Research Area | Substantial Gaps and Challenges | Research Priorities |
Data Quality and Integration | - Incomplete or inconsistent data from various sources. - Lack of standardized data formats. | - Develop methods for data harmonization and integration. - Explore AI-driven data cleaning and quality control techniques. |
Species Identification | - Limited AI models for accurate species identification. - Variability in species behavior and appearance. | - Train AI models for species identification in varying conditions. - Improve AI algorithms for species tracking. |
Illegal Activity Detection | - Limited real-time monitoring capabilities. - Lack of data on illegal fishing activities. | - Develop AI-driven systems for real-time detection of illegal fishing. - Enhance data collection and sharing mechanisms. |
Ecological Modeling | - Complexity in modeling marine ecosystems. - Lack of data for comprehensive models. | - Develop AI-based ecological models for MPAs. - Explore methods for data augmentation and synthesis. |
Ethical and Legal Frameworks | - Unclear legal boundaries for AI-based enforcement. - Ethical concerns related to privacy and fairness. | - Establish clear legal frameworks for AI-driven monitoring and enforcement. - Address ethical considerations through guidelines. |
Human-AI Interaction | - Limited user-friendly interfaces for non-technical users. - Insufficient training for MPA staff on AI tools. | - Design intuitive interfaces for MPA managers and staff. - Provide training and support for AI tool adoption. |
Long-term Monitoring | - Sustainability of AI-based systems over time. - Changing environmental conditions and threats. | - Investigate the resilience of AI systems in dynamic marine environments. - Adapt AI tools for evolving conservation needs. |