Journal of Threatened
Taxa | www.threatenedtaxa.org | 26 May 2026 | 18(5): 28750–28769
ISSN 0974-7907 (Online) | ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.10251.18.5.28750-28769
#10251 | Received 11 November 2025 | Final received 03 April 2026|
Finally accepted 23 April 2026
Rapid camera-trap assessment of
mammals in Tripura, India: new records and implications for conservation
Omkar Patil
1, Ashutosh Joshi 2,
Rutuja Digaskar 3
& Amey Parkar 4
1–4 Vivek
PARC Foundation 208, 2nd Floor, Shilpin
Centre, 40, GD Ambekar Marg, Dadar East, Wadala,
Mumbai, Maharashtra 400031, India
1 opatil934@gmail.com
(corresponding author), 2 asjoshis@gmail.com, 3 rutujadigaskar01@gmail.com,
4 ameyparkar9@gmail.com
Abstract: This study presents first ever
rapid camera-trapping assessment of mammals across protected areas of Tripura,
northeastern India, located within the Indo-Burma biodiversity hotspot. Surveys
were conducted between January and April 2024 in Sepahijala
Wildlife Sanctuary, Clouded Leopard National Park, Trishna
Wildlife Sanctuary, Bison National Park, and Gumti
Wildlife Sanctuary, resulting in 469 trap nights. A total of 19 mammalian
species belonging to 16 genera, 10 families, and four orders were documented. Trishna Wildlife Sanctuary recorded the highest species
diversity, followed by Sepahijala and Gumti. This study features the first photographic evidence
of the Ferret Badger, range extensions for the Malayan Porcupine and the
Fishing Cat. These findings fill important distribution gaps and highlight the
conservation significance of Tripura’s fragmented forests and wetland mosaics.
Despite their small size and increasing anthropogenic pressures, the protected
areas of Tripura support a diverse mammalian assemblage. The study demonstrates
the value of rapid, pragmatic field approaches for generating essential
ecological information under resource constraints and underscores the need for
continued monitoring and regional connectivity planning.
Keywords: Biodiversity monitoring,
camera-trapping, habitat connectivity, Indo-Burma biodiversity hotspot,
landscape fragmentation, pragmatism.
Editor: Bhargavi Srinivasulu,
Zoo Outreach Organisation, Hyderabad, Telangana,
India. Date of publication: 26
May 2026 (online & print)
Citation: Patil, O., A. Joshi, R. Digaskar
& A. Parkar (2026). Rapid
camera-trap assessment of mammals in Tripura, India: new records and
implications for conservation. Journal of
Threatened Taxa 18(5):
28750–28769 . https://doi.org/10.11609/jott.10251.18.5.28750-28769
Copyright: © Patil et al. 2026. 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: The Habitats Trust.
Competing interests: The authors declare no competing interests.
Author details: Omkar Patil is an ecologist whose work primarily revolves around carnivore & landscape ecology and human animal interactions. Ashutosh Joshi functions as a research advisor to the wildlife research division of the Vivek PARC Foundation. He holds a PhD in environmental sciences. Rutuja Digaskar is an ecologist whose work focuses on wild carnivores in high altitude landscapes. Amey Parkar is currently pursuing masters in Zoology from the Homi Bhabha State University & functioned as an intern in the Vivek PARC Foundation. He actively works in understanding the ecology of mammals & birds.
Author contribution: OP, AJ and AP contributed to data collection. Study conception, study design, preparation, data collection and analysis were performed by OP & AJ. The first draft of the manuscript was written by OP and all authors contributed to refining & revising the manuscript.
Acknowledgments: We thank the Tripura Forest Department for research permits, opportunity, and continuous support during the study. We are grateful to The Habitats
Trust for funding, camera traps and technical assistance. Field support from the ground staff, Banmitras, and field facilitators was invaluable. We thank the directors and team of Vivek PARC Foundation for their guidance. Special thanks to our team
members Divyajit Bal, Ninad Ajanikar, Arthur Lewis, Raj Jadhav, Kartheek Thevar, Sherly Barbosa, and Shrikar Ashtaputre for their efforts during fieldwork and data compilation.
Introduction
Tropical regions of southern and
southeastern Asia support some of the richest yet most threatened biodiversity
on Earth (Woodruff 2010). Within this region, India harbours
exceptional mammalian diversity across varied landscapes ranging from the
Himalaya to the Indo-Burma and Western Ghats hotspots (Myers et al. 2000).
