Species delimitation 4.0: integrative taxonomy meets artificial intelligence

Area of Science:

• Evolutionary biology
• Genomics
• Taxonomy

Background:

• Species concepts are often based on morphology and regional descriptions, leading to ill-founded entities.
• Complex evolutionary processes like hybridization and polyploidy challenge traditional species delimitation.
• Current integrative taxonomy faces challenges including differing species concepts, lack of universal markers, and subjective data integration.

Purpose of the Study:

• To address the limitations of current species delimitation methods.
• To propose a novel approach combining integrative taxonomy with artificial intelligence.
• To reduce subjectivity and accelerate the revision of eukaryotic biodiversity.

Main Methods:

• Integrating genetics/genomics, morphology, and ecology.
• Applying artificial intelligence for automated feature learning and data integration.
• Utilizing a unified species concept for consistent delimitation.

Main Results:

• Artificial intelligence enables automated data integration and feature learning.
• The proposed approach reduces subjectivity in species delimitation.
• This method is expected to accelerate the revision of eukaryotic biodiversity.

Conclusions:

• A unified species concept combined with AI and integrative taxonomy offers a powerful solution for species delimitation.
• This approach can overcome the limitations of morphology-based and traditional methods.
• The integration of AI is crucial for unraveling complex evolutionary patterns and accelerating biodiversity research.