A digital twin for real-time biodiversity forecasting with citizen science data
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View abstract on PubMed
Summary
This summary is machine-generated.Citizen science can now predict bird distributions using AI and smartphone audio, even without expert identification skills. This digital twin approach accelerates biodiversity data collection for real-time ecological monitoring.
Area Of Science
- Ecology
- Bioacoustics
- Computational Biology
Background
- Citizen science generates vast biodiversity data but faces challenges in data quality and participant expertise.
- Bridging the gap between raw data collection and actionable research outputs is crucial for effective biodiversity monitoring.
Purpose Of The Study
- To demonstrate how citizen science, combined with a digital twin and machine learning, can enable accurate real-time avian distribution predictions.
- To overcome limitations in species identification skills among citizen scientists and improve data quality.
Main Methods
- Utilized a smartphone app to collect raw audio data from citizen scientists across Finland.
- Developed a digital twin integrating machine learning for automated bird sound classification, validation, and reclassification.
- Implemented interval recordings and permanent point count networks to mitigate spatiotemporal sampling biases.
Main Results
- Generated over 15 million bird detections in two years.
- Digital twin-informed models showed higher accuracy in predicting bird spatiotemporal distributions compared to independent test data.
- The machine learning classifiers continuously improved through ongoing data validation and reclassification.
Conclusions
- This scalable approach enhances inclusivity in citizen science by enabling contributions from individuals without expert identification skills.
- The digital twin methodology accelerates the generation of reliable biodiversity information, particularly for understudied regions.
- The system has significant potential for real-time biomonitoring and advancing ecological research.
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