Despite this richness, many landscapes remain poorly studied due to their
remoteness, limited accessibility and research bias toward established
protected areas (Datta et al. 2008; Jhala et al. 2020). These gaps limit our ability to
generate spatially explicit and quantitative data on species occurrence and
abundance, particularly in smaller or peripheral reserves that function as
critical ecological corridors.
Tripura, located in the northeastern
corner of India, forms an ecological bridge between the Indian subcontinent and
southeastern Asia. The state lies within the Indo-Burma biodiversity hotspot
and supports a mosaic of tropical evergreen, semi-evergreen and moist deciduous
forests (Champion & Seth 1968; Deb 1981). Its geographic position between
the hill forests of Bangladesh and Mizoram enables biological exchange between
the Indian and southeastern Asian faunal realms (Datta
et al. 2008). While previous studies have documented mammalian diversity in
Tripura through checklists and regional accounts (Majumder et al. 2015;
Majumdar & Datta 2018; Talukdar et al. 2021),
these efforts have been descriptive and presence-based.
Existing knowledge of mammals in
Tripura is therefore limited in three key ways: (i) a
lack of standardised, effort-based surveys, (ii)
absence of quantitative data on relative abundance and detection patterns, and
(iii) minimal understanding of species distribution across multiple protected
areas within a unified sampling framework. Most available studies rely on
opportunistic records, localised surveys or
species-specific investigations, such as those of Western Hoolock Gibbon Hoolock
hoolock and Gaur Bos gaurus
(Gupta et al. 2005; Dasgupta et al. 2008). While valuable, these studies do not
provide comparable or landscape-level insights necessary for conservation
planning.
In this context, camera-trapping
offers a robust, non-invasive and standardised method
for generating comparable data on species occurrence, detection, and relative
abundance across sites. However, prior to this study, no landscape-scale,
systematic camera-trapping assessment had been conducted across the protected
areas of Tripura. This represents a critical knowledge gap, particularly given
the increasing anthropogenic pressures on the state’s forests.
Tripura’s forest landscapes are
undergoing rapid change due to shifting cultivation, illegal logging, expansion
of rubber plantations and infrastructure development (Gupta 1998; Chatterjee
2008). These drivers contribute to internal fragmentation and alter habitat
connectivity. In such dynamic and resource-constrained contexts, long-term
ecological studies are often challenging. Rapid, exploratory biodiversity
assessments when implemented using standardised
protocols, provide an effective approach to generate baseline data and identify
conservation priorities within limited time frames (Wearn
& Glover-Kapfer 2017)
A pragmatic research approach is
therefore essential, one that balances scientific rigor with logistical
feasibility. Such approaches emphasize adaptive field strategies, stakeholder
participation and collaboration with local forest departments to ensure both
data quality and management relevance (Sutherland et al. 2004; Mishra et al.
2017; Palencia et al. 2022).
Against this backdrop, the
present study was designed as an exploratory, landscape-scale camera-trapping
assessment to address critical data deficiencies in Tripura. Specifically, the
study aimed to: (i) document mammalian occurrence
using standardized camera-trapping across protected areas, (ii) generate
effort-based indices of relative abundance and detection, and (iii) establish a
baseline dataset to support future monitoring and management interventions by
the Tripura Forest Department.
This study represents the first
systematic, multi-site camera-trapping exercise conducted across protected
areas in Tripura, moving beyond opportunistic and checklist-based approaches to
provide a quantitative and comparable understanding of mammalian communities.
The rapid assessment was conducted between January and April 2024 as a
collaborative initiative between the Vivek PARC
Foundation, The Habitats Trust, and the Tripura Forest Department.
Materials
and Methods
Study area
The study was conducted across
the protected areas of Tripura: Sepahijala Wildlife
Sanctuary (SWS), Clouded Leopard National Park (CLNP), Trishna
Wildlife Sanctuary (TWS), Bison National Park (BNP), Gumti
Wildlife Sanctuary (GWS), and Rowa Wildlife Sanctuary
(RWS). CLNP falls within SWS and is hereafter collectively referred to as SWS.
Similarly, BNP, located within TWS, is referred to as TWS. Together they
represent the principal habitats of the state, covering approximately 613 km².
These areas include tropical evergreen, semi-evergreen and moist deciduous
forest types interspersed with bamboo brakes and cultivation zones (Deb 1981)
(Table 1, Figure 1).
Tripura has about 59.89% forest
cover (Majumdar & Datta 2018). Historical
accounts indicate that the forests of Tripura have long been subjected to
anthropogenic pressures, particularly shifting cultivation, timber extraction,
and plantation expansion (Gupta 1998). Climatically, the region experiences
four distinct seasons, winter, summer, monsoon, and post-monsoon, with annual
rainfall of 2,000–2,500 mm (Debnath et al. 2021). The present survey, conducted
between January and April 2024, represents a winter to pre-monsoon sampling.
