Systematic data in biodiversity studies: use it or lose it.

Area of Science:

• Biodiversity research
• Conservation science
• Biogeography

Background:

• Collections data are crucial for understanding species distributions but are prone to collection bias.
• Existing methods for biodiversity studies sometimes avoid collections data due to bias concerns.

Purpose of the Study:

• To evaluate new methods for modeling species distributions using collections data combined with abiotic factors.
• To identify high-priority biodiversity sites based on irreplaceability.

Main Methods:

• Modeling species distributions using 25,111 records (5,123 species) from Guyana, incorporating abiotic data.
• Utilizing a subset of 320 species for some modeling approaches.
• Assessing modeled distributions and selecting sites using the irreplaceability metric.

Main Results:

• The study examined the effectiveness of new modeling techniques for predicting species distributions.
• High-priority conservation areas were identified based on species uniqueness and predicted distributions.
• Challenges in data accessibility and systematist engagement were highlighted.

Conclusions:

• Modeling approaches integrating collections and abiotic data offer a robust way to assess biodiversity and guide conservation efforts.
• Addressing data formatting and engaging systematists are key to advancing biodiversity research.
• Irreplaceability is a valuable concept for prioritizing conservation sites.