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\begin{document}

\title{Dhole pack size variation: Assessing effect of Prey availability and
Apex predator}



\author[1]{Aishwarya Bhandari}%
\author[1]{Pallavi Ghaskadbi}%
\author[1]{Parag  Nigam}%
\author[2]{Bilal Habib}%
\affil[1]{Affiliation not available}%
\affil[2]{Wildlife Institute of India}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Ecosystems wherein social and solitary predators share space, complex
asymmetric intraguild interactions actively shape the group size of
social carnivores. Intense intraguild predation has been known to result
in reduced group size and low recruitment rates in subordinate social
carnivores. In South and South-east Asia, Dhole, tiger and leopard form
a widely distributed sympatric guild of large carnivores. In this paper
we attempted to understand the pack size dynamics of a subordinate
social predator, the dhole, by investigating factors underlying pack
size variation at two neighbouring sites. We further evaluated our
local-scale patterns of pack size variation at a larger scale by doing a
distribution-wide assessment of pack size across dhole ranging
countries. Across study sites, we found an inverse relationship between
the local abundance of a top predator and group size of a subordinate
predator while accounting for variability in resources and habitat
heterogeneity. Tiger density was low (0.71/100 Km2) at the site having
large dhole packs (16.8 \selectlanguage{ngerman}± 3.1) whereas, a relatively smaller average
dhole pack size (6.4 ± 1.3) was observed in a higher tiger density
(5.67/100 Km2) area. Our results on distribution-wide assessment are
concurrent with local-scale results, showing a positive association
between prey abundance and pack size and a negative association between
tiger densities and dhole pack size. Our study takes us one step closer
to trying to answer the age-old question of what drives the pack size of
social predators in a multi-predator system. Linking behaviour to
population dynamics and carnivore interactions is another highlight of
the study. Often helpful while optimizing conservation triage and
formulation of management implications like recovery and translocations.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Introduction}

In the past few decades, there has been a shift in wildlife biology
studies from single species targeted approach to ecosystem conservation
approach (Linnell \& Strand, 2000). This holistic approach reveals how
interspecific interactions can alter community structures and ecosystem
functioning (Ford \& Goheen, 2015). One such interaction is intraguild
competition among large carnivores that shape the predatory guild
(Palomares \& Caro, 1999). Often considered as keystone species in the
terrestrial ecosystems (Caro \& O'Doherty,1999), ecological effects of
large carnivores extend down to herbivores and plants (Ritchie \&
Johnson, 2009) thereby structuring ecosystems along multiple food web
pathways. Therefore, safeguarding of viable large carnivore populations
is essential for ecosystem equilibrium, that cannot be ensured without
understanding their demographic responses to each other.

Over the years, it has been established that competition among predators
can be direct or indirect (Case \& Gilpin, 1974; Macdonald, 1983; Crooks
\& Soule, 1999; Ritchie \& Johnson, 2009; Letnic, Ritchie, \& Dickman,
2012). The indirect form is termed as exploitative competition (Case \&
Gilpin, 1974). Wherein resources are harvested disproportionately by one
predator and are not available for other competing predators (Vance,
1984). This type of interaction is mostly unidirectional with larger
body sized carnivores i.e. apex predators (Ordiz, Bischof, \& Swenson,
2013), dominating the guild. Evolutionarily, competitively subordinate
carnivores have adapted to exploitative competition by opting for
differential life-history strategies like group living, reduced overlap
in diet, Spatio-temporal activity and habitat use (Creel \& Creel,1996;
Durant, 2000; Creel, 2001; Durant, 2002).

On the other hand, interference interaction is a rather direct form of
competition in the predatory guild (Vance,1984). Some manifestations of
interference competition are interspecific territoriality,
kleptoparasitism and direct killing (Linnell \& Strand, 2000). However,
interference competition is not easy to demonstrate, because it is
multifaceted and involves an array of factors acting along, such as
anthropogenic disturbance, alterations in community structure of prey
and other predators and the overall productivity of ecosystems
(Greenville, Wardle, Tamayo, \& Dickman, 2014; Newsome \& Ripple, 2015;
Swanson et al., 2014). Therefore, there is a dearth of theoretic and
practical understanding of interference competition because of the lack
of replicability of such 'natural experiments' (Linnell \& Strand,
2000).

Most of our understanding on intraguild competition comes from studies
on exploitative competition, although interference competition is
critical in multi-predator systems and is always functioning in the
background (Periquet, Fritz, \& Revilla, 2015). Shreds of evidence from
classic ecology studies indicate that subordinate predator experience
low recruitment rates and even face extirpations in a high apex predator
density scenario (Carbyn, Armbruster, \& Mamo, 1994; Clark, 1994;
Lindstr\selectlanguage{ngerman}öm, Brainerd, Helldin, \& Overskaug, 1995; Henke \& Bryant,
1999). However, an inverse pattern is observed when interference
competition is removed. A recent continent-wide review shows expansion
of golden jackals (\emph{Canis aureus} ) as a response to grey wolf
(\emph{Canis lupus} ) exterminations in Europe due to persecution by
humans. (Krofel, Giannatos, Cirovic, Stoyanov, \& Newsome, 2017).
Ecosystems wherein social and solitary predators share space, intraguild
competition often shape trends of group sizes in social carnivores. The
group size of subordinate predators such as African wild dogs (Lycaon
pictus) (Creel \& Creel,1996; Creel \& Creel, 1998), Spotted hyenas
(Crocuta crocuta) (M'soka, Creel, Becker, \& Droge, 2016) and
cooperative breeding mongooses (\emph{Suricata suricata} )
(Clutton\selectlanguage{english}-Brock et al., 1999) have been studied to be inversely related
to lion densities, over temporal and spatial scales.

One such sympatric guild of solitary and social carnivores, found in
South-east Asian forests is of tiger (\emph{Panthera tigris} ), dhole
(\emph{Cuon alpinus} ) and leopard (\emph{Panthera pardus} ). Tigers are
considered to be top predators whereas dhole and leopard are
intermediate predators, forming an asymmetric guild (Steinmetz,
Seuaturien, \& Chutipong, 2013). In the Indian subcontinent dholes have
been widely studied along with tigers and leopards, to understand
sympatric interactions among the three carnivores (Acharya, 2007;
Johnsingh,1992; Karanth \& Sunquist, 2000; Wang \& Macdonald, 2009;
Wegge, Odden, Pokharel, \& Storaas, 2009; Steinmetz, Seuaturien, \&,
Chutipong, 2013; Rayan \& Linkie, 2016). Competition between these
carnivores is likely because of the high overlap in diet spectrum
(Karanth \& Sunquist, 1995). Prey rich forests facilitate sympatry
between the carnivores (Karanth \& Sunquist, 2000; Wang \& Macdonald,
2009; Karanth, 2017). Studies in sub-optimal habitat conditions with
scarce resources have shown evidence of intraguild predation among the
sympatric carnivores (Steinmetz, Seuaturien, \&, Chutipong, 2013; Rayan
\& Linkie, 2016). Conversely, tiger depleted systems have shown a
significant increase in dhole site occupancy (Steinmetz, Seuaturien, \&
Chutipong, 2013; Rasphone, Kery, Kamler, \& Macdonald, 2019).