This period was suitable for camera trapping as reduced water availability concentrates
animal activity around water sources, enhancing detection probability.
Additionally, soft substrates near water bodies facilitated the identification
of animal signs such as pugmarks, aiding in optimal camera placement (Rovero & Zimmermann 2016).
Sampling design
A grid-based camera-trap sampling
framework was used following Rajaratnam et al. (2007) and Rovero
& Zimmermann (2016). Grids of 2 km² were overlaid across each protected
area. Camera traps were deployed in singles and pairs wherever feasible. In
smaller reserves such as Rowa and Sepahijala,
the grid layout was adjusted to accommodate limited area and access. Camera
placement was guided by evidence of animal activity, such as pugmarks or scats,
and grids with excessive human disturbance were avoided to minimize the bias
and theft. Within TWS, 25 grids were selected for sampling, of which 14 were
sampled, with all trap stations equipped with paired camera traps. In GWS, 48
grids were selected, of which 29 were sampled; camera traps were deployed in
pairs at 15 stations and singly at 14 stations. In SWS, seven camera trap
locations were selected, all with paired deployments. Similarly, in RWS, four
camera trap locations were sampled, each with paired camera traps. Camera traps
deployed singly were placed in areas with high human activity to reduce the
risk of theft; this approach may have introduced detection bias, as a single
camera covers a limited field of view compared to paired deployments.
The study followed a rapid
assessment protocol emphasizing spatial coverage and standardized deployment
over long-term replication. This design provides robust data on species
presence and relative abundance but does not allow estimation of absolute
densities or seasonal variation (O’Brien 2011).
Infrared and white-flash cameras
(Cuddeback X-Change and Spartan Lumen models) were
deployed for a maximum of 10 nights per location. Each site was geo-referenced,
and details such as habitat type, elevation and proximity to human settlements
were recorded. Cameras were positioned along trails or watercourses frequently
used by wildlife. At certain locations, cameras were placed facing each other
to document both flanks of patterned species (Johnson et al. 2009). Camera
traps were mounted on trees or placed within small rock cavity-like structures
at a height of approximately 30–45 cm above ground to target small-to
medium-sized mammals. Additionally, two camera traps were opportunistically
installed on trees at higher positions to sample arboreal mammals.
Camera traps were checked
periodically by Tripura Forest Department staff and Banmitras.
Cameras were concealed using natural vegetation and secured with locking
cables.
Captured images were reviewed
manually, and species identifications were verified independently by two
observers (Images 1–17). Ambiguous images were labelled as “unidentified” and
excluded from species-level analyses.
Detection bias and limitations
Camera detection probability
varies among species due to body size, activity pattern and vegetation density.
Smaller or arboreal mammals are generally under-detected (Burton et al. 2015).
Consequently, relative abundance index (RAI) values are interpreted as indices
of activity rather than true abundance. For species with fewer than 10
independent detections, results were treated qualitatively. Future monitoring
could adopt occupancy or spatially explicit capture-recapture (SECR) models
when sample sizes meet analytical thresholds (Efford
& Fewster 2012; Wearn
& Glover-Kapfer 2017).
Further, rarefaction curves were
used to standardize species richness across sites with unequal sampling effort,
enabling robust comparisons despite differences in trap-nights and
detectability. They also help assess sampling adequacy by indicating whether
additional effort is likely to record new species, making them particularly
suitable for camera-trap studies (Gotelli &
Colwell 2001; Magurran 2004).
Training, community engagement
and ethics
Before field deployment, hands-on
training sessions were organized for forest staff and Banmitras,
focusing on equipment setup, troubleshooting and maintenance. Local communities
around each protected area were informed about camera placement to minimize
interference and promote awareness. No animals were handled or disturbed during
the study, and all research was conducted under permits issued by the Tripura
Forest Department.
Data management and quality
control
All image files were
systematically named using protected area and grid identifiers in the following
format- ‘Name-of-PA_Forest-Range_Grid-ID’
(for example, GUM_GAN_CT1). Metadata, including coordinates, habitat
type, deployment date and personnel involved, were recorded in standardized
field sheets. Images and metadata were stored in duplicate on external drives.
Data analysis
Relative abundance index (RAI):
RAI was used to estimate species
activity across protected areas, serving as a cost-effective, non-invasive
index widely applied in wildlife monitoring and management (O’Brien 2011).
