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.