Natural History Collections at the Crossroads: Shifting Priorities and Data-Driven Opportunities.

Area of Science:

• Ecology
• Biodiversity Science
• Data Science

Background:

• Natural history collections are vital for eco-evolutionary and environmental research.
• Current collection strategies are often limited by a taxonomic focus.
• Accelerating environmental change and biodiversity loss necessitate evolved approaches.

Purpose of the Study:

• To propose a data-driven framework for optimizing sampling design and research prioritization in natural history collections.
• To integrate modern statistical approaches with data science and digital technologies.
• To enhance the role of collections as critical research infrastructure.

Main Methods:

• Utilizing modern statistical approaches, including information theory and decision science.
• Considering biodiversity, environmental, spatial, and temporal dimensions in sampling design.
• Developing a flexible framework for strategic collecting, databasing, and digitization.

Main Results:

• Demonstrated how data-driven tools can support strategic collection efforts.
• Provided an evidence-based pathway for collections to address contemporary environmental challenges.
• Illustrated the framework with a case study on wombat sampling in Australia.

Conclusions:

• Sampling strategies for natural history collections must evolve beyond taxonomy to address ecological challenges.
• A data-driven framework can enhance the strategic value of collections for environmental research.
• Integrating data science and statistical methods is crucial for future-proofing collections.