RAI = (No. of independent
detections/Total trap nights) * 100
Independent detections were
defined as images of the same species separated by at least 30 min to avoid
false replication (Rovero & Marshall 2009), and
RAI values were calculated per protected area for comparative assessment of
relative occurrence.
Diversity indices and hill
numbers:
Biodiversity was quantified using
two commonly applied indices: the Shannon-Wiener index (H′) and the Simpson’s
index (D or 1–D).
where pᵢ is the proportion of
detections of species i and S is the
total number of species detected. A higher H′ value indicates greater species
diversity and evenness (Spellerberg & Fedor 2003).
Simpson’s index (D) was
calculated as
where pᵢ is the same as above.
Higher values of D indicate dominance by few species, whereas values of (1–D)
closer to 1 represent higher overall diversity. These indices were calculated
for each protected area to compare community structure and dominance patterns
(He & Hu 2005).
To facilitate more intuitive and
comparable interpretation of diversity across sites, the Shannon-Wiener (H′)
and Simpson (D) indices were converted to hill numbers (Hill 1973; Jost 2006). Traditional diversity indices are non-linear
and expressed in units that are difficult to compare directly, whereas hill
numbers transform these indices into the “effective number of species,”
representing the number of equally abundant species required to produce the
observed diversity. This approach enables meaningful comparisons across sites
with varying sampling effort and community structure, and is increasingly
recommended as a standard in ecological studies (Jost
2006; Chao et al. 2014).
Shannon-Wiener (H′) and Simpson
(D) indices were converted to Hill numbers using
Rarefaction curve analysis
Independent camera-trap
detections filtered using a 30-min temporal independence threshold were used to
construct species-by-sampling-unit incidence matrices for each protected area,
excluding cattle to avoid bias from domestic animals. Each grid cell (grid_id)
was treated as a sampling unit and species presence-absence was calculated for
per unit. Sample-based rarefaction curves were generated using the ‘specaccum()’ function in the ‘vegan’
package in R, applying the random method with 1,000 permutations to estimate
mean richness and associated 95% confidence intervals (Oksanen
et al. 2022), following established protocols for sample-based accumulation
curves (Gotelli & Colwell 2001; Magurran 2004). Although coverage-based standardization is
recommended when detection probabilities vary across sites (Chao & Jost 2012), sample-based rarefaction was used here to align
with the dataset structure and sampling design.
Analytical approach
Given moderate detection rates,
RAI and diversity indices were used as the primary comparative metrics.
Occupancy or SECR models were not applied since individual recaptures per
species were below the analytical threshold of 20 independent detections (Efford & Fewster 2012). All
calculations were performed using Microsoft Excel and biodiversity indices were
computed using R v4.3.2. Spatial visualization was conducted in QGIS 3.30.2.
Results
A total of 19 mammalian species
were documented from all the protected areas through a rapid camera-trapping
survey conducted between January and April 2024 (Table 2). Rowa
Wildlife Sanctuary was not included in the analysis due to insufficient data
from camera traps. During field surveys, Barking Deer Muntiacus
vaginalis was captured in a single camera trap, and direct sightings of
Capped Langur Trachypithecus pileatus, Phayre’s Leaf
Monkey Trachypithecus phayrei,
and Rhesus Macaque Macaca mulatta were recorded, indicating the continued
presence of these species in the sanctuary. A cumulative sampling effort of 469
trap nights was achieved across all three protected areas, with the highest
effort recorded in GWS (280 trap nights), followed by TWS (122) and SWS (77)
(Table 3). Five cameras that malfunctioned or were lost were excluded from
analyses.
The number of independent
detections ranged from 46 in GWS to 150 in SWS (including 114 detections from
TWS), collectively yielding 292 independent photographic captures. Despite
variation in sampling effort, all the protected areas exhibited substantial mammalian
activity, indicating diverse assemblages across the landscape.
Across all study sites, 19
species belonging to 10 families and four orders were recorded (Table 4). These
included representatives of Carnivora, Primates, Artiodactyla,
and Rodentia. Out of the 19 species, 17 were recorded in the camera traps while
Capped Langur and Western Hoolock Gibbon Hoolock hoolock
were sighted.
Key species detected were Rhesus
Macaque, Pig-tailed Macaque Macaca leonina, Phayre’s Leaf
Monkey, Leopard Cat Prionailurus bengalensis, Common Palm Civet Paradoxurus
hermaphroditus, Small Indian Civet Viverricula indica,
Wild Pig Sus scrofa,
and Barking Deer.
The photographic evidence of
Fishing Cat Prionailurus viverrinus in TWS and Small-clawed Otter Aonyx cinerea in TWS
provides significant records of wetland-dependent species within the Indo-Burma
landscape.
