Application of citizen science with the nationwide bird census
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View abstract on PubMed
Summary
This summary is machine-generated.Citizen science (CS) effectively supplements government data for bird monitoring. While CS data quality requires improvement through training, it proves essential for understanding bird communities and predicting population changes.
Area Of Science
- Ecology
- Ornithology
- Conservation Biology
Background
- Citizen science (CS) offers a powerful solution to limitations in professional data collection.
- Government and professional wildlife monitoring often face quantitative constraints.
- The 'Winter Waterbird Census of Korea' can be enhanced by incorporating CS data.
Purpose Of The Study
- To assess the quality and potential improvements of CS data for bird monitoring.
- To analyze bird communities using CS data and compare them with national datasets.
- To demonstrate the necessity and predictive power of CS in ecological studies.
Main Methods
- Conducted four bird monitoring events using citizen science from 2021 to 2022.
- Applied Benford's Law and comparative analysis to evaluate CS data quality against national data.
- Performed bird community analysis using CS-specific data.
Main Results
- Neither CS nor national data fully adhered to Benford's Law.
- CS data showed lower alpha diversity but higher beta diversity compared to national data.
- Species composition per family was similar, but individual counts differed between CS and national data.
Conclusions
- Citizen science is crucial for ecological research and can predict bird community dynamics.
- CS data quality is influenced by participant experience and perceptions.
- Systematic training is vital for enhancing the reliability of citizen science data.
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