Journal of Threatened
Taxa | www.threatenedtaxa.org | 26 January 2025 | 17(1): 26331–26340
ISSN 0974-7907 (Online)
| ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.9087.17.1.26331-26340
#9087 | Received 10
April 2024 | Final received 15 November 2024 | Finally accepted 17 December
2024
Waterhole
utilization pattern of mammals in Jigme Singye Wangchuck National Park, Bhutan
Kunninpurathu Sivanandan
Aswin 1, Ugyen Dorji
2, Karma Sherub 3 & Mer Man
Gurung 4
1–4 Department of Forest
Science, College of Natural Resources, Royal University of Bhutan, Lobesa, Punakha, Bhutan.
1 ksaswin97@gmail.com
(corresponding author), 2 udorji.cnr@rub.edu.bt, 3 karmasherub3@gmail.com,
4 merman.gurung93@gmail.com
Editor: Anwaruddin
Choudhury, The Rhino Foundation, Guwahati, India. Date of publication:
26 January 2025 (online & print)
Citation: Aswin, K.S., U. Dorji, K. Sherub
& M.M. Gurung (2025).
Waterhole utilization pattern of mammals in Jigme Singye
Wangchuck National Park, Bhutan. Journal of Threatened Taxa 17(1): 26331–26340. https://doi.org/10.11609/jott.9087.17.1.26331-26340
Copyright: © Aswin et al. 2025. Creative Commons Attribution 4.0 International License. JoTT allows unrestricted use, reproduction, and
distribution of this article in any medium by providing adequate credit to the
author(s) and the source of publication.
Funding: This work was supported by the Mohamed bin Zayed Species Conservation Fund [grant number 230530890].
Competing interests: The authors declare no competing interests.
Author details: Aswin Kunninpurathu Sivanandan, holds a master’s degree in Natural Resource Management from the Royal University of Bhutan and a bachelor’s degree in Forestry from the College of Forestry, Kerala Agricultural University. Passionate about wildlife ecology and conservation, focusing on sustainable practices that protect biodiversity. Dr. Ugyen Dorji, associate professor at the College of Natural Resources,
Royal University of Bhutan. His teaching portfolio includes courses on research methodology, forest mensuration, applied silviculture, social forestry, and global and regional climate change. Mr. Karma Sherub, PhD candidate at ETH Zurich, and researches on eDNA analysis to monitor mammals from rivers. He is also interested in exploring human-wildlife interactions and their ecological impacts. He also teaches at College of Natural Resources of Bhutan, contributing to sustainable ecosystem management and biodiversity conservation.
Mr. Mer Man Gurung, lecturer at the College of Natural Resources, specializing in freshwater ecology and taxonomy, particularly concerning odonates and water mites. His research aims to enhance understanding of Himalayan biodiversity. He is presently involved in a project assessing high-altitude wetlands, examining how metacommunity dynamics are influenced by pollution and climate change.
Author contributions: AKS-—data collection, data analysis and paper writing; UD—principal supervisor, guidance in research methodology and manuscript preparation. KS—co-supervisor and provided guidance throughout the research work; MMG—Guidance in data analysis.
Acknowledgements: We would like to express our gratitude to the Mohamed bin Zayed Species Conservation Fund for their financial support, and to the Nature Conservation Division, Bhutan, for providing research equipment. Our heartfelt thanks go to the park officials of Jigme Singye Wangchuck National Park, including Namgay Dorji, Sanjit K. Rai, Cheku Rangdrel, Rinchen Dorji, Karma Chorten, Rathan Giri, Wangchuk Dorji, and Pema Namgyal, for their assistance during data collection. We are also grateful to Dil B. Mongar and Kinley Norbu for their on-site assistance, and to the villagers of Lawa, Lagwa, and Wangling Village for their hospitality. We thank R. Sreehari, Malik Fasil Madala, and Sachin K. Aravind from Kerala Agricultural University, Kerala, India for their support, and Shahina Noushad from the Rain Forest Research Institute, Assam, India for editing and proofreading our article. We hope that our research will contribute to the conservation of biodiversity in Jigme Singye Wangchuck National Park, Bhutan.