Abundance Index (RAI)
The relative abundance index
(RAI) revealed spatial variation in species activity across the three protected
areas. In SWS, the Rhesus Macaque exhibited the highest RAI (69.12 ± 34.74),
indicating strong primate activity within the sanctuary. Other frequently
captured species were Large Indian Civet Viverra
zibetha (25.00 ± 14.43), Pig-tailed Macaque
(17.65 ± 8.66) and Common Palm Civet (14.71 ± 10.03). Herbivores such as
Barking Deer (11.76 ± 6.42) and Wild Pig (10.29 ± 5.94) were also well
represented.
In TWS, the Common Palm Civet
(21.95 ± 10.03) and Pig-tailed Macaque (8.13 ± 8.66) were most abundant, while
Gaur Bos gaurus (4.88 ± 2.82) and Small-clawed
Otter Aonyx cinerea
(3.25 ± 1.88) represented key large and semi-aquatic mammals, respectively. The
photographic record along with numerous sightings of multiple troops of Phayre’s Leaf Monkey reaffirmed the site’s role as a
crucial habitat for this threatened primate.
In GWS, overall detection rates
were lower, with Crab-eating Mongoose Urva urva (16.37 ± 8.20) and Leopard Cat (2.49 ± 0.55) being
the most frequently recorded. The presence of Leopard Cat and Masked Palm Civet
Paguma larvata
(0.36 ± 0.21) reflects habitat heterogeneity.
The dominance of adaptable
species such as civets, macaques and small carnivores suggests that the
mammalian community remains resilient under moderate anthropogenic pressure
(Burton et al. 2015).
Species diversity
The Shannon-Wiener (H′) and
Simpson’s diversity ndices (1–D) along with Hill
numbers were calculated for each protected area (Table 5).
- Trishna
Wildlife Sanctuary (H′ = 1.984; 1–D = 0.8294) exhibited the highest diversity
and lowest dominance (D = 0.1706), indicating a well-balanced species
distribution.
- Sepahijala
Wildlife Sanctuary (H′ = 1.917; 1–D = 0.8194) showed moderate diversity,
reflecting a stable community dominated by primates and civets.
- Gumti
Wildlife Sanctuary (H′ = 1.853; 1–D = 0.7843) displayed lower diversity and
higher dominance (D = 0.2157), suggesting localized concentration of a few
species such as Crab-eating Mongoose and Leopard Cat.
- Conversion of diversity indices
to Hill numbers indicated that Trishna Wildlife
Sanctuary exhibited the highest
effective number of species (¹D = 7.27; ²D = 5.86), followed by Sepahijala Wildlife Sanctuary, and Gumti
Wildlife Sanctuary . The larger difference between ¹D and ²D in GWS suggests
greater dominance by a few species and lower community evenness, whereas TWS
showed comparatively higher evenness and a more balanced species assemblage.
These results collectively
indicate that Trishna supports the most even and
balanced mammalian assemblage, followed by Sepahijala,
while Gumti, though larger, is more ecologically
heterogeneous.
Rarefaction curves
The rarefaction curve (Figure 2)
for GWS shows a steady increase in species richness with increasing sampling
units, with the curve beginning to approach an asymptote near the highest
sampling intensity. This suggests that sampling captured most of the detectable
species in this landscape, though a small number of undetected species may
still remain. The gradually narrowing confidence interval indicates increasing
precision at higher sampling effort. The curve’s shape is consistent with
well-sampled assemblages reported in other camera-trap studies where species
detection stabilizes after sufficient spatial coverage (Kays et al. 2020).
The rarefaction curve (Figure 3)
for SWS rises sharply during the initial sampling units, reaching approximately
8–9 species before showing signs of flattening. Due to the relatively low total
number of sampling units (1–7), this apparent plateau may reflect limited
sampling effort rather than true community saturation. Small sample sizes can
give the illusion of convergence even when many species remain undetected (Gotelli & Colwell 2001). Thus, while the curve suggests
moderate richness, additional sampling is likely necessary for a robust
estimate.
The rarefaction curve (Figure 4)
for TWS reaches the highest richness values among the three groups, with the
upper confidence envelope exceeding 12 species. Unlike GWS, this curve
continues to rise at higher sampling effort, indicating incomplete sampling and
a higher likelihood of undiscovered species. This pattern aligns with studies
showing that species-rich or structurally complex habitats require higher
sampling intensity to capture full community composition. The combination of
high richness and a non-asymptotic curve suggests TWS supports a relatively
diverse mammalian assemblage and would benefit from expanded sampling.