Pack size, however, is a vital aspect and considered to be a function of
population size in social predators (Fernandez et al., 2020) but it
remains unaddressed for the dhole. So far, we have no long-term studies
to assess and understand various ecological factors that determine group
size dynamics of dhole, as seen in case of other subordinate social
predators (Periquet, Fritz, \& Revilla, 2014; Green, Farr, Holekamp,
Strauss \& Zipkin, 2019).

We observed a significant variation in pack size of dholes at the two
neighbouring protected areas having similar ecological settings, Tadoba
Andhari Tiger Reserve and Navegaon Nagzira Tiger Reserve in the Central
Indian Landscape, Maharashtra, India and attempted to investigate
factors underlying this variation in pack size of dholes. Group size
variation is a crucial characteristic of carnivore sociality
(Macdonald,1983) and is an attribute of differential ecological settings
over temporal and spatial scale (Markham, Gesquiere, Alberts, \&
Altmann, 2015). Largely governed by co-predator density and prey
abundance (Gusset \& Macdonald, 2010). Furthermore, availability of
habitat, topography and habitat features elicit prey distribution and
encounter rate at a given place and time (White \& Garrott, 2005;
Fedriani, Fuller, Sauvajot, \& York, 2000).

After investigating factors linked with pack size variation of dholes at
the two reserves, we further elucidate our local scale patterns at a
wider scale by doing a distribution-wide assessment of pack size across
dhole ranging countries. 1. Hypothesis: High apex predator density
negatively affects pack size of subordinate carnivores (Groom, Lannas,
\& Jackson, 2017). Prediction: Areas with higher tiger density will show
smaller pack sizes and vice-versa. 2. Hypothesis: When tiger density are
low pack sizes are determined by prey abundance. Prediction: Dhole pack
size will positively correlate to higher prey abundance. 3. Hypothesis:
Terrain ruggedness influences pack size as dholes are cursorial
predators (White \& Garrott, 2005). Prediction: Areas with high
ruggedness will correspond to low pack sizes and vice-versa.

\textbf{Methods and Materials:}

\textbf{2.1. STUDY AREA}

The study was primarily conducted at two neighbouring sites in the
Eastern Vidarbha Tiger Landscape within the greater Central Indian Tiger
Landscape. The Two sites were; the Tadoba Andhari Tiger Reserve (TATR)
and Navegaon Nagzira Tiger Reserve (NNTR) (Figure 1). TATR (19.95428 E
to 20.51695 E and 79.12749 N to 79.73494 N) is 625 km2 and NNTR
(20.86209 E to 21.44738 E and 79.69802 N to 80.39064 N) is 656 km2
(core) in area. Both the study sites are in the Deccan Plateau zone
according to the biogeographic classification of India (Rodgers \&
Panwar, 1988) and are located at a distance of \textasciitilde{}85 km
from each other. The study sites experience subtropical climate with
three distinct seasons- summer, monsoon, and winter. The Reserves
receive southwest monsoons with rainfall (1100-1500 mm) persisting from
June to September. The Forest type is Southern tropical dry deciduous
(Champion \& Seth, 1968). Teak (\emph{Tectona grandis)} is the dominant
species followed by \emph{Terminalia tomentosa} and \emph{Lagerstroemia
parviflora} in the study sites. The terrain type is mostly plain with
shallow valleys and rounded hills. Tiger, leopard and dhole form the
major predatory guild in the study sites and chital (\emph{Axis axis)} ,
sambar (\emph{Rusa unicolor)} , nilgai (\emph{Boselaphus tragocamelus)}
, wild pig (\emph{Sus scrofa} ), gaur (\emph{Bos gaurus)} , barking deer
(\emph{Muntiacus muntjac)} are the major prey species (Dhanwatey et al.,
2013).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1. Map showing Tadoba Andhari Tiger Reserve and Navegaon
NagziraTiger Reserve in Eastern Vidarbha Landscape. Inset showing study
area location in the map of India.}

\textbf{2.2} .) \textbf{Field and Analytical method: Dhole pack size
variation across study sites}

In field studies individual identification of dholes is challenging
because of their uniform pelage pattern (Modi, Habib, Ghaskadbi, Nigam,
\& Mondol, 2019), therefore we adhered to pack identification on the
basis of activity centres (Stansbury et al., 2014). We surveyed study
sites through camera trapping and identified activity centres on the
basis of pack's activity hotspots and concentrated indirect signs. The
understnading of movement patterns from telemetry study at our study
sites suggests that the core home range/ activity centres for dhole pack
ranges from 1 km\textsuperscript{2} to 11 km\textsuperscript{2} i.e. the
area exclusive to a pack's territory (Ghaskadbi et al.
Unpublished).\textsuperscript{.} Therefore, based on our observations of
activity hotspots, sightings of packs were only recorded at their
activity hotspots. The sightings at overlapping pack boundaries were not
included to avoided confusion between the packs.

\textbf{2.3. a) Field and Analytical method: Variation in prey
composition and density across study sites}

Line transect based distance sampling (Buckland et al., 2001) was used
to estimate prey densities at the two sites. Line transects were laid in
a stratified random framework to ensure spatial coverage of all
vegetation types. All transects were of 2 km length. Survey effort of
950 km and 984 km was put in TATR and NNTR, respectively. Data for
species, group size and composition, GPS (global positioning system)
location of every observation, bearing of the animal using compass and
angular sighting distance using laser range finders, was recorded
whenever sightings were made online transects.

\textbf{b) Estimation of ungulate species across study sites}

Individual density of all the species was calculated using Distance
program version 6.2 (Thomas et al., 2010). We first examined the data
from both sites for each species separately. Following this, the species
observations at distances beyond which sightings were almost none was
dropped or observations were binned to achieve model fit. Akaike
Information Criterion and goodness-of-fit (GOF-p) tests were used to
judge and the fit of the model. Based on the selected model, individual
density (\(\ \hat{\text{Di\ }}\)) and estimates of group density
(\(\hat{\text{Dg}}\)) were derived for each species.

\textbf{2.4. a) Field and Analytical method: Variation in tiger density
across study sites}

Capture-recapture based camera trapping was done to estimate densities
of carnivores following standard protocols (Karanth \& Nichols., 1998).
A grid size of 2 km\selectlanguage{ngerman}² was used for camera trap placement. Based on the
sign survey, camera traps were stationed on both sides of the trails, at
junctions and water sources to maximize photo captures (Karanth \&
Nichols., 1998). Distance between the camera stations was between 1 km
to 1.5 km to ensure spatial coverage in the sampling area and capture
probability of the study population (Pollock, Nichols, Brownie, \&
Hines, 1990). A closing period of 30 days (n= 9144 in TATR and n= 13440
in NNTR Trap nights) was kept to ensure demographic closure.

