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eDNA and AI Identification Reveal Complementary Signals in Phytoplankton Monitoring
Yilin Wang1, Juntao Fan1, Jing Lu2
1State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
Environmental Science & Technology
|April 23, 2026
Summary
Integrating environmental DNA (eDNA) metabarcoding, AI, and manual methods improves phytoplankton monitoring. Combining approaches enhances taxonomic coverage and community structure analysis in aquatic ecosystems.
Area of Science:
- Aquatic Ecology
- Environmental Monitoring
- Molecular Biology
Background:
- Phytoplankton are crucial for aquatic ecosystems, regulating energy transfer and biogeochemical cycles.
- Accurate phytoplankton community characterization is vital but challenging due to limitations of individual monitoring methods.
- Different methods provide complementary data, necessitating integrated approaches for comprehensive assessment.
Purpose of the Study:
- To compare the strengths and complementarity of environmental DNA (eDNA) metabarcoding, AI-assisted identification (YOLOv7), and manual identification for phytoplankton monitoring.
- To evaluate the benefits of integrating these diverse methods for a more robust understanding of phytoplankton communities.
- To assess phytoplankton-related risks in a regulated river-reservoir system.
Main Methods:
- A comparative integration study combining eDNA metabarcoding, AI-assisted identification (YOLOv7), and manual identification.
- Field surveys in a regulated river-reservoir continuum (Three Gorges region) integrating molecular and morphology-based methods with environmental covariates.
- Statistical analysis to evaluate method complementarity and environmental-community relationships.
Main Results:
- eDNA metabarcoding expanded taxonomic coverage and detected rare taxa, while AI and manual methods provided abundance data for dominant morphotypes.
- Integration of methods yielded complementarity gains, increasing overall taxonomic detection and improving community structure characterization.
- Preliminary identification of two sites with elevated phytoplankton-related risk (low diversity, high biomass).
Conclusions:
- Molecular (eDNA) and morphology-based (AI, manual) methods offer complementary ecological information for phytoplankton monitoring.
- Integrated approaches enhance the accuracy and comprehensiveness of phytoplankton community assessment.
- Further research requires regional calibration, expanded taxonomic training, and temporal validation for broader application and reliable early warning systems.
