The Global Deep Sea Exploration Goals: A representative approach to visually observing the deep seafloor

Area of Science:

• Marine Biology
• Oceanography
• Geology

Background:

• Deep-sea exploration faces historical observational biases.
• Current deep-sea datasets lack geographical and environmental representation.
• Coordinated global sampling is crucial for understanding deep-ocean ecosystems.

Purpose of the Study:

• To present a spatially balanced, probability-based global sampling design for deep-sea visual observation.
• To identify 10,000 target locations for deep-sea exploration (≥200 m depth).
• To correct for historical biases in deep-sea data collection.

Main Methods:

• Integrated four seafloor characteristics: bathymetry, geomorphology, sediment composition, and particulate organic carbon flux.
• Accounted for historical deep-submergence deployments.
• Employed a probability-based sampling design to ensure spatial balance.

Main Results:

• Identified 10,000 target locations for deep-sea visual observation.
• Proposed targets will nearly double the number of unique observed seafloor locations.
• Established a more geographically and environmentally representative deep-sea dataset.

Conclusions:

• The strategy provides a foundation for a more inclusive, coordinated, and statistically representative approach to deep-ocean science.
• Highlights scientific gaps in underexplored regions, guiding exploration and capacity-building.
• Essential for informed policy and sustainable deep-sea resource management.