Abstract: Most studies on
waterholes come from arid and semi-arid countries where water availability for
wildlife is limited. Bhutan is a country with rich running water sources. Less
is known about the waterhole usage by wildlife in the country. The present
study aimed to understand the importance and usage pattern of waterholes by
mammals in the protected areas of Bhutan. Thirty waterholes in Jigme Singye Wangchuck National Park, Bhutan
were monitored for dry and wet seasons. A generalized linear model was used to
assess the impact of various waterhole parameters on mammal usage of the
waterholes. Seven out of 12 parameters studied showed a significant impact on
waterhole visitation by mammalian species. When water availability and salinity
showed a positive impact on waterhole visits by mammals, distance from
agricultural land, altitude, herb density, canopy cover, and livestock presence
showed a negative impact. The study shows that even in the presence of major
running water sources, waterholes are well utilized by mammals independent of
seasons with ungulates being the most frequent visitors in the waterholes. This
shows the importance of waterholes in protected areas of the country for better
management of wildlife.
Keywords: Camera-trapping,
negative binomial regression, species-environment relationship, waterholes.
Abbreviations: DO—dissolved oxygen
| GBH—girth at breast height | JSWNP—Jigme Singye Wangchuck National Park | SMART—spatial monitoring and
reporting tool | TDS—total dissolved solids.
INTRODUCTION
Bhutan is the only
country that is entirely part of the eastern Himalayan hotspot known for its
rich biodiversity and extensive forest cover (Banerjee & Bandopadhyay 2016; Nepal 2022). With a land area of
<0.0075% of the world’s surface, Bhutan is home to 1.99% of the world’s
mammal species, 7.07% of its bird species, and 4.29% of its butterfly species
(Nepal 2022). The country places great emphasis on environmental protection and
management through policies such as Gross National Happiness (Thinley & Hartz-Karp 2019). According to Lham et al. (2019), the effective management of the
country’s protected areas is limited due to gaps in monitoring and research
data. Various scientific studies have been conducted in Bhutan on wildlife
management, including human-wildlife interaction and climate change (Penjor et al. 2021; Yeshey et al.
2023). No major studies have been done on the water-related aspect of wildlife
management in the country (Lham et al. 2019).
Wildlife water
development is an effective and appropriate wildlife management tool,
especially during the dry seasons (Rosenstock et al. 2004). The provision of
sufficient water in the protected areas is considered a key managerial
intervention (Hayward & Hayward 2012). The linkages between forests, water,
and wildlife create a mosaic that benefits both wildlife and communities living
in the forest (Warrington et al. 2017). The seasonal availability of water in
the water sources can impact the individual species even in their habitat
selection (Najafi et al. 2019). The non-uniform distribution of water resources
can even affect the overconsumption of vegetation in an area and thereby the
vegetative degradation in the forest (Dzinotizei et
al. 2017). Waterholes are one of the major sources of water for wildlife,
especially in arid and semi-arid ecosystems (Sirot et
al. 2016). The importance of waterholes in supporting wildlife, especially
during dry seasons, is well-documented in the context of other ecosystems too
(Vaughan & Weis 1999).
More than a water
source, the waterholes are utilized by wildlife as a foraging ground, hunting
ground, and mineral sources (Adams et al. 2003; Davidson et al. 2013; Pin et
al. 2020). Wildlife preference for waterholes may depend on various factors
such as physical, chemical, geographical, and ecological factors. These factors
must be properly studied and understood for the proper management of these
waterholes. The present study tries to understand the importance of waterholes
in Bhutan, a country with one of the highest per capita water resource
availability of 94,500 m3/capita/year (Tariq et al. 2021) and also
to understand how water quality (salinity, dissolved oxygen, total dissolved
solids), anthropogenic disturbances (distance from road, distance from
agricultural land, distance from settlements, presence of livestock),
vegetation (herb density, shrub density and canopy cover), geophysical factors
(elevation and presence of other waterholes) and availability of water in the
waterhole are related to the selection of waterholes by the mammal species in
Jigme Singye Wangchuck
National Park, Bhutan.
MATERIAL AND METHODS
Study Area
The study was
conducted in Jigme Singye Wangchuck
National Park, formerly known as Black Mountain National Park (JSWNP, 27.017 to
27.483 latitude and 90.067 to 90.683 longitude) in central Bhutan. With an area of
1,730 km2, JSWNP is the third largest national park in Bhutan. It
covers five political districts (Sarpang, Trongsa, Tsirang, Wangdue Phodrang and Zhemgang) with
elevation differences ranging 250–4,925 m (Department of Forests and Park
Services, Ministry of Agriculture and Forests, Bhutan 2021). The south-west
monsoon contributes most of the annual rainfall in the region from June to September.