Sample-based rarefaction curves
provide a standardized comparison of species richness across protected areas in
Tripura. GWS appears near-saturated, indicating adequate sampling. SWS shows
moderate richness but requires additional sampling to confirm community
estimates. TWS exhibits the highest richness and incomplete saturation,
highlighting the need for increased sampling to fully document the species
assemblage.
Noteworthy records
Several key species records
obtained during the survey represent important contributions to the faunal
inventory of Tripura:
This study
provides the first camera-trap-based photographic record of the Ferret Badger Melogale sp. from the state, reconfirming its
presence in Tripura within the Indo-Burma biodiversity hotspot (Patil et al. 2025a).
Confirmed
locality records were obtained for the Malayan Porcupine Hystrix
brachyura and the Fishing Cat Prionailurus
viverrinus from western and southern Tripura,
respectively (Patil et al. 2025b). While both species
are widely distributed across northeastern India, these represent the first
systematic photographic records from the state, thereby strengthening their
known occurrence and contributing to finer-scale range documentation.
A small
subpopulation of Gaur Bos gaurus was
documented in TWS, representing the species’ continued presence in the state
despite its absence from current IUCN Red List distribution map (Duckworth et al.
2016).
These photographic confirmations
strengthen the evidence base for Tripura’s mammalian diversity and update
species distributions within the Indo-Burma transition zone.
Despite the smaller area and
higher human population density, Tripura’s protected areas continue to sustain
a diverse mammalian community representative of the Indo-Burma transition zone.
Discussion
Mammalian assemblage and species
composition
The present assessment provides
an updated account of the mammalian community within Tripura’s protected areas
and confirms the persistence of 19 species representing 16 genera, 10 families
and four orders. The assemblage is dominated by small to medium-bodied
carnivores and primates, with ungulates contributing to the herbivore guild, a pattern
typical of fragmented tropical forest systems in the Indo-Burma biodiversity
hotspot, where adaptable species persist under anthropogenic pressure (Datta et al. 2008; Bhatt et al. 2022).
Across sites, TWS exhibited the
highest species diversity and evenness, whereas GWS displayed lower diversity
but harboured several rare and specialized species.
SWS, despite higher human influence, recorded high primate activity. Together,
these patterns indicate that Tripura’s protected areas function as complementary
components of the regional mammalian diversity.
The co-occurrence of multiple
primate species, including Phayre’s Leaf Monkey,
Rhesus Macaque, and Pig-tailed Macaque, further reflects microhabitat
heterogeneity and ecological continuity of arboreal habitats.
New records and biogeographic
implications
Several notable records expand
the known distribution of mammals in Tripura. The Malayan Porcupine recorded in
SWS represents a westward range extension (unpublished), while the Fishing Cat
documented in TWS provides the first photographic confirmation from this region
(Patil et al. 2025b). Both species are habitat
specialists, associated with mixed bamboo-deciduous forests and wetland
ecosystems, respectively, highlighting the importance of heterogeneous habitats
and small wetlands as refugia within modified landscapes. Similarly, the first
photographic evidence of Ferret Badger adjacent to a Jhum cultivation
reconfirms its presence within the region. Its occurrence in such a disturbed
habitat corroborates its known ecological adaptability and highlights the
species’ resilience in a dynamic landscape (Patil et
al. 2025a).
The species assemblage broadly
aligns with patterns reported from Manas National
Park, Dampa Tiger Reserve, and community reserves in
Meghalaya, where generalist species such as Leopard Cat and Common Palm Civet
dominate (Sethy et al. 2021; Bhatt et al. 2022; Lyngdoh et al. 2023). The occurrence of wetland and
forest-edge specialists such as Fishing Cat and Small-clawed Otter underscores
Tripura’s unique position at the intersection of Indo-Gangetic and Indo-Burmese
biogeographic zones. Despite their relatively small size and isolation, these
protected areas continue to function as important refuges and potential
dispersal corridors.
Conservation and management
Implications
The presence of a small, isolated
subpopulation of Gaur in TWS further enhances the site’s conservation
importance. Despite its exclusion from the IUCN distribution map, repeated
photographic and direct observations confirm its persistence (Duckworth et al.
2016). This population likely represents a relict group isolated from the
larger populations in Mizoram and Bangladesh, raising concerns regarding
long-term genetic viability. In contrast, Asiatic Wild Dogs Cuon
alpinus, historically present, appear to be
locally extirpated, likely due to restricted transboundary movement caused by
border fencing. Such barriers limit dispersal of wide-ranging species including
Dholes, Asian Elephants, and Gaur, reflecting the complex interface between
conservation and national security.
