Journal of Threatened
Taxa | www.threatenedtaxa.org | 26 March 2026 | 18(3): 28524–28533
ISSN 0974-7907 (Online) | ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.9495.18.3.28524-28533
#9495 | Received 17 November 2024 | Final received 20 February 2026|
Finally accepted 05 March 2026
Identification of wildlife crime
hotspots in Punjab, India via kernel density estimation analysis
Navdeep Sood 1 & Rohan Kumar 2
1 Bharat Sanchar Nigam Limited (A
Government of India Enterprise), Core Network Tx-North, Tarn Taran, Punjab 143401, India.
1,2 School of Chemical Engineering
and Physical Sciences, Lovely Professional University, Phagwara, Punjab 144411,
India.
1 ndsood@gmail.com (corresponding
author), 2 rohan.25322@lpu.co.in
Abstract: Punjab is a predominantly
agrarian state and among the least forested in India. It remains
underrepresented in w ildlife crime research. This study documents
thirty-two wildlife crime incidents affecting thousands of wild animals
compiled from media sources and official enforcement and organisational
records between 2019 and 2024. Several of the affected species are listed under
Schedule I of the Wildlife (Protection) Act, 1972 (amended in 2022). Recorded
crimes involved leopards, tigers, sambars, wild boar, Tibetan antelopes,
freshwater turtles, and marine species. Exploitation methods included the use
of firearms, trained dogs, snares, illegal trade, and smuggling of wildlife
derivatives such as Shahtoosh shawls, corals, and
lizard oil. Kernel Density Estimation analysis identified extreme-intensity
hotspots (Class 5) covering approximately 509 km² (~1.0% of the state’s
geographical area), while areas classified under Classes 2–5 collectively
covered approximately 30% of the state area.
Keywords: Crime spatial analysis,
derivatives trade, exploitation, illegal hunting, illegal wildlife trade,
illicit supply chains, landscape metrics, spatial analysis, smuggling
routes, transnational organised crime, wildlife
trafficking, wildlife seizures.
Editor: Vikram
Aditya, Centre for Wildlife Studies, Bengaluru, India. Date of publication: 26 March 2026 (online & print)
Citation: Sood, N. & R. Kumar (2026). Identification
of wildlife crime hotspots in Punjab, India. Journal of Threatened Taxa 18(3): 28524–28533. https://doi.org/10.11609/jott.9495.18.3.28524-28533
Copyright: © Sood & Kumar 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: This research received no external funding.
Competing interests: The authors declare no competing interests.
Author details: Navdeep Sood is a wildlife ecologist with field experience focusing on human–wildlife interactions, urban wildlife, community interfaces, riparian ecosystems and GIS. His work highlights wildlife patterns near riparian reserves in agrarian systems, particularly along the Beas Conservation Reserve, and contributes to understanding underrepresented conservation challenges in Punjab. He is professionally associated with Bharat Sanchar Nigam Limited (BSNL) and contributes to wildlife research alongside his professional role. Dr.Rohan Kumar holds a PhD from the Indian Institute of Technology (IIT) Roorkee and is an Associate Professor in the Department of Space Research, School of Chemical Engineering and Physical Sciences, Lovely Professional University, Punjab.
His research focuses on landslide susceptibility assessment, spatial analysis, remote sensing, Space Research and interdisciplinary applications.
Author contributions: NS: Conceptualization, data collection, analysis, interpretation of results and manuscript preparation. RK: Technical and methodological support and manuscript enhancement.
Introduction
Wildlife
crime involves a diverse array of actors, species, and commodities driven by
multiple factors; its impacts vary across environmental, social, economic, and
governance dimensions (United Nations Office on Drugs and Crime 2024). This is
a fast-growing industry (Gore et al. 2019; Hughes 2021) operated covertly by
the offenders using corrupted channels (Milner-Gulland & Leader-Williams
2019; ’t Sas-Rolfes et al. 2019), therefore, reliable
information on species involved is difficult to obtain. Wildlife exploitation
affects thousands of species across terrestrial and marine taxa (Milner-Gulland
2018; Fukushima et al. 2020), and illegal wildlife trade is described as the fourth largest
transnational illicit trade after narcotics, arms and human trafficking (Warchol 2003; Zimmerman 2003; South & Wyatt 2011; ’t Sas-Rolfes et al. 2019). It poses a significant threat to
biodiversity (Rivalan et al. 2007; Veríssimo & Wan 2019; Hinsley
et al. 2023) and is considered one of the world’s most profitable illicit trade
sectors by the International Criminal Police Organization (INTERPOL) (Masterson
2023).