JSWNP connects Jigme Dorji National Park (JDNP) with Wangchuck Centennial National Park (WCNP) in the north and
Royal Manas National Park (RMNS) with Phibsoo Wildlife Sanctuary (PWS) in the south through
biological corridors, making JSWNP biologically diverse (Tshewang
& Letro 2018). The national park supports 876
species of plants, 55 species of mammals, 323 species of birds, 376 species of
butterflies, 42 species of herpetofauna, and 16 species of fishes (Tshewang & Letro 2018; JSWNP
2021). The park also supports 10–15% of Bhutan’s total tiger population in its
cool and warm broadleaved forests (Wang & Macdonald 2006). Thirty natural
waterholes, all of similar size, were monitored over a six-month period from
March 2023 to August 2023 across four ranges (Taksha,
Langthel, Tingtibi, and Nabji) of the national park (Figure 1). Most of these
waterholes are fed by springs, while a few were sourced from rainwater.
Data Collection
The study attempted
to conduct a homogeneous sampling effort of 30 days for 30 camera stations.
Because the camera trap in station three was turned off within 20 days due to
high animal activity and the distorted camera trap in station 17, these two
camera stations were avoided. Twenty-eight camera traps for 60 days in two seasons
resulting in 1,680 trapping days. RECONYX Hyperfire
II camera traps were used for the study. The cameras were oriented in such a
way that water availability in the waterhole was evident in the captured
images. To capture all mammal species visiting the waterhole and to avoid
distractions from ground vegetation, the cameras were mounted at a height of 50
cm to 1 m above the ground level (Meek et al. 2014). Data was collected in two
seasons, dry season (March–April 2023) and wet season (July–August 2023).
The time delay of
each camera trap was 3 min and the delay between each image was 30 s . Of the images recorded by the camera traps, only those
images from which the animal species can be identified properly were analyzed.
The image of the same species within 30 minutes from the same waterhole was
considered the same individual, therefore such images were not considered for
analysis (Pin et al. 2020). It is not necessarily that the image captured shows
animals drinking at the time of observation, even their proximity near to the
waterhole was be considered as drinking behavior (Hayward & Hayward 2012).
Water quality
parameters of each waterhole were recorded twice in each season. The parameters
such as salinity, dissolved oxygen (DO) and total dissolved solids (TDS) of the
water samples were tested and recorded. Hanna Edge HI2002-02 and Microprocessor
COND-TDS-SAL-Meter LT-51 were used to test the following parameters. Parameters
such as salinity and TDS were tested within 24 h of sampling and DO was tested
in the field. The availability of water in the waterhole during the study
period and the presence of livestock in the waterhole were also recorded using
the camera trap images.
Additionally,
vegetation assessment was carried out from three vegetation plots around each
waterhole. The plots were taken in three directions (0° north, 120° south-east,
and 240° south-west) 100 m from the waterhole, considering the waterhole as the
center point. All tree species within a 12.62 m radius that had a GBH greater than
10 cm were recorded. Square plots of 5 x 5 m and 1 x 1 m were used to assess
shrub and herb species, respectively, inside the same circular plot. The number
of stumps was counted for both herb and shrub species. The canopy cover around
the waterhole was recorded using Canopeo software (Patrignani & Ochsner 2015). Anthropogenic disturbance
in the waterhole was recorded by measuring the shortest distance of the
waterhole to human settlements, agricultural land, and roads. The coordinates
of the waterholes and other parameters in the field were recorded using Garmin eTrex 32x. ArcGIS software was used to determine the
shortest straight-line distance from anthropogenic disturbance to the waterhole
using the recorded coordinates from the field (Environmental Systems Research
Institute, Inc. 2016). The presence of other waterholes within 500 m of the
studied waterhole was surveyed and recorded. The altitude and slope of the
waterhole location were also recorded as geophysical parameters.
Data Analysis
The camera trap
images were used as an index of animal visit to the waterhole. For images
showing more than one individual, all the individuals were counted separately
and recorded. The camera trap images were processed to correct date and time
errors in some camera stations, and a species dataset was created using the
Camera Trap File Manager software (Panthera). Species
richness, evenness and abundance were calculated from the species dataset. For
statistical analysis, paired t-test was adopted to assess seasonal differences
in waterhole visitation by mammals (Wilkerson 2008).
