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Received: 16 October 2023
|
Revised: 20 February 2024
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Accepted: 24 February 2024
DOI: 10.1002/ajp.23618
RESEARCH ARTICLE
Population assessment of the Endangered Kashmir Gray
Langur (Semnopithecus ajax, Pocock 1928) using the
double‐observer method
Shahid Hameed
1
|Tawqir Bashir
2
|Mohammad N. Ali
2
|
Munib Khanyari
3
|Ajith Kumar
4
1
Department of Environmental Sciences &
Centre of Research for Development,
University of Kashmir, Srinagar, India
2
Centre of Research for Development,
University of Kashmir, Srinagar, India
3
Nature Conservation Foundation, Mysore,
India
4
Centre for Wildlife Studies, Bangalore, India
Correspondence
Tawqir Bashir, Centre of Research for
Development, University of Kashmir, Srinagar
190006, India.
Email: tawqir84@gmail.com
Present address
Tawqir Bashir, Division of Wildlife Sciences,
Sher‐e‐Kashmir University of Agricultural
Sciences and Technology of Kashmir, Srinagar,
India.
Abstract
Primates are among the most threatened taxa globally, therefore, there is a need to
estimate and monitor their populations. Kashmir Gray Langur Semnopithecus ajax is
an endangered species for which there is no population estimate. We used double‐
observer method to estimate its population size in the Kashmir region of North‐
Western Himalaya. We walked 1284 km across 31 survey blocks spanning all three
divisions of Kashmir viz., North, Central, and South Kashmir, covering an area of
411 km
2
. We counted a minimum of 1367 individual langurs from 27 groups. The
detection probability for observer 1 (0.719) and observer 2 (0.656) resulted in a
population estimate of 1496 (95% confidence interval [CI] 1367–1899) across 30
groups (with a mean group size of 51), giving a density estimate of 3.64 (3.33–4.62)
langurs/km². We found double‐observer surveys to be suitable for the population
estimation of langurs, and we make recommendations on how to effectively conduct
primate surveys, especially in mountainous ecosystems. Our records extend the
species distribution range beyond stated by the International Union for Conserva-
tion of Nature. Our findings also highlight that the Kashmir Himalaya is a stronghold
of the species, where conservation efforts should focus.
KEYWORDS
conservation status, double‐observer survey, endangered, gray langur, North‐Western
Himalayas, population estimation
1|INTRODUCTION
Primates serve key ecological functions and help maintain
the ecological stability of the ecosystems they inhabit (Marshall
&Wich,2016). Despite their critical role in ecosystem function-
ing, primates are among the most threatened taxa globally
(Estrada et al., 2017) primarily due to the anthropic threats of
habitat loss, wood harvesting, and hunting (Estrada &
Garber, 2022;Estradaetal.,2018). The need to estimate and
monitor their populations is thus evident (Campbell et al., 2016).
Methods used to estimate and monitor primate populations
include total counts, camera trapping, call count, DNA‐based
methods, and distance sampling—thelastofthesebeingthemost
used (Plumptre et al., 2013). Although statistically robust,
distance sampling is not practicable on species that occur in
low densities and in rugged terrain where the assumptions (such
as 30–40 sightings and accurate estimation of sighting distance)
of this method may not be met.
Am. J. Primatol. 2024;e23618. wileyonlinelibrary.com/journal/ajp © 2024 Wiley Periodicals LLC.
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https://doi.org/10.1002/ajp.23618
Abbreviations: CI, confidence interval; Obs 1, observer 1; Obs 2, observer 2; P1 and P2, detection probability of observer 1 and observer 2 respectively.