\textbf{b) Density estimation of Tiger across study sites:}

Using photo captures, tiger density was estimated in SECR framework
using maximum likelihood approach (Borchers \& Efford, 2008). The
analysis was carried out in program R (3.5.3) (R Core Team, 2013) using
package ''secr'' (3.2.1) (Efford \& Efford, 2019). In SECR, we laid a
buffer of 7 km based on spatial scale detection (\selectlanguage{greek}σ\selectlanguage{english}) around our camera
trap matrix. The best-fit model based on the lowest AIC value was then
selected for density (\(\hat{D}\)), capture probability at
activity center (g\selectlanguage{greek}ο\selectlanguage{english}), and spatial scale of detection (\selectlanguage{greek}σ\selectlanguage{english}).

\textbf{2.5. a) Data sources for distribution-wide assessment of dhole
pack size:}

Through google scholar we searched for scientific literature on pack
size of dholes, using the keywords ``Cuon alpinus'', ``Dhole'',
``Average'', ``Mean'', ``Pack-size''. Our search resulted in 34
scientific assessments from 1973 to 2018 that had reported average pack
size of dholes. These 34 assessments belonged to 24 unique protected
areas across dhole ranging countries in South and South-east Asia. 18 of
these unique sites were also a part of the recently published dhole diet
review (Srivathsa, Sharma, \& Oli, 2020). Subsequently, snowball
sampling approach was used (Handcock \& Gile, 2011), by using dhole pack
size as baseline information from the previously conducted assessments.
Literature cited within these assessments were referred to collate data
on tiger density along with prey density (of the closest or same
assessment year) and size of the protected area. We obtained data on the
topography of 24 protected areas through google earth engine (Gorelick
et a., 2017).

b) \textbf{Analytical methods:}

We used generalized linear models to examine correlates of dhole pack
size reported from 24 unique sites across dhole distribution range. We
used only those studies (n=29) for which data on all the predictor
variables were available i.e., tiger density and ungulate density, size
of the protected area (PA), elevational heterogeneity and terrain
ruggedness of the PA. We checked for correlations among predictors and
dropped the correlated ones (r \textgreater{} 0.6), prior to analysis.
After screening for normal distribution of response variable, we used
''gaussian'' family for the analysis. We tested for model parameters
based on our hypothesis, and compared them to null model (Intercept
only). Model fits were compared using Akaike's Information Criterion
corrected (AIC\textsubscript{C}), and the effect of parameters was
gauged based on direction and statistical significance of corresponding
\selectlanguage{greek}β-\selectlanguage{english}coefficients. We used ''MuMIn'' package for model selection and
averaging. Model selection was based on difference between AIC models,
(\selectlanguage{greek}Δ\selectlanguage{english}AIC \textless{} 2). Further, model selection was done using Royall's
1/8 strength of evidence and 95\% cumulative weight criteria. Model
averaging was carried out for parameters based on top model selection.
All analyses were performed in program R (R Development Core Team,
2014).

\textbf{Results:}

\textbf{3.1. Pack size variation}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

We identified seven packs from TATR and five packs form NNTR based on
their exclusive activity hotspots. Number of individuals in a pack
ranged from 7 to 12 for TATR Packs and 10 to 28 for NNTR Packs. The
reported average pack size was 6.4 (1.3) and 16.8 (3.1) for TATR and
NNTR, respectively. A significant difference was found between the pack
size of TATR and NNTR (t = -3.05, p-value = 0.02) (Figure 2).

\textbf{Figure 2. Comparison of dhole pack size from TATR and NNTR}

\textbf{3.2. Prey composition and density across study sites:}

Total prey density per 100 km\selectlanguage{ngerman}² was reported as 16.94 in NNTR and 19.28
in TATR. Major prey species in both the study sites were chital, sambar,
nilgai, wild pig, gaur, and barking deer (Table 1). Gaur density was
5.21 (1.41) the highest followed by chital 4.61(1.2) in NNTR. In TATR,
the density of chital was the highest 5.10 (1.22) followed by sambar
4.68 (0.76).

\textbf{3.3. Large predator density across study sites}

We got 452 photo captures of tigers in TATR and 211 photo captures in
NNTR. A total of 71 tigers in TATR and 8 tigers in NNTR were identified
through camera trap images. The heterogeneity model with the lowest AIC
value was chosen based on difference in gender specific capture rate.
Tiger density per 100 km² was found to be 5.67 (0.69) in TATR. The
estimated density per 100 km² for tiger in NNTR was 0.71 (0.24). The
number of photo captures of dholes was 68 and 689 in TATR and NNTR,
respectively.

\textbf{3.4. Distribution-wide assessment of dhole pack size:}

We used average dhole pack size as the response variable and tiger
density, ungulate density, area of PA, terrain ruggedness as predictor
variables. We scaled predictor variables (Size of PA, elevational
heterogeneity and terrain ruggedness) and checked for correlation among
all predictor variables. We dropped elevational heterogeneity as the
predictor variables because of its high correlation with terrain
ruggedness. We ran a total of 10 additive and interactive models (Table
2, Figure 3). The top two models achieved the model selection criterion
of \selectlanguage{greek}Δ\selectlanguage{english}AICc \textless{}2. Upon model selection we found, additive effect of
tiger density and prey density and interactive effect of tiger density
and prey density, to be the top two best models (Table 3, Figure 4 \&
5). On averaging the two top models (Table 4, Figure 6) we found a
negative association of tiger density (-0.89 \selectlanguage{ngerman}± 0.33, p = 0.01) and a
positive association of prey density (0.09 ± 0.03, p =0.03) with the
pack size and prey*tiger density (0.01 ± 0.0, p =0.18) was not
significant but still explained the relation with the response variable.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\selectlanguage{greek}\textbf{Figure 3. Showing all the significant and non-significant
predictor variables with β coefficient values at 95\% confidence value}\selectlanguage{english}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4. Dhole pack size in response to tiger density based on
distribution wide assessment}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5. Dhole pack size in response to prey density based on
distribution wide assessment}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\selectlanguage{greek}\textbf{Figure 6. Average model parameters with β coefficient values at
95\% confidence}\selectlanguage{english}

\textbf{Discussion}

Apex predator actively harvests prey population and suppress
mesopredator community structure through top-down mechanisms (Ripple et
al., 2014). However, there is a dearth of evidence for subordinate
predator suppression due to lack of such natural experiment study
systems. We got an opportunity to understand the impact of a solitary
apex predator on competitively inferior social predator in a natural
experiment setup.

The Asiatic wild dog/ dhole is listed under ``Endangered'' category by
IUCN, with about only 949-2215 mature individuals surviving globally.
Indian subcontinent harbors majority of the viable dhole population
(Kamler et al., 2015). However, the species has been exterminated from
60\% of its past distributional range in India (Karanth et al., 2010)
due to decline in prey base, habitat loss, human persecution and
intra-guild competition (Hayward, Lyngdoh, \& Habib, 2014; kamler et
al., 2015). In India, persistence of dholes is mostly confined to
protected areas with infrequent presence records from secondary forests
and agro-forest plantations (Srivathsa, Karanth, Kumar, \& Oli, 2019).
These remnant habitat patches are also shared by other large carnivores,
thereby limiting dhole population by lethal intra-guild interactions
(Steinmetz, Seuaturien, \& Chutipong, 2013). Small size of PAs and
lopsided conservation efforts can further result in over-inflated apex
carnivore densities and be detrimental for subordinate predators like
dholes (Rayan \& Linkie, 2016; Kumar, Awasthi, Qureshi, \& Jhala, 2019).
Therefore, to conserve the endangered dhole, it is crucial to understand
its response to tigers, itself an endangered and flagship species. A
holistic understanding of guild interactions would improve our ability
to optimize ecological triage while conserving charismatic apex
predators and mid-ranking predators.