The collinearity
between environmental variables were examined with the variance inflation
factor (VIF), using this function from the car package in R (Fox & Weisberg
2018). Variables with VIF >10 were considered to be highly correlated and
therefore excluded from future analysis (Montgomery et al. 2012). There was no
strong correlation between any environmental variables except salinity and TDS.
Therefore, all environmental parameters except TDS were retained (Table 1).
The negative binomial
regression model from the MASS package was used to understand the impact of
different waterhole parameters on the waterhole visitation rate of mammal
species (Ripley 2022). A separate negative binomial regression model was
performed for wet and dry seasons using count data of mammalian species to
examine their preferences concerning various waterhole parameters, including
water quality (salinity and dissolved oxygen), anthropogenic disturbance (presence
of livestock, agricultural land, and settlements), vegetation (herb density,
shrub density, and canopy cover), geophysical factors (elevation and presence
of other waterholes), and water availability.
To understand how
different waterhole parameters affect each mammal
species in the selection of a waterhole, a separate negative binomial
regression model was performed for a select group of the most abundant mammals
in the studied waterhole separately for wet and dry seasons. All environmental
data were scaled using the scale function in r before performing a negative
binomial regression model to avoid bias from variables with different scales.
All the statistical analysis were performed using R v.
4.3.2 (R Development Core Team 2023).
RESULTS
Species Richness and
Abundance in the waterholes
A total of 3,549
animal visits from 23 different mammal species were recorded over 1,680
trapping days (Table 2). Relatively high species richness was observed in the
waterhole during the dry season (M = 4.29) compared to the wet season (M =
3.89). Camera station 13 in the Langthel range showed
the highest species richness in both the wet and dry seasons. Ungulate species
(Rusa unicolor, Sus
scrofa, and Muntiacus
vaginalis) showed higher abundance in the waterhole compared to the other
mammal species in both the seasons (Figure 2). Muntiacus
vaginalis was the only mammal species reported from all 28 waterholes.
Negative Binomial
Regression Model
From the separate
negative binomial regression models for the dry and wet seasons, four waterhole
parameters showed a significant impact on the use of waterholes by mammals,
including canopy cover (Est. = -0.835, SE = 0.226, p = 0.000) and the presence
of livestock (Est. = -0.619, SE = 0.225, p = 0.006) in the dry season.
Conversely, in the wet season, more parameters showed significance: shrub
density (Est. = -0.493, SE = 0.232, p = 0.03), distance from agricultural land
(Est. = -0.548, SE = 0.243, p = 0.02), and altitude (Est. = -0.500, SE = 0.206,
p = 0.01). Availability of water, salinity, and canopy cover showed a
significant impact on mammal visits both in wet and dry seasons. Salinity,
water availability, and the presence of agricultural land showed a positive
impact on the animal visit to the waterhole whereas the presence of livestock,
altitude, herb density, and canopy cover of the waterhole location showed a
negative impact on the waterhole visit of mammal species.
As Rusa unicolor, Sus
scrofa, and Muntiacus
vaginalis exhibited the highest abundance at the studied waterhole,
separate negative binomial regression models were conducted for each of these three mammal species across both wet and dry seasons. The
negative binomial regression model for Rusa
unicolor revealed that parameters such as distance from settlements (Est. =
0.95, SE = 0.46, p = 0.039), shrub density (Est. = -1.29, SE = 0.64, p =
0.045), crown cover (Est. = -1.47, SE = 0. 0.48, p = 0.002), and water
availability (Est. = 1.37, SE = 0.52, p = 0.008) significantly influenced the
visitation rates of Rusa unicolor to
the waterhole. Notably, shrub density (Est. = 1.15, SE = 0.54, p = 0.034)
around the waterhole was found to be the most influential factor for Sus scrofa. Sus scrofa was
found to prefer waterholes with higher shrub density, particularly during the
dry season. Muntiacus vaginalis showed
a significant impact on the waterhole parameters including dissolved oxygen
(Est. = -0.62, SE = 0.27, p = 0.022), presence of livestock (Est. = -0.78, SE =
0.32, p = 0.014), and crown cover (Est. = -0.62, SE = 0.25, p = 0.011).