One such species, first reported by Sir Walter R. Lawrence
(Lawrence, 1895), is the Endangered Kashmir gray langur Semnopithecus
ajax (Photograph 1, for additional photographs refer to supporting
information) (https://www.iucnredlist.org/species/39833/17943210),
occurring in the North‐Western Himalaya and extending from Himachal
Pradeshintheeast(Singh&Thakur,2020) to Jammu and Kashmir
(Jahangeer et al., 2019; Khaleel, 2020; Minhas et al., 2012;Mir
et al., 2022;Sharma&Ahmed,2017) in the west. Despite a recent
integrative taxonomic study (Arekar et al., 2021)proposingthat
Himalayan langurs should be considered a single species (Semnopithecus
schistaceus), the International Union for Conservation of Nature (IUCN)
continues to identify langurs from Northwestern Himalayas as a
separate species, known as the Kashmir gray langur (S. ajax). The
langurs mostly inhabit temperate broad leaf forests and conifer mixed
forests in the elevation range of 2200–4000 m across the Himalayas
(Kumar et al., 2020), forming uni‐and multimale bisexual groups and all‐
male groups (Singh & Thakur, 2020), averaging about 50 individuals
(Jahangeer et al., 2019). However, in Kashmir Himalayas this species
occurs in the elevation range of 1500–3200 m (Khaleel, 2020). These
langurs also show seasonal altitudinal migration (Singh & Thakur, 2020),
predominantly using moist temperate broad leaf and coniferous forests
in winter and subalpine forests during the summer (Minhas et al., 2010).
Kashmir gray langur is listed as Endangered (Kumar et al., 2020)
under criteria C2a(i), with small population (<2500 adults), continuing
decline with none of the subpopulations having >250 adults. The
decline of prey populations in the Kashmir Himalayas in recent decades
(Habib et al., 2014) signifies substantial predation pressure on langurs
due to the inadequacy of alternative prey populations, especially for
common leopards Panthera pardus (Mir et al., 2022). Moreover, the
distribution range of Kashmir gray langur falls within the area predicted
to experience an increase of 1.5°C in temperate and 5%–7.5% in
precipitation for every 1°C increase in global temperature due to climate
change (Graham et al., 2016). This has the potential to significantly alter
the habitat of the species and further threaten its long‐term survival.
The species is in Appendix I of CITES and in Schedule I Part A of the
Indian Wildlife Protection Act, 1972, amended up to 2002 (Molur
et al., 2003) giving it the highest level of protection from international
trade and hunting, respectively. Nevertheless, this species is affected by
conversion of natural forests to croplands, and other forms of land use
change in the Kashmir Himalaya (Ahmad et al., 2021,2023;Wani
et al., 2016). Dearth of information on population at the regional level
may either underrepresent or incorrectly classify a species at the global
level, thereby influencing conservation actions (e.g., Khara et al., 2021 for
Urial Ovis vigenei).
With this background, we conducted the first systematic
population estimation of the species in the Kashmir region, which is
a major part of its distribution. We used the double‐observer method
(Forsyth & Hickling, 1997) because distance sampling was not
practicable due to the rugged terrain and low density of the species.
The landscape characteristics such as rugged terrain of the study area
and better visibility in the forests during winters provided an ideal
opportunity to use double‐observer method, which has already been
used in population estimation of group living animals in mountainous
ecosystem such as Himalaya and the Tien Shan (Khanyari et al., 2021;
Leki et al., 2018; Suryawanshi et al., 2012; Thinley et al., 2019). We
also provide insights into the applicability of the double‐observer
method in the Himalayan landscape. This study sets the benchmark
to monitor future population trends, helps in prioritization of sites for
conservation, and contributes data to IUCN Red List assessment.
2|MATERIALS AND METHODS
2.1 |Study area
Located in the North‐Western Himalaya, Kashmir (Figure 1) has a
temperate climate (Romshoo et al., 2020) and also forms a part of the
Himalayan biodiversity hotspot (Myers et al., 2000). Kashmir
PHOTOGRAPH 1 Kashmir Gray Langur
from a deciduous forest in Autumn, Kashmir.
Photo Credit: Shahid Hameed.
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(15,948 km
2
), has a rugged topography with elevation ranging from
1116 to 5153 m ASL. The region has a mean annual precipitation of
~1240 mm (Bhat et al., 2022) and a temperature range of −9°C in
winters to 38°C in summers (Prasher, 2015; Shafiq et al., 2018).
