The Impact of Artificial Intelligence in Wildlife Monitoring in Protected Areas: A Case Study of Yankari Game Reserve, Bauchi State
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1.1 Background of the Study
Wildlife monitoring is essential for conserving biodiversity and maintaining ecological balance, particularly in protected areas. Yankari Game Reserve in Bauchi State, one of Nigeria’s most prominent wildlife sanctuaries, faces challenges such as poaching, habitat degradation, and human-wildlife conflict. Artificial Intelligence (AI) has become an innovative tool in addressing these challenges by enabling advanced monitoring, predictive analytics, and real-time decision-making.
AI-driven technologies such as camera traps, drones equipped with machine learning capabilities, and acoustic sensors have transformed wildlife monitoring. These tools can identify species, track animal movements, and detect illegal activities, providing park authorities with actionable insights to protect endangered species and preserve habitats (Ahmed & Ibrahim, 2024). This study investigates the impact of AI in wildlife monitoring at Yankari Game Reserve, highlighting its potential to enhance conservation efforts.
1.2 Statement of the Problem
Yankari Game Reserve faces significant threats to its biodiversity due to limited resources for monitoring and controlling illegal activities. Traditional wildlife monitoring methods are labor-intensive and often ineffective in addressing these challenges. AI technologies offer advanced solutions for monitoring and conservation, yet their application in Yankari remains minimal. This study explores the role of AI in addressing these challenges and improving wildlife monitoring efforts.
1.3 Objectives of the Study
- To assess the impact of AI technologies on wildlife monitoring at Yankari Game Reserve.
- To evaluate the effectiveness of AI-driven tools in detecting and preventing poaching activities.
- To identify challenges in implementing AI technologies in wildlife conservation.
1.4 Research Questions
- How does AI enhance wildlife monitoring at Yankari Game Reserve?
- What is the effectiveness of AI-driven tools in preventing poaching and illegal activities?
- What challenges affect the adoption of AI technologies in wildlife conservation?
1.5 Research Hypothesis
- AI significantly enhances wildlife monitoring and conservation at Yankari Game Reserve.
- AI-driven tools are effective in detecting and preventing poaching activities.
- Limited funding and technical expertise hinder the implementation of AI in wildlife conservation.
1.6 Significance of the Study
The study highlights the transformative role of AI in addressing wildlife conservation challenges. Its findings provide insights for policymakers, conservationists, and technology developers seeking to protect biodiversity in protected areas.
1.7 Scope and Limitations of the Study
The study focuses on the application of AI in wildlife monitoring at Yankari Game Reserve. It does not cover other protected areas or explore non-AI-based conservation strategies. Limitations include data availability and the nascent application of AI in wildlife conservation in Nigeria.
1.8 Operational Definition of Terms
- Wildlife Monitoring: The process of tracking and observing animal populations and their habitats.
- Artificial Intelligence (AI): The use of machine learning and data analysis to perform tasks requiring human intelligence.
- Camera Traps: Remote cameras used to capture images of wildlife.
- Acoustic Sensors: Devices that detect sounds to monitor animal behavior or human activities.
- Biodiversity Conservation: Efforts to protect and preserve the variety of life in a given ecosystem.
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CHAPTER ONE
INTRODUCTION
1.1