DISCUSSION
This research was a
preliminary study to understand the importance and utilization pattern of
waterholes by mammals in the protected areas of Bhutan. The results of the
study showed a fairly high species richness in the waterhole, recording a total
of 23 mammal species from the waterholes studied (Table 2). Ungulate species
were the frequent visitors to the waterhole (Figure 2) as their water
requirements are relatively high compared to the other mammal species (Najafi
et al. 2019). This can also be due to the higher densities of ungulates in
general. The result of the paired sample t-test did not show any significant
difference in the use of waterholes in the wet and dry seasons, which implies
that more than a seasonal watering point, waterholes were utilized by the
mammals regardless of the season. The significance of water availability in the
waterhole in both seasons also back the following
statement. The presence of water in the waterhole must be a concern as 52.7% (n
= 16) of the waterholes studied were found to be without water at some point
during the data collection, with two waterholes being completely dry throughout
the dry season. The result of a separate negative binomial regression model
performed for the most abundant mammal species (Table 8) also shows the close
relationship of Rusa unicolor with
water availability in the waterhole.
Regarding the water
quality parameters of the waterhole, salinity showed a positive impact on the
waterhole visit by the mammal species both in the wet and in the dry seasons.
One-unit increase in the salinity showed a 53.3% increase in the animal visit
in the dry season and a 63.7% in the wet season. Consistent with some previous
studies on waterholes, the positive impact of animal visitation on water
salinity in JSWNP may be to meet the mineral requirements of mammalian species
(Adams et al. 2003). This can be one of the reasons why mammal species tend to
prefer waterholes over the freshwater streams in the national park. Whether the
waterhole in the national park is used by the mammals as an alternative source
to meet their mineral requirements is still a question as the presence of the
salt licks around the waterhole was not considered as one of the variables for
the following study, which merits further research in the following topic.
The presence of
livestock has been reported from the 13 of the waterholes monitored which had a
significant negative impact on the waterhole visit of the mammal species,
especially during the dry season (Table 3). Muntiacus
vaginalis also exhibited a negative impact on the presence of livestock in
waterholes (Table 9), particularly during the dry season when livestock
activity is high in the forest areas of Bhutan (Buffum
et al. 2009). Many studies showed the implications of sharing the same water
source by wildlife and livestock, especially when it comes to the spreading of
disease from livestock to wildlife species which shows a higher potential in
stagnant water sources like waterhole (Cowie et al. 2016). Ungulate species as
well as the livestock were camera-trapped defecating in the waterholes having
high use pressure. The forest department needs to give much importance to the
following situation in the protected areas of the country. In the wet season,
there was no significant effect observed on the presence of livestock on the
mammals (Table 4). Further research needs to be conducted for a better
understanding of the following situation in the country.
The proximity of
agricultural land to waterholes was found to have a significant positive impact
on waterhole visitation rates by mammal species during the wet season. This
positive impact was not observed during the dry season. The presence of farmers
engaged in agricultural activities during the dry season may account for the
non-significant effect during this period, particularly as the major
agricultural activity in my study area is the cultivation of black cardamom,
which involves significant fieldwork that occurs only two to three times a
year. The negative binomial regression model for Sus
scrofa indicated a strong association between Sus scrofa and
waterholes characterized by higher shrub density (Table 5). In contrast, other
mammals tended to prefer waterhole locations with lower shrub density. Most
shrub species recorded around the waterholes monitored were non-palatable
species. The dense shrub cover can limit visibility and potentially increasing
predation risk (Sutherland et al. 2018). Sus
scrofa preferred these bushy habitats to take
advantage of the concealment they provide, which could help them minimize
predation risk. In contrast, larger ungulate species may avoid waterholes with
dense shrub patches due to their need for greater visibility to detect
predators.
CONCLUSION
The following study
shows that even in the presence of major running water sources, mammals tend to
prefer waterholes for their water requirements. The results show that salinity
may be the reason why the mammals prefer waterholes over the running water
source in the national park. In addition to salinity, waterhole parameters
including distance from agricultural land, altitude, herb density, canopy
cover, livestock presence, and water availability also significantly impacted
the waterhole visit by the mammal species. More importance needs to be given to
the waterhole management practices in JSWNP. Currently, reliable data on the
distribution of waterholes in the national park is lacking. The SMART (Spatial
Monitoring and Reporting Tool) patrolling data of the waterhole also seems to
be unreliable, which was the major challenge faced during the initial stages of
research data collection (Wildlife Conservation Society). The preparation of accurate data base on
waterhole distribution and water availability throughout the year will help in
the better management of waterholes in the national park. This can also support
future research on waterholes in the national park. Since salinity and water
availability in the waterhole seem to be the most influential parameters for
mammals regardless of seasons, it is recommended that more importance be given
to waterholes with continuous water availability and presence of salinity when
it comes to future waterhole management practice in the country. Stagnant water
sources such as waterholes shared by livestock and wildlife, can be a medium
for the spread of disease from livestock to wildlife. Therefore, the forest
department needs to consider the presence of livestock in the waterhole to avoid
further impacts. In the following context, the presence of disease-causing
pathogens and antibiotic-resistant bacteria (AMR) in waterholes is the subject
of further research.