Kashmir Himalaya include temperate, subalpine, and alpine vegeta-
tion (Jee, 2020). The temperate forests (1500–3000 m) have a mix of
coniferous and broad‐leaved trees dominated by Cedrus deodara,
Pinus wallichiana, Abies pindrow, Taxus wallichiana, Acer caesium, Celtis
australis, Berberis lycium, and Rosa webbiana. The subalpine forests
(3000–3600 m) are dominated by Betula utilis, A. pindrow, A. caesium,
Prunus cornuta, Juniperus squamata, Salix denticulata, and Viburnum
grandiflorum. The alpine forests (>3600 m) are dominated by alpine
scrub vegetation comprising of Salix,Rhododendron, Cotoneaster,
Lonicera, and Juniperus species (Dar & Khuroo, 2013; Haq
et al., 2020).
2.2 |Method
We used the double‐observer method (Forsyth & Hickling, 1997),
which has been refined and used for estimating populations of
mountain ungulates in Himalaya and Central Asia (Khanyari
et al., 2021; Leki et al., 2018; Suryawanshi et al., 2012) and primates
in Himalayas (Thinley et al., 2019). Double‐observer method relies on
a few field assumptions (Khanyari et al., 2019) and these include: (1)
groups of animal (i.e., langur) can be identified individually; 2) For
each survey block, observers have entire visual coverage; (3) areas
within blocks are surveyed independently by two teams separated in
time. This method has been recommended to reliably survey and
estimate populations of primates in rugged mountains such as
Himalayas (Leki et al., 2018; Thinley et al., 2019). We conducted the
survey with the permission from the concerned authorities (permit
number: WLP/Res/F‐101/20‐21/752‐54).
Before the field survey, we confirmed the presence of the
langurs in 6 out of 10 districts of Kashmir using literature search
(Khaleel, 2020; Mir et al., 2015; Sharma & Ahmed, 2017) and
interviews with wildlife officials, researchers, transhumance and
trekkers. We divided these areas with langurs into 31 smaller blocks
(Table 1) using mountain ridgelines and tree lines as boundaries, to
ensure population closure at the time of the survey and maximum
visual coverage within the survey blocks. Following Suryawanshi
et al. (2021), we premapped the survey blocks using Google Earth
FIGURE 1 Study area map showing (a) the political map of India (b) the Kashmir region with the elevation gradient, district boundaries,
International Union for Conservation of Nature (IUCN) range of Kashmir gray langur (IUCN, 2022a,2022b), and sightings during this study,
including opportunistic sightings both within and outside the surveyed areas, and (c) shows the surveyed areas. [Color figure can be viewed at
wileyonlinelibrary.com]
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TABLE 1 Information on the various sampling details such as areas surveyed, block name, block area, group size of langurs, and so on.
District Survey area Block Date of survey Time of sighting Latitude Longitude Block area No. of trails Group size Observer 1 Observer 2 Infants
Central Kashmir
Ganderbal Khodmarg Saelmarg January 22, 2022 11:24‐11:48 a.m. 34.2333 74.86424 18 2 59 1 1 7
Ganderbal Khodmarg Khodmarg January 23, 2022 09:23 a.m. 34.24347 74.83378 15 2 53 0 1 10
Ganderbal Naranag Marchoi January 28, 2022 11:23–11:56 a.m. 34.37182 75.01324 22 2 56 1 1 9
Ganderbal Naranag Marchoi 11:48 a.m.–12:17 p.m. 34.37207 75.0104 2 1 1 ‐
Ganderbal Naranag Marchoi 2:23 p.m. 34.3719 75.00591 1 1 0 ‐
