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The main objective is to investigate the potential and applicability of emerging technologies for intelligent monitoring of wildlife as a strategy to mitigate conflicts between wildlife and human communities.
The specific objectives include mapping and characterizing the main conflicts between local communities and wildlife, evaluating the available emerging technologies, and developing an integrated model for intelligent wildlife monitoring adapted to the ecological and socioeconomic realities of the park.
The research will employ qualitative and exploratory methods, supported by quantitative data such as species counts and incident reports. It will involve literature review, field diagnosis, technological assessment, technical proposal development, participatory validation, and data analysis.
The study evaluates technologies such as camera traps, drones, GPS collars, and remote sensing software, along with artificial intelligence tools to enhance wildlife monitoring.
Community acceptance is crucial because it directly influences the success of mitigation strategies. Participatory approaches that consider local contexts can lead to more effective conservation outcomes.
The expected outcomes include improved data collection efficiency, better decision-making for wildlife management, and enhanced coexistence between local communities and wildlife.
Challenges include habitat fragmentation, climate change, and competition for natural resources, which pose significant threats to wildlife conservation and affect communities living near protected areas.
The literature review is significant as it gathers scientific publications, technical reports, and legislation related to human-wildlife conflicts and conservation technologies, providing a foundation for the research.
Field diagnosis will involve semi-structured interviews with local residents, community leaders, and park managers, as well as reviewing records of wildlife incidents to gather qualitative insights.
Participatory validation involves engaging with managers and community representatives to assess the social and ecological applicability of the proposed monitoring system, ensuring it meets local needs.
The research activities are scheduled over several semesters, including literature review, field workshops, data analysis, and final writing and publication of results.
Integrating emerging technologies is important as it allows for real-time monitoring of wildlife, tracking of species, and adaptive management of protected areas, enhancing conservation efforts.
Drones can provide aerial surveillance, collect data over large areas quickly, and access hard-to-reach locations, improving the efficiency and effectiveness of wildlife monitoring.
The research aims to strengthen local environmental governance by utilizing technologies to improve wildlife monitoring and management, aligning with Mozambique's commitments to biodiversity conservation.
The Parque Nacional de Mágoè serves as a unique case study for integrating multiple emerging technologies in wildlife monitoring, particularly in a context with high socio-environmental tensions.
The research addresses socio-economic needs by proposing a monitoring model that considers local realities, aiming to reduce conflicts and promote coexistence between communities and wildlife.
Artificial intelligence can enhance data analysis and decision-making processes in wildlife monitoring, allowing for more accurate predictions and responses to wildlife behavior and conflicts.
Challenges may include technological limitations, the need for community training and engagement, and ensuring the system is adaptable to the ecological and logistical conditions of the park.
The CBD is relevant as it provides a framework for biodiversity conservation efforts, guiding the research towards aligning with global commitments and enhancing local governance in conservation.
The research will analyze qualitative data from interviews and community feedback, as well as quantitative data on species counts, incidents, and technological assessments using descriptive statistics and geospatial mapping.
The participatory approach is significant as it fosters collaboration between researchers and local communities, ensuring that the monitoring system is culturally relevant and socially accepted.