Using citizen science data for predicting the timing of ecological phenomena across regions

Area of Science:

• Ecology
• Computational Biology
• Data Science

Background:

• Limited long-term observational data hinders accurate prediction of intra-annual ecological changes.
• Increasing availability of time-stamped citizen science records, including photographic evidence, offers new opportunities for ecological monitoring.
• Traditional statistical and machine learning methods struggle with sparse, long-term ecological datasets.

Purpose of the Study:

• To develop a novel framework for predicting ecological phenomena using citizen science data.
• To leverage machine learning algorithms to model ecological events based on temporal observation records.
• To address the challenge of temporal bias in citizen science data for ecological prediction.

Main Methods:

• Developed a framework based on the concept of relative phenological niche.
• Utilized machine learning algorithms to model citizen science observations as temporal samples of environmental conditions.
• Validated the approach for predicting temporal dynamics of ecological events across geographical scales.

Main Results:

• The novel framework accurately predicts the temporal dynamics of ecological events.
• The approach demonstrates robustness against temporal bias in recording effort.
• Successfully modeled ecological phenomena across large geographical scales using citizen science data.

Conclusions:

• Citizen science data holds vast potential for predicting ecological phenomena across space and in near real time.
• The developed framework is readily applicable to ecologists and practitioners using predictive modeling.
• This approach enhances ecological forecasting capabilities by integrating diverse, accessible data sources.