Researchers
frequently rely on indirect evidence, such as media-reported incidents or
seizure records, to infer patterns of illegal hunting, trade, and trafficking
(Rosen & Smith 2010; Athreya et al. 2015). This
evidence can have biases, making it hard to discern trends across countries
with varying reporting capacities (Underwood et al. 2013). Less charismatic
species are often underrepresented, while species of high public interest
dominate reports (Chawla et al. 2020). In India, substantial illegal trade
involves common or widely distributed species harvested for wild meat,
traditional medicine, religious rituals and the exotic-pet market, yet these
species receive comparatively little scientific attention (Rana & Kumar
2023). Media-based studies, therefore, provide a valuable tool for documenting
overlooked wildlife-crime patterns, as demonstrated for jackals (Chawla et al. 2020),
leopards (Athreya et al. 2015), and other carnivores
in human-modified landscapes (Akash et al. 2025).
Velho et
al. (2012) conducted a comprehensive review in India and reported an absence of
documented hunting, poaching, bushmeat, or wildlife trade crimes in Punjab at
that time, highlighting a critical data gap. Chawla et al. (2020) later
documented a single wildlife crime incident in Punjab involving jackals in
2018. A decadal shift, however, reveals the emergence of multiple wildlife
crime records, indicating a substantial increase in both occurrence and
reporting. The present study, which compiles data from 2019–2024, shows that
even a relatively modest dataset of 32 reported incidents corresponds to
several thousands of wild animals being affected. These findings underscore the
tip-of-the-iceberg nature of the documented cases (United Nations Office on
Drugs and Crime 2016), as many incidents likely remain unreported, reflecting a
far more extensive and complex reality of wildlife crime.
Following
established media-report based research (Athreya et
al. 2015; Chawla et al. 2020; Akash et al. 2025), this study compiles reported
wildlife-crime incidents from 2019–2024 and uses kernel density estimation
(KDE) to quantify crime areas of the state, providing the first systematic
overview for Punjab. Globally, geospatial analysis and KDE have been widely
applied in wildlife research (Hart & Zandbergen
2014; Fleming et al. 2015; Chamling & Bera 2020; Gore et al. 2022; Graves et al. 2022; Sood et al. 2025) and were employed in the present study to
identify spatial patterns of hotspot mapping and quantification of areas most
affected by illegal wildlife activities.
Study Area
The study
area (Figure 1) is an agrarian state located in Punjab, northwestern India. It
has a forest cover of < 3.6% of its geographical area, of which 0.02% is
very dense forest (Forest Survey of India 2023). The state’s fertile plains are
connected to the biodiverse Shivalik Range. The
western boundary of Punjab is constrained by a fully fenced international
border with Pakistan which restricts wildlife movement. There are several Ramsar-designated wetlands that serve as critical wintering
and staging grounds for migratory waterbirds and as
important habitats for resident waterbird assemblages
(Delany et al. 2006). Negligible forest cover, proximity to the hills in the
north and the east, presence of wetlands and rivers flowing from the Shivaliks, fertile plains, major urban centres
such as Jalandhar, Chandigarh, Ludhiana, and Amritsar scattered across central
plains and the fenced border in the west create an environment conducive to the
urban wildlife and scope for intense human-wildlife interactions (Sood et al. 2025). The state remains poorly represented in
scientific literature on wildlife crime, hence media reports provide an
essential information source for documenting such incidents.
Materials and Methods
This study
applied a systematic, multilingual media-reports search methodology adapted
from Athreya et al. (2015), Chawla et al. (2020) and
Akash et al. (2025) to document wildlife-crime incidents in Punjab between
March 2019 and July 2024. This period was selected because reliable,
continuous, and verifiable wildlife-crime records from Punjab became
consistently available from March 2019 onwards, enabling the compilation of a
complete dataset without temporal gaps. Data were compiled from authenticated
English, Hindi, and Punjabi media reports sourced from major newspapers with
robust digital archives (Supplementary Table S1) supplemented by incidents and
wildlife derivative seizure records from Punjab extracted from government,
reputed non-government organisation and enforcement
websites including the Wildlife Crime Control Bureau (WCCB), TRAFFIC India and
the Wildlife Trust of India (WTI).
