In an era of rapid advances in machine learning and data analysis, wildlife conservation is gaining powerful new tools. For a vast and varied landscape like Tsavo—home to over 17,000 elephants spread across 43,000 km²—being able to predict threats before they emerge could make a real difference. This article explores how AI-driven “predictive patrols,” inspired by frameworks like PoachNet, could revolutionize our anti-poaching work. Thanks to Tsavo Trust’s existing data systems, collaring program, and real-time tracking platform, we may already be poised to lead the way.
What are predictive patrols?
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Terrain ruggedness, water sources, and vegetation
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Historical poaching incidents
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Real-time ranger reports and community alerts
The result is a dynamic risk map. Each grid cell is assigned a poaching probability. Rangers can then be deployed where the risk is highest, maximizing impact and efficiency.
Though Tsavo Trust does not currently run PoachNet, its reported 85 percent hotspot-prediction accuracy shows how AI can turn large, messy datasets into actionable insights.
Why Tsavo Trust is well-prepared
In August 2024, Tsavo Trust, in partnership with Kenya Wildlife Service, WRTI, Save the Elephants and Wildlife Works, fitted 13 GPS collars on elephants (nine males and four females), while retrieving outdated units. These collars offer real-time movement updates that are crucial for conflict mitigation, security planning, and studying habitat connectivity.
All this data, combined with ranger movements and aerial patrols, feeds into Earth Range—our real-time tracking platform. Layering AI-generated risk maps on Earth Range could enable automated alerts whenever nearby patrols align with predicted hotspots. This would allow us to act quickly and strategically.
Benefits of predictive patrols in Tsavo
| Benefit | What it means for Tsavo Trust |
|---|---|
| Precision patrols | Focused deployment saves fuel, reduces ranger fatigue, and strengthens deterrence |
| Faster response | Alerts reduce mobilisation time significantly, helping intercept threats earlier |
| Model improvement | Field feedback, whether from encounters or false alerts, fine-tunes AI patterns based on seasonal or behavioral shifts |
What this could mean for wildlife protection
Introducing AI-driven predictive patrols alongside real-time tracking marks a new chapter in anti-poaching work. By converting detailed data into operational intelligence, rangers can anticipate rather than react. As models and sensors continue to improve, Tsavo Trust could see stronger protection for elephants, more efficient use of resources, and a safer refuge for wildlife across the region.
In a landscape as large and complex as Tsavo, this blend of smart technology and frontline patrols offers a serious path forward. It’s the future of conservation—measurable, adaptive, and focused.