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

AI Article Synopsis

  • Long-term observational data is scarce, limiting the prediction of ecological variations using traditional statistical or machine-learning methods.
  • A new framework utilizes citizen-science data and machine-learning to model ecological observations based on environmental conditions, enhancing prediction accuracy.
  • This approach demonstrates the potential of using citizen-science data for real-time predictions of ecological events across large areas, making it accessible for ecologists and practitioners.

Article Abstract

The scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-learning algorithms to model observation records as a temporal sample of environmental conditions in which the represented ecological phenomenon occurs. Our approach accurately predicts the temporal dynamics of ecological events across large geographical scales and is robust to temporal bias in recording effort. These results highlight the vast potential of citizen-science observation data to predict ecological phenomena across space, including in near real time. The framework is also easily applicable for ecologists and practitioners already using machine-learning and statistics-based predictive approaches.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11266983PMC
http://dx.doi.org/10.1093/biosci/biae041DOI Listing

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