In this study, we found that the pack size was reduced in higher tiger
density areas. This might be due to the influence of interference
competition wherein a narrower niche is available for dholes to occupy
which limits their persistence. Studies from Africa also show a similar
trend where pack sizes of African wild dogs were reduced in areas of
higher lion densities (Groom, Lannas, \& Jackson, 2017). At high tiger
density site (TATR) we also observed direct intraguild predation events
i.e. tigers killing dholes (n= 5) however, no such events have been
observed in NNTR, the low tiger density site. Similar events have been
documented in African system by Creel and Creel (1996), where lions
actively chase away dogs and in certain instances even kill them.

Conversely, in low tiger density area, larger pack size is indicative of
a competitive release scenario (Groom, Lannas, \& Jackson, 2017). A
similar trend has been observed in spotted hyena clan size and pack size
of African wild dog due to reduced interspecific competition in a lion
depleted system and easy availability of food subsidies (Periquet,
Fritz, \& Revilla, 2014; Green, Farr, Holekamp, Strauss, \& Zipkin,
2019; Groom, Lannas, \& Jackson, 2017).

There was no significant difference between the individual prey
densities at both studies, however, the higher density of a large-body
size prey in NNTR is also interesting to note. This finding is in line
with the idea that low numbers of apex predator lead to an increase in
abundance of large-bodied prey in a system. The subordinate predators
are limited by body-size and cannot target such large prey and this
might be one of the possible explanations for our finding. Similar
pattern is observed in study systems where wolf (\emph{Canis lupus} )
populations are controlled (Burgar, Burton, \& Fisher, 2019) that not
only causes coyote (\emph{Canis latrans} ) abundance to increase but
also leads to stable number of adults in caribou (\emph{Rangifer
tarandus} ) population since coyotes are size-limited and cannot take
down larger individuals (Lewis, Gullage, Fifield, Jennings, \& Mahoney,
2017).

To examine whether reserve level patterns were concurrent with global
scale, we did a distribution-wide assessment of pack size across dhole
distributional range. We found support for our first two hypotheses,
dhole pack size were positively associated with prey density and
negatively associated with tiger densities. Statistically significant
but moderate effect size reflects that the patterns were diffused at
global scale. Patterns of subordinate predator response to apex
predators numbers do not always mirror each other over temporal and
spatial scales (Jachowski et al., 2020), pertaining to area specific
ecological settings, anthropogenic disturbances, level of protection at
reserve level and connectivity in the landscape at regional scale
(Greenville, Wardle, Tamayo, \& Dickman, 2014; Newsome, \& Ripple,
2015). We did not find support for our third hypothesis, dhole pack size
was not associated with high terrain ruggedness however, a low pack size
has been reported from studies of high altitude and rugged terrain
(Bashir, Bhattacharya, Poudyal, Roy, \& Sathyakumar, 2014; Lyngdoh,
Gopi, Selvan, \& Habib, 2014).

Taken together, our reserve level and distribution wide assessment
results are in support of the hypothesis that area specific ecological
settings such as prey composition, abundance, and co-predator density
are drivers of carnivore community structure, group size and behavior
(M'soka, Creel, Becker, \& Droge, 2016). Patterns of this study also
adhere to trends that have been observed in other long-term studies from
Serengeti National Park, Ngorongoro Conservation Area and Selous Game
Reserve, Tanzania, where the group size of subordinate predators are
positively associated with availability of food resources and negatively
associated with, apex predator numbers (Creel \& Creel, 1996; Creel \&
Creel, 1998; Woodroffe \& Ginsberg, 1999; Pole, 2000; P\selectlanguage{ngerman}ériquet, Fritz,
\& Revilla, 2015; Green, Farr, Holekamp, Strauss, \& Zipkin, 2019).

Tiger and dhole belong to the same carnivore guild because of similar
diet spectrum (Hayward, Lyngdoh, \& Habib, 2014), and therefore their
persistence in an area is linked to each other (Johnsingh,1992). Studies
based on non-invasive sampling such as camera trapping and occupancy
surveys suggest that carnivores adapt to mechanisms of niche
partitioning, depending on resource availability to facilitate sympatry
(Karanth et al, 2017). Very few studies have demonstrated that intense
intra-guild competition result in reduced density and spatial
displacement of the subordinate predators (Harihar, Pandav, \& Goyal,
2011; Mondal, Gupta, Bhattacharjee, Qureshi, \& Sankar, 2012; Steinmetz,
Seuaturien, \& Chutipong, 2013). Our study provides a new direction in
understanding how intra-guild competition can also potentially limit
pack size of dhole.

Competition from dominant carnivores remains one of the major factors
that drives densities of other sympatric carnivores in a system
(Wrangham, Gittleman, \& Chapman, 1993). NNTR and TATR are a comparable
natural experiment setup that allows us to study how these two
endangered tropical carnivores interact and understand consequences of
dominance shift. We suggest that the increased pack size of a social
subordinate predator (dhole) is an outcome of low density of apex
predator and availability of wider niche. The decision to form large
packs as compared to small packs is going to be more advantageous to
limit the growth of top predator. However, future long-term studies are
suggested based on multiple pack-year data, disease prevalence,
predation, kleptoparasitism events and recruitment rate of dholes,
across a gradient of apex predator density would further validate our
hypothesis.

\textbf{Reference}

Linnell, J.D. and Strand, O., 2000. Interference interactions,
co\selectlanguage{english}-existence and conservation of mammalian carnivores.~\emph{Diversity
and Distributions} ,~\emph{6} (4), pp.169-176.
https://doi.org/10.1046/j.1472-4642.2000.00069.x

Ford, A.T. and Goheen, J.R., 2015. Trophic cascades by large carnivores:
a case for strong inference and mechanism.~\emph{Trends in Ecology \&
Evolution} ,~\emph{30} (12), pp.725-735.
https://doi.org/10.1016/j.tree.2015.09.012

Palomares, F. and Caro, T.M., 1999. Interspecific killing among
mammalian carnivores.~\emph{The American Naturalist} ,~\emph{153} (5),
pp.492-508. https://doi.org/10.1086/303189

Caro, T.M. and O'Doherty, G., 1999. On the use of surrogate species in
conservation biology.~\emph{Conservation biology} ,~\emph{13} (4),
pp.805-814. https://doi.org/10.1046/j.1523-1739.1999.98338.x

Ritchie, E.G. and Johnson, C.N., 2009. Predator interactions,
mesopredator release and biodiversity conservation.~\emph{Ecology
letters} ,~\emph{12} (9), pp.982-998.
https://doi.org/10.1111/j.1461-0248.2009.01347.x

Case, T.J. and Gilpin, M.E., 1974. Interference competition and niche
theory.~\emph{Proceedings of the National Academy of Sciences}
,~\emph{71} (8), pp.3073-3077. https://doi.org/10.1073/pnas.71.8.3073

Macdonald DW. The ecology of carnivore social behaviour. Nature. 1983
Feb 3;301(5899):379-84.