During
media data collection, a mixed Boolean OR–AND search strategy was employed,
wherein each report was required to contain at least one from the six keywords
‘wildlife’, ‘crime’, ‘killed’, ‘poaching’, ‘smuggling’, ‘bushmeat’, along with
the word ‘Punjab’. To reduce omission of species not explicitly described under
general offence-related terms (e.g., birds, reptiles, turtles), additional
searches were conducted using species-specific terms identified during the
initial screening process. Additionally, all retrieved reports and records were
constrained to fall within the predefined temporal window. Online searches were
conducted using Google Search with same keyword combinations in English, Hindi,
and Gurmukhi (Punjabi) and conducted in incognito mode to minimize algorithmic
personalization bias. Government and organisational
sources were used for cross-verification of incidents and confirmation of
seizure details and were not treated as independent primary records when
corresponding media reports existed. Duplicate entries were consolidated based
on matching date, species and locality identifiers to avoid double counting.
Most Punjabi-language results were derivative of corresponding English or Hindi
reports and did not provide additional primary information on wildlife crime.
Only a single relevant, non-duplicated report was identified from Punjabi
digital media (News18 Punjab). Data were compiled exclusively for all available
wild animal species, with domesticated taxa expressly excluded from the
dataset.
A total of
149 data records were retrieved which underwent a rigorous, multi-stage
workflow consisting of relevance screening, duplicate consolidation and
verification of species identity, locality, offence type and enforcement
actions and n = 32 records were selected that fell into specified criteria.
Only incidents specifying the location of the crime were retained for spatial
analysis. Each validated incident area was georeferenced using latitude and
longitude coordinates using Google maps, generating KML/KMZ files to map the
points. The spatial data were then projected to UTM Zone 43N with all necessary
conversions applied to ensure accurate area calculations.
Each
incident was coded using an event-based framework derived from the above
studies, species taxonomy, offence typology (poaching, illegal trade,
trafficking, possession, conflict-driven killing), modus operandi, seizure
characteristics and enforcement responses. Each incident was georeferenced and
spatially mapped by assigning it to the smallest clearly identifiable
administrative unit reported in the news or record source.
To create
spatial map and identify hotspots of wildlife crime, QGIS 3.40, a free and
open-source Geographic Information System (GIS) software widely used for
spatial analysis and mapping was used. Kernel Density Estimation (KDE) was
applied to the georeferenced area points. KDE generates a continuous smooth
density surface from discrete point locations, providing a realistic
representation of spatial crime concentration patterns (Hart & Zandbergen 2014; Hu et al. 2018) unlike simple point-count
methods.
A uniform
30 × 30 m raster grid was generated by clipping to the Punjab administrative
boundary to create the analytical background surface. A quadratic kernel
function was applied to the incident layer to produce a continuous density
raster, which was subsequently classified into five intensity categories using
the Natural Breaks (Jenks) algorithm for spatial prioritization. Bandwidth was
determined using Silverman’s rule of thumb as implemented in QGIS (Silverman
1986).
The
cumulative distribution function (CDF), which quantifies the cumulative
proportion of KDE (Chen 2017), was calculated as the cumulative proportion of
raster cells relative to the total number of cells in the study area, enabling
quantitative assessment of spatial concentration of wildlife crime. All
calculations and CDF visualisations were completed in
Microsoft Excel 365.
This
integrated and replicable methodology combines media reported incidents,
georeferencing, and spatial analysis to generate the first systematic spatial
representation of reported wildlife crime incidents in Punjab.
Results
A dataset
compiled from various sources is presented in Supplementary Table S2, which
forms the basis for the analyses presented in this study. Cumulative seizure
quantities suggest that the number of wild animals impacted runs into several
thousands, highlighting the substantial scale of wildlife crime in the region.
Kernel
Density Estimation of wildlife crime incidents in Punjab
Wildlife
crime incidents were mapped using KDE (Figure 2). The KDE output was
subsequently classified into five intensity classes to quantify the proportion
of area affected under wildlife crime.