Ganderbal Naranag Dumail January 27, 2022 09:47–10:12 a.m. 34.36176 74.98795 10 1 42 1 1 7
Ganderbal Poshkar Gali/Machkani January 26, 2022 ‐‐‐14
Ganderbal Mohandmarg Behanwar January 04, 2022 2:23 p.m. 34.31616 74.79363 26 3 54 0 1 7
Ganderbal Sumbal Sonmasti January 30, 2022 ‐‐‐12 4 ‐‐ ‐
Srinagar Dachigam NP Zahul January 17, 2022 3:14 p.m. 34.11935 74.94937 11 2 3 0 1 ‐
Srinagar Dachigam NP 2:45 p.m. 34.12148 74.94122 24 1 0 2
Srinagar Dachigam NP Oak Patch/Daphama January 14, 2022 07:46–08:12 a.m. 34.14022 74.93156 07 1 206 1 1 38
Srinagar Dachigam NP Upper Draphama January 14, 2022 10:43–11:12 a.m. 34.1277 74.94212 06 2 32 1 1 4
Srinagar Dachigam NP Pannar/Padshahdub January 15, 2022 03:54–04:14 p.m. 34.15353 74.94109 09 2 234 1 1 29
Srinagar Dachigam NP Minuv/Drogg January 16, 2022 11:03–11:26 a.m. 34.1158 74.96552 11 3 32 1 1 5
Srinagar Dachigam NP Pahilpur Nallah January 16, 2022 10:13–10:39 a.m. 34.1172 74.98063 08 1 63 1 1 11
Srinagar Dachigam NP Vaskhar January 18, 2022 3:26 p.m. 34.11912 74.98809 09 1 1 1 0 ‐
Srinagar Dachigam NP Kotnar January 19, 2022 03:19–03:34 p.m. 34.11263 74.97635 08 1 52 1 1 8
Srinagar Dachigam NP Chandar Nar January 19, 2022 09:59–10:33 a.m. 34.1138 74.97138 09 1 71 1 1 11
Srinagar Dachigam NP Kawnaar January 15, 2022 08:46 a.m. 34.11892 74.94877 09 1 1 1 0 ‐
Srinagar Dachigam NP Raznarig January 17, 2022 10:53–11:29 a.m. 34.13055 74.93547 09 2 6 1 1 ‐
Srinagar Mahadev/Dara Lidwas January 20, 2022 ‐‐‐13 2 ‐‐‐‐
South Kashmir
Anantanag Overa WLS Mahraja Hut November 29, 2021 09:53–10:39 a.m. 33.95222 75.25346 11 2 73 1 1 16
Anantanag Overa WLS Wehnar November 28, 2021 3:56 p.m. 33.95394 75.25893 09 2 3 1 0 ‐
Anantanag Rajparian WLS Daksum November 23, 2021 ‐‐‐14 2 ‐‐‐‐
Pulwama Tral Shikargah November 08, 2022 09:23 a.m. 33.89537 75.14127 14 1 83 1 0 12
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Pro, and sometimes adjusted the area of these blocks retrospectively
after fieldwork, as the surveyed area did not always precisely align
with the initially planned survey block. These blocks covered non‐
protected as well as protected areas including the Dachigam and
Kazinag National Parks; Limber, Lachipora, Hirpora, Overa‐Aru,
Rajparian, and Gulmarg Wildlife Sanctuaries; and Tral and Wangath
Conservation Reserves. To enhance species detection, we conducted
surveys of these blocks during the winter months (November
2021–January 2022) when the langurs migrate to lower elevations
(Minhas et al., 2010) and also form large groups (Khaleel, 2020). We
used mountain ridgelines and the uppermost trails to increase the
detection of this arboreal langur. In each survey block, we walked
1–4 trails. In certain instances, we entered the forests to obtain group
counts.
We used simultaneous survey (Khanyari et al., 2021) with the
first observer team (of two persons) starting at ~0800 h or 14 h and
the second team following the same trail with a delay of 30–60 min.
These times coincided with the foraging/feeding time of the langurs
(Khaleel, 2020).
To meet the assumption of population closure, we strategi-
cally defined survey blocks based on: terrain characteristics,
visual coverage of observer, species' home range, and daily path
length (Minhas et al., 2013); surveyed each block within a day and
adjacent blocks either on the same day or the subsequent day.