The findings of this study
underscore several key implications for conservation management in Tripura:
Habitat
heterogeneity and wetland protection: The detection of Fishing Cat,
Small-clawed Otter and Malayan Porcupine highlights the importance of
conserving diverse habitats, including riparian zones, bamboo thickets and
degraded mixed forests. Management plans should incorporate small wetlands and
community-managed water bodies within buffer areas.
Connectivity
and landscape integration: The isolation of Bos gaurus
and the extirpation of Cuon alpinus highlight the vulnerability of wide-ranging
species requiring large home ranges. Continuous border fencing has likely
restricted transboundary movement, isolating populations within Tripura and
limiting functional connectivity with Bangladesh. Conservation efforts should
therefore prioritize strengthening ecological linkages within the state,
particularly between protected and adjoining non-protected forests, and
maintaining connections with forested landscapes of Mizoram. Enhancing habitat
quality and reducing anthropogenic pressures will be critical to sustain viable
populations in an increasingly fragmented landscape. Under the current
landscape configuration, opportunities for functional transboundary
conservation with Bangladesh are limited, as continuous border fencing
restricts wildlife movement and dispersal. Consequently, populations on either
side are likely to function independently, reducing the effectiveness of
traditional transboundary conservation approaches that rely on ecological
connectivity.
Monitoring
small carnivores and lesser-known species: The first photographic record of Melogale sp. underscores the importance of
long-term monitoring of cryptic species. Expanding camera-trap networks both
temporally and spatially will help capture rare occurrences and establish
reliable population baselines.
Together, these findings
emphasize that Tripura’s small but ecologically varied protected areas remain
critical for regional mammal conservation and warrant sustained management
attention and monitoring, although interpretations are conservative given the
rapid assessment nature of the survey.
Pragmatism in research
Conducting wildlife research in
smaller northeastern states poses logistical, temporal and infrastructural
constraints. The current study adopted a pragmatic research approach,
emphasizing achievable, methodologically sound outcomes within the available time
and resources. Limited camera availability was addressed through a rotating
deployment strategy, while sampling grids were selected based on animal sign
evidence to maximize detection probability and spatial coverage.
These decisions directly
contributed to key outcomes of the study. The optimized camera deployment
enabled coverage across multiple protected areas within a short duration,
resulting in the documentation of 19 species, including rare and previously
unrecorded taxa such as Ferret Badger. The targeted placement of cameras in
areas with animal signs improved detection success, facilitating the recording
of habitat specialists such as Fishing Cat and Malayan Porcupine and allowing
estimation of relative abundance across sites.
Pragmatism in conservation
research prioritizes the generation of actionable data that can directly inform
management decisions even when comprehensive long-term datasets are unavailable
(Sutherland et al. 2004).
In this study, a combination of
structured sampling and flexible field design ensured that results remain both
reliable and management-relevant, particularly for guiding rapid conservation
interventions and strengthening baseline data for future monitoring in Tripura.
Conclusion
This assessment provides the most
recent and one of the first systematic accounts of Tripura’s mammalian fauna to
date. The documentation of new species records, range extensions, and isolated
subpopulations contributes substantially to regional biodiversity knowledge.
Despite limited area and increasing anthropogenic pressures, the protected
areas of Tripura retain ecological significance as functional refuges within
the Indo-Burma biodiversity hotspot. The study reinforces the need for
continuous monitoring, landscape-level habitat management and the integration
of pragmatic research strategies into conservation planning.
Table 1. Details of the
protected areas of Tripura.
|
Protected area |
Size (km2) |
Habitat type |
Elevation range (in m) |
Disturbance level |
|
Sepahijala WS |
18.5 |
Moist mixed deciduous forest |
10–50 |
High |
|
Trishna WS |
194.71 |
Moist mixed deciduous forest |
51–82 |
Moderate to high |
|
Gumti WS |
389.5 |
Tropical semievergreen
forest |
40–300 |
High |
|
Rowa WS |
0.8 |
Moist deciduous forest |
70 |
Moderate to high |
Table 2. Summary of the
camera-trapping effort undertaken in all the protected areas
of Tripura. Note: Rowa Wildlife Sanctuary
was not included
in the analysis due to insufficient
camera-trap data. Records from
camera traps and opportunistic sightings were documented during field surveys.
|
Protected area |
No. of camera-trap stations |
Active trap nights |
Period (2024) |
|
Rowa WS |
4 |
12 |
29 Jan– 04 Feb |
|
Sepahijala WS |
8 |
77 |
10 Feb–18 Feb |
|
Trishna WS |
15 |
112 |
19 Feb–08 Mar |
|
Gumti WS |
29 |
280 |
09 Mar–05 Apr |
|
Total |
56 |
481 (469) |
- |
Table 3. Camera-trap effort, number of species
detected, number of independent captures and total trap nights across protected
areas of Tripura.