The
analysis based on raster cells and area coverage (Table 1) revealed that Class
1, depicted as background blue layer, covers 69.3% of the state area and
represents no reported incidents across the majority of the state. Class 2,
represents crime intensity between low-to-moderate, covers 19.7% of the state
area, indicating small concentrations of illegal activity. Class 3,
intermediate intensity, covers 7.1% of the total area. Class 4 shows elevated
intensity in 2.9% of the state, forming a high-risk zone. Class
5, represents extreme intensity or the core hotspot and includes 508.9
km² (1.0% of the total state area). Class 5 highlights areas of concentrated
illegal wildlife activity.
The cumulative
distribution function (CDF) (Figure 3) from KDE illustrates that a small
fraction of cells (Classes 3–5, ~11.0% of total) accounts for the highest
intensity zones, demonstrating that wildlife-crime is not uniformly distributed
but highly clustered with pronounced hotspots. The KDE map shows these near the
Shivalik foothills and within the districts of
Amritsar, Jalandhar, Rupnagar, Hoshiarpur, Ludhiana, Pathankot, SAS Nagar, and Fazilka. These results emphasize a multi-scalar hierarchy
of risk, from extensive low-intensity backgrounds to compact but critically
significant extreme hotspots.
Discussion
This study
presents a spatial analysis of reported wildlife crime incidents in Punjab
between 2019 and 2024. Velho et al. (2012) reported an absence of wildlife
crime data from Punjab, and Chawla et al. (2020) later identified only a single
incident. The present analysis documents thirty-two incidents affecting
multiple taxa, indicating that wildlife crime in Punjab is more diverse and
spatially structured than previously recognized. Use of firearms, clutch-wire
snares, trained hunting dogs, nets, and vehicles suggests a combination of
opportunistic hunting and organized trafficking operations. The seizures of
marine derivatives and tiger cubs supports the existence of structured supply
chains.
KDE
indicated that incidents were not evenly spread across Punjab. The quantified
areas in the highest intensity class cover about 1% of the state (~509 km²).
Classes of moderate to high intensity together account for nearly one-third of
the total area. Higher densities were observed near the Shivalik
foothills and within few districts only. These patterns suggest that ecological
edges and transport connectivity may be associated with the observed
clustering. The findings create a measurable spatial concentration of reported
offences in a low-forest agrarian state. Although reporting bias cannot be
excluded, the presence of high-intensity clusters suggests persistent localized
activity.
Species
Affected
Incidents
involving leopards Panthera pardus included gunshot fatalities, limb mutilation
(claw removal), snare capture and the killing of a 6–8 month
old cub displaying gunshot wounds and bite marks with pursuit by hunting
dogs. Two incidents involved the recovery of tiger Panthera
tigris derivatives (skin and skeleton), and one
documented a trafficked tiger cub in Punjab, a tiger’s non-range state (Image
1). Tiger is classified as ‘Endangered’ (EN) under the IUCN Red List of
Threatened Species (IUCN) and is listed in Convention on International Trade in
Endangered Species of Wild Fauna and Flora (CITES) Appendix I, which prohibits
trade internationally. Firearms, trained dogs and metal snares correspond with
established methods used against large carnivores in India and globally
(Zielinski et al. 2006; Becker et al. 2013).
Ungulates
such as Sambar Rusa unicolor featured
repeatedly, including cases of firearm injury, limb removal, antler
possession/trade and raw meat seizure. Barking Deer Muntiacus
muntjak was also affected. These patterns mirror
ungulate poaching elsewhere in India (Rana & Kumar 2023).
Wild Boar Sus scrofa was the
most frequently recorded species. Incidents involved mass live capture,
transport, meat extraction and mortality during illicit movement (e.g., 127
individuals: 32 dead, 95 rescued) possibly for meat (Ingram et al. 2021).
Studies show that wild meat is exchanged through complex commercial networks
involving multiple stages in the supply chain (Bennett et al. 2007), the trade
of wild meat contributes significantly to species extinction (Ripple et al.
2016).
Seizures
included marine items such as 69 gorgonian sea fans, 1.4 kg organ pipe coral,
and 4.8 kg coral fragments, as well as 38 containers of bear bile, 137 ‘Hatha
Jodi’ items, Indian Spiny-tailed Lizard Saara
hardwickii oil, and exotic parrots (Macaws).
