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Combining Artificial Intelligence and Local Ecological Knowledge to Document the Largest Ever-Counted Cape Buffalo
Emily Bennitt1, Blair R Costelloe2,3,4, Ben Koger5,6
1Okavango Research Institute University of Botswana Maun Botswana.
Ecology and Evolution
|June 17, 2026
Summary
Large wild mammal aggregations are declining globally. This study documented a record-breaking mega-herd of 4,128 Cape buffalo using wildlife footage and local knowledge, highlighting the value of citizen science for conservation.
Area of Science:
- Ecology and conservation biology
- Wildlife population dynamics
- Citizen science and technology integration
Background:
- Large wild mammal aggregations are declining globally, often before scientific documentation.
- Non-academic wildlife video recordings are increasing, offering opportunistic data collection opportunities.
- Understanding ecological value of large aggregations is crucial for conservation.
Purpose of the Study:
- To document and quantify a rare, large aggregation of Cape buffalo.
- To explore the utility of non-professionally collected wildlife footage for ecological research.
- To integrate local ecological knowledge with technological methods for comprehensive data.
Main Methods:
- Utilized video footage from a wildlife film producer in Mababe Depression, Botswana.
- Applied automated detection and tracking algorithms to count buffalo in the footage.
- Incorporated local ecological knowledge from stakeholders for contextual understanding.
Main Results:
- Documented a minimum verified herd size of 3,676 Cape buffalo.
- Extrapolated an estimated total herd size of approximately 4,128 individuals.
- Identified this aggregation as the largest documented Cape buffalo herd, termed a 'mega-herd'.
Conclusions:
- The study successfully documented a mega-herd, highlighting the value of opportunistic data and interdisciplinary collaboration.
- Recommends further research into the ecological drivers and consequences of forming such large aggregations.
- Demonstrates the potential of combining citizen science, technology, and local knowledge for conservation efforts.
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