Crooks, K.R. and Soule, M.E., 1999. Mesopredator release and avifaunal
extinctions in a fragmented system.~\emph{Nature} ,~\emph{400} (6744),
pp.563-566. https://doi.org/10.1038/23028

Letnic, M., Ritchie, E.G. and Dickman, C.R., 2012. Top predators as
biodiversity regulators: the dingo Canis lupus dingo as a case
study.~\emph{Biological Reviews} ,~\emph{87} (2), pp.390-413.
https://doi.org/10.1111/j.1469-185X.2011.00203.x

Vance, R.R., 1984. Interference competition and the coexistence of two
competitors on a single limiting resource.~\emph{Ecology} ,~\emph{65}
(5), pp.1349-1357. https://doi.org/10.2307/1939115

Ordiz, A., Bischof, R. and Swenson, J.E., 2013. Saving large carnivores,
but losing the apex predator?.\emph{BiologicalConservation} ,~\emph{168}
,pp.128-133. https://doi.org/10.1016/j.biocon.2013.09.024

Creel, S. and Creel, N.M., 1996. Limitation of African wild dogs by
competition with larger carnivores.~\emph{Conservation Biology}
,~\emph{10} (2), pp.526-538.
https://doi.org/10.1046/j.1523-1739.1996.10020526.x

Durant, S.M., 2000. Living with the enemy: avoidance of hyenas and lions
by cheetahs in the Serengeti.~\emph{Behavioral ecology} ,~\emph{11} (6),
pp.624-632. https://doi.org/10.1093/beheco/11.6.624

Durant, S.M., 1998. Competition refuges and coexistence: an example from
Serengeti carnivores.~\emph{Journal of Animal ecology} ,~\emph{67} (3),
pp.370-386. https://doi.org/10.1046/j.1365-2656.1998.00202.x

Creel, S., 2001. Four factors modifying the effect of competition on
carnivore population dynamics as illustrated by African wild
dogs.~\emph{Conservation Biology} ,~\emph{15} (1), pp.271-274.
https://doi.org/10.1111/j.1523-1739.2001.99534.x

Greenville, A.C., Wardle, G.M., Tamayo, B. and Dickman, C.R., 2014.
Bottom-up and top-down processes interact to modify intraguild
interactions in resource-pulse environments.~\emph{Oecologia}
,~\emph{175} (4), pp.1349-1358.
https://doi.org/10.1007/s00442-014-2977-8

Newsome, T.M. and Ripple, W.J., 2015. Carnivore coexistence: trophic
cascades.~\emph{Science} ,~\emph{347} (6220), pp.383-383. DOI:
10.1126/science.347.6220.383-a

Swanson, A., Caro, T., Davies\selectlanguage{english}-Mostert, H., Mills, M.G., Macdonald, D.W.,
Borner, M., Masenga, E. and Packer, C., 2014. Cheetahs and wild dogs
show contrasting patterns of suppression by lions.~\emph{Journal of
animal ecology} ,~\emph{83} (6), pp.1418-1427.
https://doi.org/10.1111/1365-2656.12231

Periquet, S., Fritz, H. and Revilla, E., 2015. The Lion King and the
Hyaena Queen: large carnivore interactions and
coexistence.~\emph{Biological reviews} ,~\emph{90} (4), pp.1197-1214.
https://doi.org/10.1111/brv.12152

Carbyn, L.N., Armbruster, H.J. and Mamo, C., 1994. The swift fox
reintroduction program in Canada from 1983 to 1992. Restoration of
endangered species, pp.247-271.

Clark, T.W., 1994. Restoration of the endangered black-footed ferret: a
20-year overview. Restoration of endangered species, pp.272-297.

Lindstrom, E.R., Brainerd, S.M., Helldin, J.O. and Overskaug, K., 1995,
January. Pine marten---red fox interactions: a case of intraguild
predation?. In Annales zoologici fennici (pp. 123-130). Finnish
Zoological and Botanical Publishing Board.

Henke, S.E. and Bryant, F.C., 1999. Effects of coyote removal on the
faunal community in western Texas.~\emph{The Journal of Wildlife
Management} , pp.1066-1081.

Krofel, M., Giannatos, G., \selectlanguage{polish}Ć\selectlanguage{english}irovi\selectlanguage{polish}č, \selectlanguage{english}D., Stoyanov, S. and Newsome, T.M.,
2017. Golden jackal expansion in Europe: a case of mesopredator release
triggered by continent-wide wolf persecution?. Hystrix: Italian journal
of mammalogy, 28(1), pp.9-15. https://doi.org/10.4404/hystrix-28.1-11819

M'soka J, Creel S, Becker MS, Droge E. 2016 Spotted hyaena survival and
density in a lion depleted ecosystem: The effects of prey availability,
humans and competition between large carnivores in African savannahs.
Biol. Conserv. 201, 348--355. https://doi:10.1016/j.biocon.2016.07.011

Clutton-Brock, T.H., Gaynor, D., McIlrath, G.M., Maccoll, A.D.C.,
Kansky, R., Chadwick, P., Manser, M., Skinner, J.D. and Brotherton,
P.N.M., 1999. Predation, group size and mortality in a cooperative
mongoose, Suricata suricatta.~\emph{Journal of Animal Ecology}
,~\emph{68} (4), pp.672-683. https://
doi:10.1046/j.1365-2656.1999.00317x

Steinmetz, R., Seuaturien, N. and Chutipong, W., 2013. Tigers, leopards,
and dholes in a half-empty forest: assessing species interactions in a
guild of threatened carnivores.~\emph{Biological conservation}
,~\emph{163} , pp.68-78. https://doi.org/10.1016/j.biocon.2012.12.016

Acharya, B.B., 2007. The ecology of the dhole or Asiatic wild dog (Cuon
alpinus) in Pench Tiger Reserve, Madhya Pradesh.~\emph{Unpublished PhD
Dissertation. Saurashtra University, Rajkot, India} .