|
Protected area |
No. of species detected |
No. of independent captures |
Total trap nights |
|
Sepahijala WS |
9 |
107 |
77 |
|
Trishna WS |
12 |
114 |
112 |
|
Gumti WS |
8 |
71 |
280 |
|
Total |
- |
292 |
469 |
Table 4. Checklist of mammalian
species recorded from the protected
areas of Tripura with relative
abundance index (RAI) values.
|
Common name |
Scientific name |
Order |
Family |
RAI SWS |
RAI TWS |
RAI GWS |
|
Northern Red Muntjac |
Muntiacus vaginalis |
Artiodactyla |
Cervidae |
11.76 ± 6.42 |
1.42 ± 6.42 |
- |
|
Wild Boar |
Sus scrofa |
Artiodactyla |
Suidae |
10.29 ± 5.94 |
- |
- |
|
Gaur |
Bos gaurus |
Artiodactyla |
Bovidae |
- |
4.88 ± 2.82 |
- |
|
Common Palm Civet |
Paradoxurus hermaphroditus |
Carnivora |
Viverridae |
14.71 ± 10.03 |
21.95 ± 10.03 |
2.14 ± 10.03 |
|
Large Indian Civet |
Viverra zibetha |
Carnivora |
Viverridae |
25.00 ± 14.43 |
- |
- |
|
Small Indian Civet |
Viverricula indica |
Carnivora |
Viverridae |
- |
0.81 ± 0.41 |
0.36 ± 0.41 |
|
Masked Palm Civet |
Paguma larvata |
Carnivora |
Viverridae |
- |
- |
0.36 ± 0.21 |
|
Crab-eating Mongoose |
Urva urva |
Carnivora |
Herpestidae |
- |
7.32 ± 8.20 |
16.37 ± 8.20 |
|
Ferret Badger |
Melogale sp. |
Carnivora |
Mustelidae |
- |
- |
1.78 ± 1.03 |
|
Asian Small-clawed Otter |
Aonyx cinerus |
Carnivora |
Mustelidae |
- |
3.25 ± 1.88 |
- |
|
Leopard Cat |
Prionailurus bengalensis |
Carnivora |
Felidae |
1.47 ± 0.55 |
1.63 ± 0.55 |
2.49 ± 0.55 |
|
Fishing Cat |
Prionailurus viverrinus |
Carnivora |
Felidae |
- |
- |
2.68 ± 2.09 |
|
Jungle Cat |
Felis chaus |
Carnivora |
Felidae |
- |
0.81 ± 0.47 |
- |
|
Malayan Porcupine |
Hystrix brachyura |
Rodentia |
Hystricidae |
7.35 ± 4.25 |
- |
- |
|
Phayre’s Leaf Monkey |
Trachypithecus phayrei |
Primates |
Cercopithecidae |
- |
0.81 ± 0.47 |
- |
|
Capped Langur |
Trachypithecus pileatus |
Primates |
Cercopithecidae |
- |
- |
- |
|
Rhesus Macaque |
Macaca mulatta |
Primates |
Cercopithecidae |
69.12 ± 34.74 |
40.65 ± 34.74 |
- |
|
Northern Pig-tailed Macaque |
Macaca leonina |
Primates |
Cercopithecidae |
17.65 ± 8.66 |
8.13 ± 8.66 |
0.36 ± 8.66 |
|
Hoolock Gibbon |
Hoolock hoolock |
Primates |
Hylobatidae |
- |
- |
- |
Table 5. Simpson’s (1–D) and Shannon-Wiener (H′) diversity indices, along with hill numbers
for mammals across protected areas of Tripura. Diversity
indices and corresponding Hill numbers (q = 1
and q = 2) across SWS, TWS,
& GWS representing the effective number of common and dominant
species, respectively.
|
|
SWS |
TWS |
GWS |
|
Total individuals (N) |
196 |
192 |
130 |
|
Simpson’s dominance index (D) |
0.1805861 |
0.1705934 |
0.2157424 |
|
Simpson’s index of diversity
(1-D) |
0.8194139 |
0.8294066 |
0.7842576 |
|
Shannon’s index (H’) |
1.917 |
1.984 |
1.853 |
|
Hill number (¹D=e^{H′}) |
6.80 |
7.27 |
6.38 |
|
Hill number (²D=1/D) |
5.54 |
5.86 |
4.63 |
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