocks + num\_4rocks +
perc\_herb\_forb + perc\_shrub + perc\_tallgrass\_reeds +
height\_tallgrass\_reeds + x \selectlanguage{english}(NDVI\_Winter) + x \selectlanguage{english}(NDVI\_Summer) + stdev
(NDVI\_Summer)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
88.5\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
perc\_4rocks num\_4rocks x \selectlanguage{english}(NDVI\_Summer)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: South-East Ladakh}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: South-East Ladakh}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: South-East Ladakh}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: South-East Ladakh}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Modelling framework}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Best model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{AIC}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Top contributors to model}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Statistical model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
macrotis \textasciitilde{} perc\_3rocks + perc\_4rocks +
DEM.elevation\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
29.74\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Purposeful model}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
macrotis \textasciitilde{} perc\_rocks + perc\_3rocks + perc\_4rocks +
num\_4rocks + perc\_vegetation + perc\_shrub + stdev (NDVI\_Winter)+
DEM.elevation\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Perc\_4rocks num\_4rocks DEM.elevation Perc\_shrub stdev
(NDVI\_Winter)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Sub-model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
macrotis \textasciitilde{} perc\_3rocks + perc\_4rocks +
DEM.elevation\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
29.74\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: North-West Ladakh}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: North-West Ladakh}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: North-West Ladakh}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: North-West Ladakh}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Modelling framework}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Best model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{AIC}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Top contributors to model}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Statistical model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
macrotis \textasciitilde{} num\_4rocks + perc\_4rocks + x
\selectlanguage{english}(NDVI\_Winter)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
45.94\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Purposeful model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
macrotis \textasciitilde{} perc\_bareground + perc\_4rocks + num\_3rocks
+ num\_4rocks + perc\_vegetation + perc\_shrub + perc\_herb\_forb +
perc\_tallgrass\_reeds + height\_shrub + height\_herb\_forb +
height\_tallgrass\_reeds + num\_tallgrass\_reeds +
num\_shortgrass\_sedge + num\_3rocks + perc\_bareground + x
\selectlanguage{english}(NDVI\_Winter) + x \selectlanguage{english}(NDVI\_Summer) + stdev (NDVI\_Summer) + x \selectlanguage{english}(\selectlanguage{greek}Δ \selectlanguage{english}NDVI) +
DEM.elevation\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
38\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
All variables are equally important\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Sub-model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
macrotis \textasciitilde{} num\_4rocks + perc\_4rocks +
perc\_tallgrass\_reeds + num\_shortgrass\_sedge Average.Delta.NDVI\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
45.5\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 2: Best multivariate models found using the StepAIC function
across different modelling methods (see Model 1, Model 2 and Model 3 in
Methods) and spatial scales for \emph{Ochtona nubrica} . Within each
spatial level, the best model and the approach has been highlighted.

\textbf{Spatial Level: Ladakh}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Modelling framework}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Best model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{AIC}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Top contributors to model}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Statistical model}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
nubrica \textasciitilde{} perc\_vegetation + perc\_rocks + num\_3rocks +
num\_4rocks + num\_shortgrass\_sedge + perc\_herb\_forb + perc\_shrub +
height\_tallgrass\_reeds + height\_shrub + height\_trees + Slope + x
\selectlanguage{english}(NDVI\_Winter)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
26\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Purposeful model}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
nubrica \textasciitilde{} perc\_vegetation + perc\_3rocks + perc\_4rocks
+ perc\_herb\_forb + perc\_shrub + height\_shrub + x \selectlanguage{english}(NDVI\_Winter) + x
\selectlanguage{english}(NDVI\_Summer)+ x \selectlanguage{english}(\selectlanguage{greek}Δ \selectlanguage{english}NDVI)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
All variables are equally important besides variables related to
rocks.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Sub-model}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
nubrica \textasciitilde{} perc\_3rocks + perc\_4rocks + perc\_herb\_forb
+ perc\_shrub + x \selectlanguage{english}(NDVI\_Summer) + num\_shrub +
height\_shortgrass\_sedge + height\_tallgrass\_reeds +
height\_shrub\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
All variables are equally important besides x \selectlanguage{english}(NDVI\_Summer)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: Changthang}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: Changthang}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: Changthang}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: Changthang}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Modelling framework}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Best model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{AIC}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Top contributors to model}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Statistical model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
nubrica \textasciitilde{} num\_shrub + perc\_herb\_forb + perc\_shrub +
height\_tallgrass\_reeds + height\_shrub + Average.NDVI\_Nov\_Dec\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
20.12\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Purposeful model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
nubrica \textasciitilde{} perc\_bareground + perc\_3rocks + perc\_shrub
+ height\_herb\_forb + height\_shrub + Average.NDVI\_Nov\_Dec\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Sub-model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
nubrica \textasciitilde{} perc\_vegetation + perc\_shrub + x
\selectlanguage{english}(NDVI\_Winter)+ height\_shrub\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
All variables are equally important\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: West Ladakh}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: West Ladakh}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: West Ladakh}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: West Ladakh}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Modelling framework}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Best model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{AIC}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Top contributors to model}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Statistical model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
nubrica \textasciitilde{} height\_tallgrass\_reeds + stdev (NDVI)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
All variables are equally important\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Purposeful model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
nubrica \textasciitilde{} height\_tallgrass\_reeds + x \selectlanguage{english}(\selectlanguage{greek}Δ \selectlanguage{english}NDVI)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
All variables are equally important\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Sub-model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
nubrica \textasciitilde{} x \selectlanguage{english}(NDVI\_Summer) +
height\_tallgrass\_reeds\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
All variables are equally important\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 3: Best multivariate models found using the StepAIC function
across different modelling methods (see Model 1, Model 2 and Model 3 in
Methods) and spatial scales for \emph{Ochotona ladacensis} . Within each
spatial level, the best model and the approach has been highlighted.

\textbf{Spatial Level: Ladakh}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Modelling framework}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Best model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{AIC}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Top contributors to model}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Statistical model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
ladacensis \textasciitilde{} num\_4rocks + DEM.elevation\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
80.95\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Purposeful model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
ladacensis \textasciitilde{} perc\_bareground + perc\_vegetation +
perc\_rocks + perc\_1rocks + perc\_4rocks + num\_4rocks + perc\_trees +
height\_shortgrass\_sedge + DEM.elevation + Aspect + x
\selectlanguage{english}(NDVI\_Winter)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
63.99\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
All variables equally important\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Sub-model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
ladacensis \textasciitilde{} perc\_1rocks + num\_4rocks + height\_shrub
+ DEM.elevation + Aspect\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
73.37\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: Changthang}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: Changthang}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: Changthang}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Spatial Level: Changthang}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Modelling framework}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Best model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{AIC}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Top contributors to model}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Statistical model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
ladacensis \textasciitilde{} DEM.elevation + Slope\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
89.94\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Purposeful model}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
ladacensis \textasciitilde{} perc\_1rocks + perc\_4rocks + height\_shrub
+ DEM.elevation + Slope\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
71.93\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
perc\_1rocks perc\_4rocks DEM.elevation Slope\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Sub-model}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
ladacensis \textasciitilde{} perc\_rocks + perc\_1rocks + perc\_4rocks +
perc\_cushionplant + height\_shrub\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
79.5\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

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