Table 1.
Results of multicollinearity between variables showing the variance inflation
factor of individual variable in wet and dry season.
|
Variable |
Variance inflation
factor (VIF) |
|
|
Dry season |
Wet season |
|
|
Dissolved oxygen |
2.44 |
1.83 |
|
Salinity |
1.58 |
1.50 |
|
Water availability |
4.25 |
3.01 |
|
Distance to river |
2.45 |
2.60 |
|
Waterholes within
500 m |
1.55 |
2.21 |
|
Altitude |
2.56 |
2.65 |
|
Slope |
2.02 |
2.51 |
|
Distance to road |
4.37 |
5.08 |
|
Distance to
agriculture land |
2.60 |
2.10 |
|
Distance to
settlement |
1.66 |
2.39 |
|
Herb density |
2.11 |
2.67 |
|
Shrub density |
2.03 |
2.66 |
|
Canopy cover |
2.50 |
2.80 |
|
Livestock |
1.56 |
2.59 |
Table 2.
Mammal species recorded from the waterholes and their seasonal visit.
|
Species |
IUCN Red List |
No. of visit |
||
|
Common name |
Scientific name |
Dry season |
Wet season |
|
|
Asian Black Bear |
Ursus thibetanus |
Vulnerable |
19 |
86 |
|
Asian Golden Cat |
Catopuma temminckii |
Near Threatened |
4 |
4 |
|
Assamese Macaque |
Macaca assamensis |
Near Threatened |
27 |
19 |
|
Black Giant
Squirrel |
Ratufa bicolor |
Near Threatened |
- |
2 |
|
Clouded Leopard |
Neofelis nebulosa |
Vulnerable |
- |
1 |
|
Dhole |
Cuon alpinus |
Endangered |
9 |
4 |
|
Gaur |
Bos gaurus |
Vulnerable |
- |
12 |
|
Gee’s Golden Langur |
Trachypithecus geei |
Endangered |
2 |
5 |
|
Himalayan Goral |
Naemorhedus goral |
Near Threatened |
- |
1 |
|
Hoary-bellied
Squirrel |
Callosciurus pygerythrus |
Least Concern |
8 |
12 |
|
Indian Leopard |
Panthera pardus fusca |
Near Threatened |
2 |
6 |
|
Mainland Leopard
Cat |
Prionailurus bengalensis |
Least Concern |
6 |
1 |
|
Mainland Serow |
Capricornis sumatraensis |
Vulnerable |
6 |
- |
|
Malayan Porcupine |
Hystrix brachyura |
Least Concern |
9 |
10 |
|
Marbled Cat |
Pardofelis marmorata |
Near Threatened |
2 |
- |
|
Masked Palm Civet |
Paguma larvata |
Least Concern |
20 |
3 |
|
Nepal Gray langur |
Semnopithecus schistaceus |
Endangered |
15 |
- |
|
Northern Red
Muntjac |
Muntiacus vaginalis |
Least Concern |
606 |
209 |
|
Rodent |
Niviventer sp. |
- |
- |
24 |
|
Sambar |
Rusa unicolor |
Vulnerable |
959 |
1185 |
|
Small Indian
Mongoose |
Urva auropunctata |
Least Concern |
14 |
2 |
|
Wild Boar |
Sus scrofa |
Least Concern |
167 |
90 |
|
Yellow-throated
Marten |
Martes flavigula |
Near Threatened |
8 |
7 |
Table 3.