Johnsingh, A.J.T., 1992. Prey selection in three large sympatric
carnivores in Bandipur.~\emph{Mammalia} ,~\emph{56} (4), pp.517-526.
https://doi.org/10.1515/mamm.1992.56.4.517

Karanth, K.U. and Sunquist, M.E., 2000. Behavioural correlates of
predation by tiger (Panthera tigris), leopard (Panthera pardus) and
dhole (Cuon alpinus) in Nagarahole, India.~\emph{Journal of Zoology}
,~\emph{250} (2), pp.255-265.
https://doi.org/10.1111/j.1469-7998.2000.tb01076.x

Wang, S.W. and Macdonald, D.W., 2009. Feeding habits and niche
partitioning in a predator guild composed of tigers, leopards and dholes
in a temperate ecosystem in central Bhutan.~\emph{Journal of Zoology}
,~\emph{277} (4), pp.275-283.
https://doi.org/10.1111/j.1469-7998.2008.00537.x

Wegge, P., Odden, M., Pokharel, C.P. and Storaas, T., 2009.
Predator--prey relationships and responses of ungulates and their
predators to the establishment of protected areas: a case study of
tigers, leopards and their prey in Bardia National Park,
Nepal.~\emph{Biological Conservation} ,~\emph{142} (1), pp.189-202.
https://doi.org/10.1016/j.biocon.2008.10.020

Rayan, D.M. and Linkie, M., 2016. Managing conservation flagship species
in competition: Tiger, leopard and dhole in Malaysia.~\emph{Biological
Conservation} ,~\emph{204} , pp.360-366.
https://doi.org/10.1016/j.biocon.2016.11.009

Karanth, K.U. and Sunquist, M.E., 1995. Prey selection by tiger, leopard
and dhole in tropical forests. Journal of Animal Ecology, pp.439-450.
https://doi.org/10.1016/j.biocon.2016.11.009

Karanth, K.U., Srivathsa, A., Vasudev, D., Puri, M., Parameshwaran, R.
and Kumar, N.S., 2017. Spatio-temporal interactions facilitate large
carnivore sympatry across a resource gradient.~\emph{Proceedings of the
Royal Society B: Biological Sciences} ,~\emph{284} (1848), p.20161860.
https://doi.org/10.1016/j.biocon.2016.11.009doi:10.1098/rspb.2016.1860

Rasphone, A., K\selectlanguage{ngerman}éry, M., Kamler, J.F. and Macdonald, D.W., 2019.
Documenting the demise of tiger and leopard, and the status of other
carnivores and prey, in Lao PDR's most prized protected area: Nam
Et-Phou Louey.~\emph{Global Ecology and Conservation} ,~\emph{20} ,
p.e00766. https://doi.org/10.1016/j.gecco.2019.e00766

Fernández-Gil, A., Quevedo, M., Barrientos, L.M., Nuño, A., Naves, J.,
de Gabriel, M., Ordiz, A. and Revilla, E., 2020. Pack size in humanized
landscapes: the Iberian wolf population.~\emph{Wildlife Biology}
,~\emph{2020} (2). https://doi.org/10.2981/wlb.00594

Green, D.S., Farr, M.T., Holekamp, K.E., Strauss, E.D. and Zipkin, E.F.,
2019. Can hyena behaviour provide information on population trends of
sympatric carnivores?.~\emph{Philosophical Transactions of the Royal
Society B} ,~\emph{374} (1781), p.20180052.
https://doi.org/10.1016/j.biocon.2016.11.009doi:10.1098/rstb.2018.0052

Markham, A.C., Gesquiere, L.R., Alberts, S.C. and Altmann, J., 2015.
Optimal group size in a highly social mammal.~\emph{Proceedings of the
National Academy of Sciences} ,~\emph{112} (48), pp.14882-14887.
https://doi.org/10.1016/j.biocon.2016.11.009

Gusset, M. and Macdonald, D.W., 2010. Group size effects in
cooperatively breeding African wild dogs.~\emph{Animal Behaviour}
,~\emph{79} (2), pp.425-428. https://doi:10.1016/j.anbehav.2009.11.021

White, P.J. and Garrott, R.A., 2005. Northern Yellowstone elk after wolf
restoration.~\emph{Wildlife Society Bulletin} ,~\emph{33} (3),
pp.942-955.
https://doi.org/10.2193/0091-7648(2005)33{[}942:NYEAWR{]}2.0.CO;2

Rodgers, W.A. and Panwar, S.H., 1988. Biogeographical classification of
India New Forest.~\emph{Dehra Dun, India} .

Champion, H.G. and Seth, S.K., 1968.~\emph{A revised survey of the
forest types of India} . Manager of publications.

Dhanwatey, H.S., Crawford, J.C., Abade, L.A., Dhanwatey, P.H., Nielsen,
C.K. and Sillero-Zubiri, C., 2013. Large carnivore attacks on humans in
central India: a case study from the Tadoba-Andhari Tiger
Reserve.~\emph{Oryx} ,~\emph{47} (2), pp.221-227.
https://doi:10.1017/S0030605311001803

Modi, S., Habib, B., Ghaskadbi, P., Nigam, P. and Mondol, S., 2019.
Standardization and validation of a panel of cross-species
microsatellites to individually identify the Asiatic wild dog (Cuon
alpinus). PeerJ, 7, p.e7453. https://doi.org/10.7717/peerj.7453

Stansbury, C.R., Ausband, D.E., Zager, P., Mack, C.M., Miller, C.R.,
Pennell, M.W. and Waits, L.P., 2014. A long\selectlanguage{english}-term population monitoring
approach for a wide\selectlanguage{english}-ranging carnivore: noninvasive genetic sampling of
gray wolf rendezvous sites in Idaho, USA. The Journal of Wildlife
Management, 78(6), pp.1040-1049. https://doi.org/10.1002/jwmg.736

Grassman, L.I., Tewes, M.E., Silvy, N.J. and Kreetiyutanont, K., 2005.
Spatial ecology and diet of the dhole Cuon alpinus (Canidae, Carnivora)
in north central Thailand. Mammalia, 69(1), pp.11-20.
https://doi.org/10.1515/mamm.2005.002

Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers,
D.L. and Thomas, L., 2001. Introduction to distance sampling: estimating
abundance of biological populations.

Thomas, L., Buckland, S.T., Rexstad, E.A., Laake, J.L., Strindberg, S.,
Hedley, S.L., Bishop, J.R., Marques, T.A. and Burnham, K.P., 2010.
Distance software: design and analysis of distance sampling surveys for
estimating population size.~\emph{Journal of Applied Ecology}
,~\emph{47} (1), pp.5-14. https://doi:10.1111/j.1365-2664.2009.01737.x

Karanth, K.U. and Nichols, J.D., 1998. Estimation of tiger densities in
India using photographic captures and recaptures.~\emph{Ecology}
,~\emph{79} (8), pp.2852-2862.

Pollock, K.H., Nichols, J.D., Brownie, C. and Hines, J.E., 1990.
Statistical inference for capture-recapture experiments.~\emph{Wildlife
monographs} , pp.3-97.

Borchers, D.L. and Efford, M.G., 2008. Spatially explicit maximum
likelihood methods for capture--recapture studies.~\emph{Biometrics}
,~\emph{64} (2), pp.377-385.
https://doi:10.1111/j.1541-0420.2007.00927.x

Efford M, Efford MM. 2019 Package `secr'.