Theblocksweresmallenoughtogetfullvisualcoverageofthe
area, and in instances of surveying the larger blocks (i.e.,
Mohandmarg) or the blocks with complex configurations of
terrain (i.e., Sonmasti), we increased the number of observer
teams and the number of trails. At the end of each survey day, the
two observer teams compiled the sightings to identify groups
seen by either or both teams.
During the field surveys, categorizing these langurs into distinct
age‐sex groups was challenging due to their dense body coat (fur),
arboreal behavior, and evasive nature. To avoid the chances of
double‐counting of groups, we relied on time and location of
sightings, group size, and the number of infants, to distinguish
between different groups (Table 1).
2.3 |Data analysis
We estimated the total number of langur groups using the two‐
survey mark‐recapture in the “BBRecapture”package, which uses the
Bayesian framework in R statistical and programming environments
(Fegatelli & Tardella, 2013). We modeled the detection probability of
the two teams separately (“mt”model) (Suryawanshi et al., 2012). The
detection probabilities for observer one (P1) and observer two (P2)
were interpreted as the estimated detection probability of model
“mt”for occasions one and two, respectively. To estimate the number
of groups (Ĝ) across all the survey blocks in our study area, we fitted
the “mt”model using the function BBRecap with a “uniform prior.”As
this was the first time we were using this method in this study area,
we decided to use uninformed uniform priors. We conducted 10,000
TABLE 1 (Continued)
District Survey area Block Date of survey Time of sighting Latitude Longitude Block area No. of trails Group size Observer 1 Observer 2 Infants
Shopian Hirpora Dubjin November 03, 2021 ‐‐‐12 2 ‐‐‐‐
Shopian Hirpora Mia Ka Bela November 04, 2021 ‐‐‐16 2 ‐‐‐‐
Pulwama Tral Satura November 09, 2021 04:03–04:24 34.06477 75.0671 11 1 52 1 1 6
North Kashmir
Baramulla Gulmarg WLS Botepathar/Alefarm December 07, 2021 ‐‐‐18
Baramulla Lachipora A Brendward December 14, 2021 02:09–02:41 p.m. 34.19705 74.07227 18 1 54 1 1 7
Baramulla Lachipora B Neelsar December 15, 2021 09:39 a.m. 34.21056 74.09174 22 2 48 0 1 5
Baramulla Limber Gratnar December 11, 2021 2:28 p.m. 34.2008 74.15286 24 2 28 0 1 3
Baramulla Limber Mithwayen December 12, 2021 11:41 a.m. 34.21469 74.16269 16 2 34 1 0 6
Note: Codes 1 and 0 in the observer columns mean group sighted and group not sighted, respectively.
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MCMC iterations with 1000 burn‐in. Further details on this are
provided by Fegatelli and Tardella (2013).
We estimated the total population (N
est
) as a product of the
number of groups (Ĝ) and mean group size (μ). To estimate
confidence intervals (CI), we used the variance in the estimated
number of groups and the mean group size. We generated a
distribution of the estimated group size by bootstrapping 10,000
times with replacements. The distribution of N
est
was generated by
multiplying 10,000 random draws of the estimated number of groups
(Ĝ) weighted by the posterior probability and draws of the mean
group size (μ). The median of the resultant distribution was the
estimated langur population (N
est
), and the 2.5th and 97.5th
percentiles were used as confidence intervals.
To arrive at the densities, we divided the estimated population by
the total sampled area. The sum of surveyed blocks was determined
using the built‐in function on Google Earth Pro.
3|RESULTS
We recorded 1367 langurs across 31 surveyed blocks covering
411 km
2
of area during 1284 km of the survey effort. The observed
group size ranged from 1 to 234 individuals (mean = 51, n= 27). The
detection probabilities of the two teams were 0.719 and 0.656 were
comparable. The estimated number of groups was 30 (Figure 2a,b)
and the estimated population was 1496 individuals (95% confidence
interval [CI]: 1367–1899) giving a density of 3.64 (3.33–4.62) langur
km
2
(Figure 2c). Table 2provides information on all the double‐
observer parameters for our population assessment. The populations
of the Central, South, and North divisions were estimated to be 1022
(CI = 922–1304, P1= 0.75, and P2 = 0.71), 263 (CI = 211–595,
P1 = 0.72, and P2 = 0.43), and 410 (CI = 163–1314, P1 = 0.34, and
P2 = 0.48) respectively. The estimated group size across divisions
varied between 41 and 53 (North 41, Central 52, and South 53). The
population of gray langur in Dachigam National Park (144 km
2
) was
785 (CI = 725–1017) with an estimated mean group size of 60
individuals, including detection probabilities for observer 1 and
observer 2 to be 0.806 and 0.672, respectively.
