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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.<\/jats:p>","DOI":"10.1093\/biosci\/biae041","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T08:42:45Z","timestamp":1720514565000},"page":"383-392","source":"Crossref","is-referenced-by-count":13,"title":["Using citizen science data for predicting the timing of ecological phenomena across regions"],"prefix":"10.1093","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0666-9755","authenticated-orcid":false,"given":"C\u00e9sar","family":"Capinha","sequence":"first","affiliation":[{"name":"Centre of Geographical Studies, Institute of Geography and Spatial Planning of the University of Lisbon , Lisbon , Portugal"},{"name":"Associate Laboratory Terra Lisbon , Portugal"}]},{"given":"Ana","family":"Ceia-Hasse","sequence":"additional","affiliation":[{"name":"BIOPOLIS, CIBIO, InBIO Associate 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