Srivathsa, A., Sharma, S. and Oli, M.K., 2020. Every dog has its prey:
Range-wide assessment of links between diet patterns, livestock
depredation and human interactions for an endangered
carnivore.~\emph{Science of The Total Environment} ,~\emph{714} ,
p.136798. https://doi.org/10.1016/j.scitotenv.2020.136798

Handcock, M.S. and Gile, K.J., 2011. Comment: On the concept of snowball
sampling.~\emph{Sociological Methodology} ,~\emph{41} (1), pp.367-371.
https://doi.org/10.1111\%2Fj.1467-9531.2011.01243.x

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and
Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial
analysis for everyone.~\emph{Remote sensing of Environment} ,~\emph{202}
, pp.18-27. https://doi.org/10.1016/j.rse.2017.06.031

Ripple, W.J., Estes, J.A., Beschta, R.L., Wilmers, C.C., Ritchie, E.G.,
Hebblewhite, M., Berger, J., Elmhagen, B., Letnic, M., Nelson, M.P. and
Schmitz, O.J., 2014. Status and ecological effects of the world's
largest carnivores.~\emph{Science} ,~\emph{343} (6167).
DOI:10.1126/science.1241484

Kamler, J.F., Songsasen, N., Jenks, K., Srivathsa, A., Sheng, L. and
Kunkel, K., 2015. Cuon alpinus. The IUCN Red List of Threatened Species
2015: e. T5953A72477893.

Karanth, K.K., Nichols, J.D., Karanth, K.U., Hines, J.E. and Christensen
Jr, N.L., 2010. The shrinking ark: patterns of large mammal extinctions
in India.~\emph{Proceedings of the Royal Society B: Biological Sciences}
,~\emph{277} (1690), pp.1971-1979.
https://doi.org/10.1098/rspb.2010.0171

Hayward, M.W., Lyngdoh, S. and Habib, B., 2014. Diet and prey
preferences of dholes (C uon alpinus): dietary competition within A
sia's apex predator guild.~\emph{Journal of Zoology} ,~\emph{294} (4),
pp.255-266. https://doi.org/10.1111/jzo.12171

Srivathsa, A., Karanth, K.U., Kumar, N.S. and Oli, M.K., 2019. Insights
from distribution dynamics inform strategies to conserve a dhole Cuon
alpinus metapopulation in India.~\emph{Scientific reports} ,~\emph{9}
(1), pp.1-12. https://doi.org/10.1038/s41598-019-39293-0

Kumar, U., Awasthi, N., Qureshi, Q. and Jhala, Y., 2019. Do conservation
strategies that increase tiger populations have consequences for other
wild carnivores like leopards?.~\emph{Scientific reports} ,~\emph{9}
(1), pp.1-8. https://doi.org/10.1038/s41598-019-51213-w

Groom, R.J., Lannas, K. and Jackson, C.R., 2017. The impact of lions on
the demography and ecology of endangered African wild dogs.~\emph{Animal
Conservation} ,~\emph{20} (4), pp.382-390.
https://doi.org/10.1111/acv.12328

Burgar, J.M., Burton, A.C. and Fisher, J.T., 2019. The importance of
considering multiple interacting species for conservation of species at
risk.~\emph{Conservation Biology} ,~\emph{33} (3), pp.709-715.
https://doi.org/10.1111/cobi.13233

Lewis, K.P., Gullage, S.E., Fifield, D.A., Jennings, D.H. and Mahoney,
S.P., 2017. Manipulations of black bear and coyote affect caribou calf
survival. The Journal of Wildlife Management, 81(1), pp.122-132.
https://doi.org/10.1002/jwmg.21174

Jachowski, D.S., Butler, A., Eng, Robin Y.Y., Gigliotti, L., Harris, S.,
and Williams, A., 2020. Identifying mesopredator release in
multi\selectlanguage{english}-predator systems: a review of evidence from North America. Mammal
Review, https://doi.org/10.1111/mam.12207

Bashir, T.A.W.Q.I.R., Bhattacharya, T., Poudyal, K., Roy, M. and
Sathyakumar, S., 2014. Precarious status of the Endangered dhole Cuon
alpinus in the high elevation Eastern Himalayan habitats of
Khangchendzonga Biosphere Reserve, Sikkim, India. Oryx, 48(1),
pp.125-132. https://doi.org/10.1017/S003060531200049X

Lyngdoh, S., Gopi, G.V., Selvan, K.M. and Habib, B., 2014. Effect of
interactions among ethnic communities, livestock and wild dogs (Cuon
alpinus) in Arunachal Pradesh, India. European Journal of Wildlife
Research, 60(5), pp.771-780. https://doi.org/10.1007/s10344-014-0846-8

Harihar, A., Pandav, B. and Goyal, S.P., 2011. Responses of leopard
Panthera pardus to the recovery of a tiger Panthera tigris
population.~\emph{Journal of Applied Ecology} ,~\emph{48} (3),
pp.806-814. https://doi.org/10.1111/j.1365-2664.2011.01981.x

Mondal, K., Gupta, S., Bhattacharjee, S., Qureshi, Q. and Sankar, K.,
2012. Response of leopards to re-introduced tigers in Sariska Tiger
Reserve, Western India.~\emph{International Journal of Biodiversity and
Conservation} ,~\emph{4} (5), pp.228-236.
https://doi.org/10.5897/IJBC12.014

Wrangham, R.W., Gittleman, J.L. and Chapman, C.A., 1993. Constraints on
group size in primates and carnivores: population density and day-range
as assays of exploitation competition.~\emph{Behavioral ecology and
Sociobiology} ,~\emph{32} (3), pp.199-209.
https://doi.org/10.1007/BF00173778