Hatha Jodi refers to the dried hemipenes of Indian
monitor lizards (Bhattacharya & Koch 2018), specifically the Bengal Monitor
Varanus bengalensis
and Yellow Monitor Varanus flavescens. Under Wildlife (Protection) Act, 1972
(amended in 2022) and international regulations (CITES Appendices 2017), the
penalties for killing these lizards or trading their body parts are comparable
to those for tiger (D’Cruze et al. 2018). The seizure
of marine taxa from inland Punjab is concerning, indicating long-distance
trafficking networks and highly organised supply
chains.
Seizure of
210 Shahtoosh shawls implied the killing of over
several hundred of Tibetan Antelopes. Shahtoosh trade
is a derivative form of wildlife trade in which Tibetan Antelope Pantholops hodgsonii,
locally called Chiru, are killed for their fine underfur, known as shahtoosh (Mallon & Jiang 2009). An estimated four
Tibetan Antelopes are killed for every shahtoosh
scarf, so 210 shawls represent roughly 840 animals (Gibbens
2019).
Parrots,
peacocks, pheasants, raptors, and freshwater turtles were affected through
poaching, illegal possession and trade. Birds are illegally traded all over the
world (Matias et al. 2012; Alves et al. 2013; Rodewald
et al. 2024) and studies make alarming claims that trade networks focused in
Southeast Asia harvested nine million turtles (Miller et al. 2019)
Crime
Methods and Trafficking Patterns
Crimes
included firearms, shotguns, trained dogs, nets, clutch-wire snares, metal
traps, daggers and transport vehicles indicate organised
poaching networks. Clutch-wire snares used as efficient killing devices (Haq et al. 2023) were repeatedly recorded, reflecting
opportunistic and targeted poaching. Several incidents involved transportation
of wildlife or their derivatives. Tiger derivatives were transported by Punjab
residents to southern India, wild boar were trafficked across state borders and
marine wildlife derivatives were seized in Amritsar, a known transit hub for
illegal wildlife trade
(Wildlife Trust of India 2024). Shahtoosh
shawls were moved through Amritsar and Pathankot. Attari,
near Amritsar, Punjab, serves as a key land route for the illegal smuggling of
wildlife products internationally (Pragatheesh et al.
2022).
Spatial
Distribution of Incidents
Wildlife-crime
incidents were concentrated in Shivalik-adjacent
areas and within districts of Amritsar, Jalandhar, Rupnagar, Hoshiarpur,
Ludhiana, Pathankot, SAS Nagar, and Tarn Taran.
However, further field-based validation would be required to confirm causal
drivers.
Limitations
and Future Directions
This
analysis is based on media reports and organizational records and is therefore
subject to reporting and detection bias. Charismatic species may have received
disproportionate media attention. The dataset reflects only reported incidents
rather than true prevalence. Spatial analysis was limited to georeferenced
cases only. Therefore, the results should be interpreted as a spatial baseline
of reported wildlife crime rather than a comprehensive estimate of total
occurrence.
Conclusion
This study
presents the first geospatial assessment of reported wildlife crime incidents
in Punjab between 2019 and 2024. Despite being predominantly agrarian with
limited forest cover, the state exhibits measurable spatial clustering of
wildlife crime, with extreme-intensity hotspots occupying approximately 1% of
the geographical area. While the dataset likely represents only a subset of
total occurrences, the findings establish a quantitative spatial baseline that
may inform targeted monitoring, enforcement prioritization, and future
research.
Table 1. Spatial
distribution and priority ranking of kernel density estimation classes for wildlife-crime
hotspots in Punjab.
|
KDE Class |
Hotspot Priority |
Area (km²) |
% of state area |
Raster cell count |
|
5 |
Priority 1 (Extreme hotspot) |
508.9 |
1.0% |
565,488 |
|
4 |
Priority 2 (High intensity) |
1,465.8 |
2.9% |
1,628,640 |
|
3 |
Priority 3 (Moderate intensity) |
3,575.1 |
7.1% |
3,972,372 |
|
2 |
Priority 4 (Low–moderate
intensity) |
9,908.4 |
19.7% |
11,009,378 |
|
1 |
Priority 5 (Background / no
incidents) |
34,878.2 |
69.3% |
38,753,577 |
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