4|DISCUSSION
4.1 |Population estimation
The primary objective of this study was to estimate the population
abundance of the Kashmir Gray Langur in the Kashmir Himalayas
using the double‐observer survey method and to gain insights into
the applicability of this method to survey primates in mountain
regions. Our results indicate that the double‐observer survey method
is statistically robust and can be used to estimate arboreal primate
FIGURE 2 Panel graph showing (a) the
posterior probability distribution estimated by
the “mt”model for the range of the number of
langur groups, (b) histogram of 10,000
bootstrapped means of langur group size. The
vertical solid line indicates the median and (c)
the histogram of the estimated langur
population. The solid line indicates the median
and the dotted lines indicate the 95%
confidence interval.
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populations in the mountainous terrains. This is because (1) our
population estimate was relatively precise 1496 (1367–1899)
langurs, (2) both observers had relatively high and comparable
detection probability (Obs 1 = 0.719 and Obs 2 = 0.656), and (3) we
were able to fulfill the assumptions of the survey method. Because
the group size and observers' distance influence detection probability
(Forsyth & Hickling, 1997), the relatively high detection probabilities
can be partly ascribed to the two independent survey framework of
the double‐observer method allowing multiple observers to comply
for imperfect detections (Forsyth et al., 2022), and partly to
conducting the surveys in the winter season when visibility is high,
and also langurs form larger groups during this season.
To ensure the accuracy of the double‐observer survey, we
adapted the survey to field conditions such as avoiding the areas with
high human interference and the trails that affect visual coverage,
and we suggest the same for future surveys as well. To optimize the
sampling effort and increase the detection probability, we conducted
surveys during the winter (November 2021 to January 2022). During
this period, the upper reaches receive high snowfall, and langur
groups move to lower elevations (Khaleel, 2020; Minhas et al., 2010).
They also form large group sizes, some being >200 individuals
(Table 1). We also had a few single large male sightings, which we
assume were dispersing males, similar to those reported earlier from
Dachigam National Park (Mir et al., 2015). Therefore, we recommend
future population estimation studies to integrate the understanding
of species ecology and behavior into the sampling design.
Among the three surveyed divisions, observers recorded low
detection probabilities (P1 = 0.34, P2 = 0.48) in the northern division
which could be attributed to the restricted access to border areas
leading to the selection of trails with compromised visual coverage, or
due to low density of langurs in this division. Low detection
probability could also result from evasive behavior of groups which
maybe heightened in regions where langurs are not used to human
presence and/or persecuted (e.g., see Argali information in Khanyari
et al., 2021). However, this needs further research. The high
detection probabilities recorded in the Central Kashmir, particularly
in Dachigam National Park (P1 = 0.806 and P2 = 0.672), may be
ascribed to trails with comparatively greater visibility, presence of
open habitats, high density of langurs, with 19 of a total of 28 groups
alone sighted in this division. The overall detection probabilities of
the two observers (Obs 1 = 0.719 and Obs 2 = 0.656) were
comparable suggesting both the observers have detected more
groups in common and missed few groups. The slightly lower overall
detection probability of the second observer can be due to the
evasive nature of the species indicating their avoidance of human
presence during surveys. For example, in the case of the Dachigam
National Park, langur groups inhabiting the lower elevations where
human activities such as patrolling, wildlife tourism, and research
activities are more prevalent, tend to display lesser wariness towards
human presence compared to langur groups inhabiting higher
altitudes within the same National Park.