Summary of negative binomial model for dry season with model average
coefficient, standard error (SE), Z- value and significant value expressed as
hyper link with the coefficient (Signif. codes: 0
‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1).
|
|
Estimate |
SE |
z-value |
|
Intercept |
3.704 *** |
0.177 |
20.955 |
|
Dissolved oxygen |
-0.117 |
0.256 |
-0.456 |
|
Salinity |
0.427 * |
0.205 |
2.086 |
|
Water availability |
0.582 * |
0.279 |
2.088 |
|
Other waterhole |
-0.184 |
0.208 |
-0.883 |
|
Altitude |
-0.130 |
0.227 |
-0.575 |
|
Agricultural land |
0.010 |
0.226 |
0.044 |
|
Settlements |
0.334 |
0.193 |
1.737 |
|
Herb density |
-0.276 |
0.214 |
-1.291 |
|
Shrub density |
-0.230 |
0.227 |
-1.012 |
|
Crown cover |
-0.835 *** |
0.226 |
-3.699 |
|
Livestock |
-0.619 ** |
0.225 |
-2.748 |
Table 4.
Summary of negative binomial model for wet season with model average
coefficient, standard error (SE), Z- value and significant value expressed as
hyper link with the coefficient (Signif. codes: 0
‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1).
|
|
Estimate |
SE |
z-value |
|
Intercept |
3.253 *** |
0.177 |
18.359 |
|
Dissolved oxygen |
-0.277 |
0.226 |
-1.225 |
|
Salinity |
0.494 * |
0.210 |
2.351 |
|
Water availability |
0.728 ** |
0.222 |
3.279 |
|
Other waterhole |
-0.224 |
0.223 |
-1.003 |
|
Altitude |
-0.500 * |
0.206 |
-2.424 |
|
Distance from
agricultural land |
-0.548 * |
0.243 |
-2.259 |
|
Distance from
settlements |
0.190 |
0.193 |
0.981 |
|
Herb density |
-0.325 |
0.219 |
-1.479 |
|
Shrub density |
-0.493 * |
0.232 |
-2.123 |
|
Crown cover |
-0.480 * |
0.235 |
-2.045 |
|
Livestock |
-0.075 |
0.229 |
-0.326 |
Table 5.
Summary of negative binomial model for Sus scrofa in the dry season with model average
coefficient, standard error (SE), Z- value, and significant value expressed as
a hyper link with the coefficient (Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01
‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1).
|
|
Estimate |
SE |
z value |
|
(Intercept) |
0.99 * |
0.41 |
2.41 |
|
Dissolved oxygen |
-0.74 |
0.59 |
-1.25 |
|
Salinity |
-0.31 |
0.49 |
-0.63 |
|
Water availability |
0.30 |
0.62 |
0.48 |
|
Other waterhole |
0.04 |
0.48 |
0.09 |
|
Altitude |
0.06 |
0.53 |
0.12 |
|
Distance from
agricultural land |
-0.24 |
0.66 |
-0.36 |
|
Distance from
settlements |
-0.24 |
0.50 |
-0.48 |
|
Herb density |
0.99 |
0.71 |
1.39 |
|
Shrub density |
1.15 * |
0.54 |
2.11 |
|
Crown cover |
-0.42 |
0.50 |
-0.84 |
|
Livestock |
-0.71 |
0.53 |
-1.33 |
Table 6.
Summary of negative binomial model for Sus scrofa in the wet season with model average
coefficient, standard error (SE), Z- value, and significant value expressed as
hyperlink with the coefficient (Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’
0.05 ‘.’ 0.1 ‘ ’ 1).
|
|
Estimate |
SE |
z value |
|
(Intercept) |
0.33 |
0.54 |
0.61 |
|
Dissolved oxygen |
-1.05 |
0.72 |
-1.47 |
|
Salinity |
-0.47 |
0.69 |
-0.68 |
|
Water availability |
0.23 |
0.69 |
0.34 |
|
Other waterhole |
0.29 |
0.66 |
0.43 |
|
Altitude |
-0.81 |
0.71 |
-1.15 |
|
Distance from
agricultural land |
-0.31 |
0.77 |
-0.40 |
|
Distance from
settlements |
0.22 |
0.67 |
0.32 |
|
Herb density |
0.40 |
0.91 |
0.44 |
|
Shrub density |
0.30 |
0.74 |
0.41 |
|
Crown cover |
-0.01 |
0.65 |
-0.02 |
|
Livestock |
0.52 |
0.65 |
0.81 |
Table 7.
