Adopting Artificial Intelligence and Machine Learning for Conservation and Animal Ecology in Protected Areas: A Case of Gorillas and Golden Monkeys in Mgahinga National Park
Wanyera Francis John *
Department of Hospitality and Tourism, East African University, Kigali, Rwanda.
*Author to whom correspondence should be addressed.
Abstract
The golden monkey (Cercopithecus kandti) and mountain gorillas (Gorilla beringei beringei) is listed as endangered on the IUCN Red list. Despite the growing advancement and application of Artificial Intelligence (AI) and Machine Learning (ML) in wildlife monitoring and ecological studies, there remains a significant gap in their effective integration into conservation practices within Mgahinga Gorilla National Park (MGNP). The study aims to examine the potential application of AI and ML in improving data collection, monitoring, and conservation of endangered mountain gorillas and golden monkeys in MGNP, with a focus on assessing current monitoring methods, identifying existing challenges, and exploring how AI and ML technologies can enhance wildlife ecology and conservation efforts in the park. A descriptive research design was adopted, integrating both qualitative and quantitative approaches. Primary data were collected through field observations and structured interviews with park staff, including rangers, guides, trackers, veterinary personnel, and law enforcement teams. Secondary data were obtained from park records, satellite imagery, and existing literature. The study population consisted of 40 staff members, from which a sample of 28 respondents was selected using Slovin’s formula. Purposive and simple random sampling techniques were applied to select participants, with the individual serving as the sampling unit. Data were analysed using SPSS to generate descriptive statistics. The study therefore concluded that the integration of AI and ML-based models is necessary to overcome current limitations by improving data processing, enhancing real-time monitoring, and supporting more accurate and timely conservation decisions. Overall, the study highlights the urgent need for adopting intelligent technological systems to strengthen wildlife monitoring, improve efficiency, and ensure the long-term conservation of endangered species and their habitats in MGNP. The study further recommends that the park invest in modern and updated equipment and tools, including advanced sensors, surveillance systems, and digital monitoring technologies.
Keywords: Artificial intelligence, machine learning, conservation, animal ecology