Our study revealed a more extensive spatial distribution of
langurs compared to the recent findings reported by Khaleel (2020).
In addition to corroborating previous records, we documented the
presence of langurs in previously unreported areas outside of
protected areas such as Satura in the Pulwama, Gagarpathri Ajas in
Bandipora, Batkot in Anantanag, Mohandmarg and Khodmarg in
Ganderbal, and Chanowali and Chandimar in Ponch.
In addition to population assessment, a double‐observer survey
could be used to better understand the distribution and habitat use of
langurs since it reduces observer‐specific detection biases and
enhances likelihood of detecting animals across a wider range of
habitats. During our surveys, groups were mostly sighted near or
adjacent to cliffs, gorges, mountain streams, and in areas with
TABLE 2 Population estimation parameters of the Kashmir Gray Langur assessed through the double‐observer method in three divisions.
Double‐observer parameters Central Kashmir South Kashmir North Kashmir Overall
Population estimates 1022 263 410 1496
Lower confidence Intervals (2.5%) 922 211 163 1367
Upper confidence Intervals (97.5%) 1304 595 1314 1899
Estimated no. of groups 16 5 6 30
Mean group size 41 52 53 51
Range of group sizes 1–234 3–83 28–54 1–234
Common groups 12 2 1 15
Unique groups obs. 1 2 2 1 7
Unique groups obs. 2 1 0 2 5
P1 (Obs. 1) 0.75 0.72 0.34 0.719
P2 (Obs. 2) 0.71 0.43 0.48 0.656
Survey area (km
2
) 226 87 98 411
Density (langur km
−2
) 4.52 (4.07–5.76) 3.02 (2.42–6.83) 4.18 (1.66–13.40) 3.64 (3.33–4.62)
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broad‐leaved deciduous vegetation, particularly the habitat patches
representing Himalayan horse chestnut (Aesculus indica), Kashmir elm
(Ulmus wallichiana), common oak (Quercus robur), Himalayan viburnum
(Viburnum grandiflorum), and Parrotia (Parrotiopsis jacquemontiana).
4.2 |Using double‐observer surveys to better
inform species conservation
Our study is similar to recent studies on the endemic and endangered
Zanzibar Red Colobus Monkey and Golden Langur (Davenport
et al., 2019; Thinley et al., 2019), which used total counts and
double‐observer methods to establish population baselines. Kashmir
gray langur is listed as endangered by the IUCN with limited data on
population and distribution (Kumar et al., 2020). Our study shows
that langur populations are restricted to certain pockets rather than
large contiguous habitats (see Figure 2; Kumar et al., 2020). While
anecdotal evidence suggests that approximately 1500 mature
Kashmir gray langurs exist across the species range (Kumar
et al., 2020; Molur et al., 2003), our study suggests that this number
might be underestimated, considering that we did not survey large
regions in the Chenab landscape (i.e., the Jammu region of Jammu
and Kashmir Union Territory) and the Chamba region of the adjoining
state of Himachal Pradesh, which are potential homes for the
Kashmir gray langur.
This study highlights the importance of Dachigam National Park
in langur conservation as a stronghold for this species, harboring
nearly half (785, CI: 725–1017) of the total estimated population.
However, across the sites within the protected areas (e.g., Dachigam
National Park) and outside (e.g., Mohandmarg), langurs and their
habitats face serious threats. Broad‐leaved deciduous forests, which
are important for langurs, particularly in harsh winters, are
increasingly threatened by climate change (Graham et al., 2016)
and anthropogenically driven habitat loss (Haq et al., 2020; Molur
et al., 2003). Crop raiding by langurs and retaliation by crop owners'
impact both people's livelihoods and langur conservation (Molur
et al., 2003). Periodic population surveys using double‐observer
method and threat assessments can help us identify conservation
hotspots, and assess population trends (Mihoub et al., 2017); this can
form the benchmark to evaluate impact of conservation interventions
on the species' population.