Summary of negative binomial model for Rusa
unicolor in dry season with model average coefficient, standard error (SE),
Z- value and significant value expressed as hyper link with the coefficient
(Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘
’ 1).
|
|
Estimate |
SE |
z value |
|
(Intercept) |
1.85 *** |
0.41 |
4.56 |
|
Dissolved oxygen |
0.33 |
0.61 |
0.55 |
|
Salinity |
0.49 |
0.46 |
1.08 |
|
Water availability |
0.54 |
0.64 |
0.85 |
|
Other waterhole |
-0.32 |
0.45 |
-0.71 |
|
Altitude |
-0.27 |
0.52 |
-0.52 |
|
Distance from
agricultural land |
0.80 |
0.62 |
1.29 |
|
Distance from
settlements |
0.95 * |
0.46 |
2.06 |
|
Herb density |
-0.63 |
0.67 |
-0.94 |
|
Shrub density |
-1.29 * |
0.64 |
-2.00 |
|
Crown cover |
-1.47 ** |
0.48 |
-3.05 |
|
Livestock |
-0.39 |
0.52 |
-0.76 |
Table 8.
Summary of negative binomial model for Rusa
unicolor in wet season with model average coefficient, standard error (SE),
Z- value and significant value expressed as hyper link with the coefficient
(Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘
’ 1).
|
|
Estimate |
SE |
z value |
|
(Intercept) |
1.85 *** |
0.40 |
4.65 |
|
Dissolved oxygen |
-0.25 |
0.48 |
-0.52 |
|
Salinity |
0.63 |
0.49 |
1.29 |
|
Water availability |
1.37 ** |
0.52 |
2.62 |
|
Other waterhole |
0.07 |
0.47 |
0.15 |
|
Altitude |
-0.39 |
0.50 |
-0.79 |
|
Distance from
agricultural land |
0.07 |
0.56 |
0.12 |
|
Distance from
settlements |
0.44 |
0.50 |
0.88 |
|
Herb density |
-0.82 |
0.68 |
-1.21 |
|
Shrub density |
0.11 |
0.55 |
0.21 |
|
Crown cover |
-0.86 |
0.48 |
-1.77 |
|
Livestock |
-0.49 |
0.53 |
-0.93 |
Table 9. Summary of negative binomial model for Muntiacus
vaginalis in the dry season with model average coefficient, standard error
(SE), Z- value, and significant value expressed as hyperlink with the
coefficient (Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1).
|
|
Estimate |
SE |
z value |
|
(Intercept) |
2.66 *** |
0.21 |
12.98 |
|
Dissolved oxygen |
-0.03 |
0.29 |
-0.10 |
|
Salinity |
0.36 |
0.24 |
1.50 |
|
Water availability |
0.40 |
0.31 |
1.28 |
|
Other waterhole |
-0.43 . |
0.23 |
-1.85 |
|
Altitude |
0.30 |
0.26 |
1.14 |
|
Distance from
agricultural land |
-0.23 |
0.32 |
-0.73 |
|
Distance from
settlements |
0.25 |
0.24 |
1.02 |
|
Herb density |
-0.09 |
0.34 |
-0.26 |
|
Shrub density |
-0.24 |
0.27 |
-0.88 |
|
Crown cover |
-0.62 * |
0.25 |
-2.52 |
|
Livestock |
-0.78 * |
0.32 |
-2.46 |
Table 10.
Summary of negative binomial model for Muntiacus
vaginalis in wet season with model average coefficient, standard error
(SE), Z- value and significant value expressed as hyper link with the
coefficient (Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1).
|
|
Estimate |
SE |
z value |
|
(Intercept) |
1.34 *** |
0.29 |
4.64 |
|
Dissolved oxygen |
-0.62 * |
0.27 |
-2.28 |
|
Salinity |
-0.02 |
0.26 |
-0.08 |
|
Water availability |
0.00 |
0.28 |
-0.01 |
|
Other waterhole |
-0.26 |
0.27 |
-0.96 |
|
Altitude |
-0.36 |
0.28 |
-1.27 |
|
Distance from
agricultural land |
-0.47 |
0.30 |
-1.53 |
|
Distance from
settlements |
0.41 |
0.26 |
1.56 |
|
Herb density |
0.06 |
0.38 |
0.16 |
|
Shrub density |
0.24 |
0.30 |
0.80 |
|
Crown cover |
0.17 |
0.27 |
0.63 |
|
Livestock |
-0.98 |
0.99 |
-0.99 |
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