\textbf{Table 1. Individual density of various prey species from NNTR
and TATR, Maharashtra, India}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Prey Species\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
NNTR (Individual density and SE)\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Group size\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
TATR (Individual density and SE)\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
Group size\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Chital\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4.61\selectlanguage{ngerman}±1.2\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.02\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.10\selectlanguage{ngerman}±1.22\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.13\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Sambar\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.41\selectlanguage{ngerman}±0.32\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.88\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4.68\selectlanguage{ngerman}±0.76\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.25\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Nilgai\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.99\selectlanguage{ngerman}±0.35\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.81\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.09\selectlanguage{ngerman}±0.36\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.50\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Wild pig\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3.12\selectlanguage{ngerman}±1.11\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6.32\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.42\selectlanguage{ngerman}±2.08\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
7.22\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Gaur\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.21\selectlanguage{ngerman}±1.41\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.98\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.03\selectlanguage{ngerman}±1.15\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.35\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Barking deer\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.6\selectlanguage{ngerman}±0.2\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.96\selectlanguage{ngerman}±0.23\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.37\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2. Model selection Following information criterion}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllllll@{}}
\toprule
Model ID & Models & (Intercept) & Prey.density & Reserve.size &
Terrain.rug & Terrain.rug & Tiger.density & Prey.density:Tiger.density &
df & logLik & AICc & delta & weight &
R\textsuperscript{2}\tabularnewline
\midrule
\endhead
mod3 & Tiger density + Prey density & 8.385 & 0.106 & NA & NA & NA &
-0.721 & NA & 4 & -71.68 & 153.02 & 0.00 & 0.56 & 0.436\tabularnewline
mod2 & Tiger density * Prey density & 9.450 & 0.066 & NA & NA & NA &
-1.162 & 0.011 & 5 & -70.61 & 153.83 & 0.81 & 0.37 &
0.476\tabularnewline
mod1 & Tiger density + Prey density + PA size + Terrain ruggedness &
10.793 & 0.102 & -0.085 & -0.085 & -1.309 & -0.722 & NA & 6 & -71.09 &
158.00 & 4.98 & 0.05 & 0.458\tabularnewline
mod9 & Tiger density & 10.191 & NA & NA & NA & NA & -0.379 & NA & 3 &
-77.44 & 161.84 & 8.82 & 0.01 & 0.161\tabularnewline
mod4 & Tiger density + Terrain ruggedness & 12.986 & NA & NA & NA &
-1.757 & -0.393 & NA & 4 & -76.71 & 163.09 & 10.07 & 0.00 &
0.202\tabularnewline
mod6 & Tiger density + PA size & 15.950 & NA & -1.878 & -1.878 & NA &
-0.453 & NA & 4 & -76.84 & 163.35 & 10.33 & 0.00 & 0.194\tabularnewline
mod0 & Null model & 8.286 & NA & NA & NA & NA & NA & NA & 2 & -79.99 &
164.44 & 11.41 & 0.00 & 0\tabularnewline
mod10 & Prey density & 7.157 & 0.034 & NA & NA & NA & NA & NA & 3 &
-79.37 & 165.70 & 12.67 & 0.00 & 0.041\tabularnewline
mod5 & Prey density + Terrain ruggedness & 9.296 & 0.031 & NA & NA &
-1.311 & NA & NA & 4 & -79.02 & 167.71 & 14.69 & 0.00 &
0.064\tabularnewline
mod7 & Prey density + PA size & 3.526 & 0.044 & 1.146 & 1.146 & NA & NA
& NA & 4 & -79.20 & 168.07 & 15.04 & 0.00 & 0.052\tabularnewline
mod8 & PA size + Terrain ruggedness & 11.306 & NA & -0.228 & -0.228 &
-1.523 & NA & NA & 4 & -79.53 & 168.72 & 15.70 & 0.00 &
0.031\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3. Model selection Following AIC\textsubscript{C,}Royall's
1/8 rule for strength of evidence, and 95\% cumulative weight criteria}

\par\null\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
\begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Model Selection with delta AICc less than 2\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Model ID\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Model\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Intercept\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Prey.density\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Reserve.size\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Terrain. rug\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Tiger.density\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Prey.density:Tiger.density\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
K\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
LogLik\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
AICc\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
delta\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
weight\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
mod3\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Tiger density + Prey density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
8.385\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.106\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-0.721\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-71.678\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
153.022\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.000\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.600\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
mod2\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Tiger density * Prey density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
9.450\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.066\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-1.162\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.011\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-70.611\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
153.830\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.808\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.400\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model Selection using Royall's 1/8 rule for strength of evidence\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Intercept\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Prey.density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Reserve.size\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Terrain.rug\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Tiger.density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Prey.density:Tiger.density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
K\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
LogLik\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
AICc\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
delta\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
weight\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
mod3\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Tiger density + Prey density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
8.385\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.106\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-0.721\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-71.678\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
153.022\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.000\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.600\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
mod2\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Tiger density * Prey density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
9.450\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.066\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-1.162\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.011\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-70.611\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
153.830\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.808\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.400\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Model selection based on 95\% cumulative weight criteria\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Intercept\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Prey.density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Reserve.size\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Terrain.rug\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Tiger.density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Prey.density:Tiger.density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
K\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
LogLik\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
AICc\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
delta\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
weight\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
mod3\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Tiger density + Prey density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
8.385\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.106\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-0.721\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-71.68\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
153.02\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.00\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.60\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
mod2\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Tiger density * Prey density\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
9.450\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.066\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
NA\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-1.162\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.011\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-70.611\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
153.83\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.81\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.40\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4.} \textbf{Model averaging output for all variables
present in the top model selection}

\par\null\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& Estimate & Std. Error & Adjusted SE & z value &
Pr(\textgreater{}\textbar{}z\textbar{}) & CI (2.5 \% -
97.5\%)\tabularnewline
\midrule
\endhead
Intercept & 8.811 & 1.248 & 1.299 & 6.783 & \textless{}2e-16 *** & 6.265
- 11.357\tabularnewline
Tiger.denisty & -0.897 & 0.340 & 0.350 & 2.562 & 0.010* & -1.583 to
-0.210\tabularnewline
Prey.density & 0.090 & 0.040 & 0.042 & 2.164 & 0.030 * & 0.008 to
0.171\tabularnewline
Prey.density*Tiger density & 0.011 & 0.008 & 0.009 & 1.315 & 0.188 &
-0.005 to 0.028\tabularnewline
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 & Signif.
codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 & Signif. codes: 0
`***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 & Signif. codes: 0 `***'
0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 & Signif. codes: 0 `***' 0.001
`**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 & Signif. codes: 0 `***' 0.001 `**'
0.01 `*' 0.05 `.' 0.1 ` ' 1 & Signif. codes: 0 `***' 0.001 `**' 0.01 `*'
0.05 `.' 0.1 ` ' 1\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legend:}

Figure 1. Map showing Tadoba Andhari Tiger Reserve and Navegaon
NagziraTiger Reserve in Eastern Vidarbha Landscape. Inset showing study
area location in the map of India.

Figure 2. Comparison of dhole pack size from TATR and NNTR Figure 3.
Showing all the significant and non-significant predictor variables with
\selectlanguage{greek}β \selectlanguage{english}coefficient values at 95\% confidence value Figure 4. Dhole pack size
in response to tiger density based on distribution wide assessment
Figure 5. Dhole pack size in response to prey density based on
distribution wide assessment

Figure 6. Average model parameters with \selectlanguage{greek}β \selectlanguage{english}coefficient values at 95\%
confidenc

\textbf{Data Accessibilty Statement}

All data is submitted alongwith manuscript in the form of supplementary
material.

\textbf{Competing Interests Statement}

We have no competing interests.

\textbf{Author Contributions section}

B.H., P.N., A.B., and P.G. conceived the ideas; B.H., A.B., and P.G. and
designed the study; A.B. and P.G. collected the data; A.B. and P.G.
analyzed the data; A.B. and B.H. led the writing of the manuscript.
B.H., P.N., A.B., and P.G. approved the final draft.

\textbf{Acknowledgements}

This study was funded by Wildlife Institute India, M.Sc. program fund
and Maharashtra Forest
Department,D-22(8)/WL/Reseach/CT-722/(12-13)/2934/2013. We thank Dean
and Director, Wildlife Institute of India for institutional support. We
thank the Chief wildlife Warden and Chief Conservator of Forest, Tadoba
Andhari Tiger Reserve and Nawegaon Nagzira Tiger Reserve, Maharashtra
State, for granting permission and facilitation of the study. We thank
Nucharin Songsassen for her data inputs. We thank Nilanjan Chatterjee
and Nakul Markandey for their help with GIS work. We thank Naman Goyal,
Kumar Ankit and Akanksha Saxena for giving valuable inputs to improve
the manuscript.

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