5|CONCLUSION
The first population estimation of the Kashmir gray langur using the
double‐observer method proved reliable with respect to the precision
of the estimates. The results of this study provide valuable
information on the abundance of this species and will help
conservationists and wildlife managers better understand and protect
this endangered species. We encourage researchers to use other
population estimation methods in a comparative manner to further
investigate the robustness of double‐observer method. We found
that the langurs have a wider distribution than previously perceived,
with populations distributed in pockets across the Kashmir Hima-
layas. The population in the area surveyed (411 km
2
) was approxi-
mately 1496 individuals (95% CI: 1367–1899) across 30 groups
(mean group size 51), with some variations among different areas.
Our results suggest that the global population of Kashmir gray
langurs is likely to be higher than the IUCN estimation, as we
surveyed only a portion of the species range. Further research using
methods like double‐observer in other regions of the Kashmir
Himalaya and Himachal Pradesh may help to paint a more
comprehensive picture of the overall distribution and population
status of the Kashmir gray langur in the North‐Western Himalaya,
and contribute towards the development of effective conservation
strategies for the species. Furthermore, we encourage the use of this
method to estimate populations of other group‐living primate species
inhabiting high‐altitude ecosystems, with prior testing of its
applicability and transferability to other species and regions.
AUTHOR CONTRIBUTIONS
Shahid Hameed: Conceptualization (equal); data curation (lead);
formal analysis (lead); funding acquisition (equal); investigation (lead);
methodology (equal); software (lead); validation (equal); visualization
(equal); writing—original draft (lead); writing—review and editing
(equal). Tawqir Bashir: Conceptualization (equal); funding acquisi-
tion (equal); methodology (equal); project administration (lead);
resources (lead); supervision (lead); validation (equal); visualization
(equal); writing—original draft (equal); writing—review and editing
(lead). Mohammad N. Ali: Resources (supporting); supervision (equal);
writing—review and editing (supporting). Munib Khanyari: Formal
analysis (equal); methodology (supporting); validation (supporting);
visualization (equal); writing—original draft (supporting); writing—
review and editing (equal). Ajith Kumar: Supervision (equal); valida-
tion (equal); visualization (equal); writing—original draft (supporting);
writing—review and editing (equal).
ACKNOWLEDGMENTS
We thank the Wildlife Biology Laboratory at the Centre of Research
for Development (CORD), University of Kashmir for providing
necessary facilities to support this work. We are grateful to the
Department of Wildlife Protection (DWP), Government of J&K for
providing necessary permissions required for surveying the protected
areas of the region. We extend our heartfelt appreciation to the
dedicated individuals who generously volunteer their time and efforts
to contribute to the success of this survey. Their passion and
commitment play a vital role in this endeavor and their names are
Ubaid Rasool, Owais Shafi, Aamir Hassan, Zahid Rasool, Momin John,
War Mohsin, War Faisal, Faisal Qayoom, Danish Afzal, Basit Fazili,
Farhan Lone, Shahid Shakeel, War Momin, Umar Rashid, Suhail
Showkat, Zeeshan Rashid, and Sheikh Rizwan. We also thank Dr.
Kulbhushansingh Ramesh Suryawanshi for generously volunteering
his time and expertise to review the first draft of this paper.
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CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
The evidence supporting our findings can be provided on request
from the corresponding author.
ORCID
Shahid Hameed http://orcid.org/0000-0001-9190-3472
Tawqir Bashir http://orcid.org/0000-0001-5176-0657
Mohammad N. Ali http://orcid.org/0000-0002-5128-9284
Munib Khanyari http://orcid.org/0000-0003-4624-5073
Ajith Kumar http://orcid.org/0000-0003-0529-1938
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SUPPORTING INFORMATION
Additional supporting information can be found online in the
Supporting Information section at the end of this article.
How to cite this article: Hameed, S., Bashir, T., Ali, M. N.,
Khanyari, M., & Kumar, A. (2024). Population assessment of
the Endangered Kashmir Gray Langur (Semnopithecus ajax,
Pocock 1928) using the double‐observer method. American
Journal of Primatology, e23618.
https://doi.org/10.1002/ajp.23618
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