Identification of wildlife crime hotspots in Punjab, India via kernel density estimation analysis
Format of work:
Journal Article
Event presented at / Journal Name:
Journal of Threatened Taxa
Speaker / Contact Author's Name:
Navdeep Sood
Speaker / Contact Author's E-mail Address:
ersood@yahoo.com
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Research aim:
This study aimed to identify and quantify spatial patterns of wildlife crime in Punjab, India, an underrepresented and predominantly agricultural region. Specifically, it sought to compile documented wildlife crime incidents and apply geospatial analysis to detect hotspots of illegal activity. By establishing a spatial baseline, the study aimed to support improved monitoring, enforcement prioritisation, and understanding of how wildlife crime operates in human-dominated landscapes.
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Background:
Wildlife crime is a major global threat to biodiversity and is considered one of the largest transnational illicit trades. However, reliable data are often limited, particularly in regions with low reporting capacity. Punjab, India, has historically been underrepresented in wildlife crime research, despite increasing evidence of illegal hunting and trade. Media-based datasets have been increasingly used to document such activity, especially for overlooked species and regions. This study addresses a key knowledge gap by providing the first systematic spatial assessment of wildlife crime in a low-forest, human-dominated landscape.
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Approach:
The authors compiled wildlife crime incidents reported between 2019 and 2024 using multilingual media sources, alongside official enforcement and organisational records. A total of 32 verified incidents were georeferenced and analysed using Kernel Density Estimation (KDE) within a GIS framework to map spatial clustering. Incidents were categorised by species, offence type, and method, and spatial intensity was classified into five hotspot categories. This approach enabled identification of priority areas and provided a quantitative assessment of the distribution and concentration of wildlife crime across the state.
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Key finding:
Wildlife crime in Punjab is highly clustered rather than evenly distributed. Extreme-intensity hotspots covered approximately 1% of the state, while moderate-to-high intensity areas spanned nearly one-third of the landscape. Incidents affected a wide range of taxa, including protected and non-charismatic species, and involved both opportunistic hunting and organised trafficking. Evidence of long-distance and transnational trade was identified, including marine species and wildlife derivatives. Despite the relatively small dataset, thousands of animals were impacted, suggesting that reported incidents represent only a fraction of total activity.
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Industry or policy relevance:
The study highlights the need for targeted, spatially informed enforcement strategies in wildlife crime prevention. By identifying clear geographic hotspots, it provides actionable evidence for allocating limited conservation and enforcement resources more effectively. Beyond Punjab, the findings are relevant to other human-dominated and under-studied regions globally, where wildlife crime may be underestimated. The study also emphasises the importance of integrating animal welfare considerations into wildlife crime policy, as these activities represent significant and often overlooked sources of animal suffering alongside conservation impacts.
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Route for practical application:
The hotspot maps generated in this study can be used by enforcement agencies to prioritise surveillance, patrols, and intervention efforts in high-risk areas. Conservation organisations can use these data to focus monitoring and community engagement initiatives. The methodology—combining media reports with spatial analysis—offers a scalable, low-cost approach that can be applied in other regions lacking systematic data. Integrating such tools into routine monitoring frameworks could improve detection of emerging trends and support more proactive, evidence-based responses to wildlife crime.
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Confidence in findings and next steps towards realising impact:
This study provides an important and novel spatial baseline for wildlife crime in an underrepresented region, supported by a transparent and replicable methodology combining media data with geospatial analysis. The identification of clear clustering patterns and hotspots adds confidence that wildlife crime is spatially structured and not randomly distributed. The consistency of patterns across taxa and offence types further supports the robustness of the findings. A number of limitations should be considered. The dataset is based on reported incidents and is therefore subject to reporting and detection bias, with likely underrepresentation of less visible or less charismatic species. The relatively small sample size (32 incidents) and reliance on secondary data limit inference about true prevalence and causal drivers. Spatial analysis was restricted to incidents with sufficient location data, and findings may not capture temporal variation or unreported activity. Future work should incorporate field-based validation, longitudinal data collection, and integration with enforcement and ecological datasets to better understand drivers of wildlife crime. Expanding this approach across regions will help assess generalisability and support the development of coordinated, evidence-based strategies for both conservation and animal welfare outcomes.
Funders:
No external funding.
Links to Open Access Publications or DOI:
Citation:
Sood, N. & Kumar, R. (2026). Identification of wildlife crime hotspots in Punjab, India. Journal of Threatened Taxa, 18(3): 28524–28533.
