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

19873 6 106942 2 324214 document 106942 biae041.pdf application/pdf d25439747be376a7380ebac31b7ae2d8 MD5 1262808 2024-07-11 08:53:51 https://pure.iiasa.ac.at/id/eprint/19873/1/biae041.pdf 19873 1 1 application/pdf text en public cc_by_4

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published 106943 1 324218 document 106943 indexcodes.txt text/x-c++ a25d59dd929cadf63266d1bc8fdbfa84 MD5 14106 2024-07-11 08:54:48 https://pure.iiasa.ac.at/id/eprint/19873/2/indexcodes.txt 19873 2 2 text/x-c++ other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/106942 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/106942 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/106942 archive 2 disk0/00/01/98/73 2024-07-11 08:54:27 2024-07-11 08:54:27 2024-07-11 08:54:27 article show Capinha C. Ceia-Hasse A. de-Miguel S. Vila-Viçosa C. Porto M. Jarić I. Tiago P. Fernández N. Valdez J. McCallum I. 1731 0000-0002-5812-9988 Pereira H.M. Using citizen science data for predicting the timing of ecological phenomena across regions pub ASA NODES 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. 2024-07-09 doi:10.1093/biosci/biae041 202 public BioScience biae041 TRUE 0006-3568 https://zenodo.org/records/11124605 data Europa Biodiversity Observation Network: integrating data streams to support policy (EuropaBON, H2020 101003553) yes info:eu-repo/grantAgreement/EC/H2020/101003553/EU//EuropaBON info:eu-repo/semantics/article info:eu-repo/semantics/openAccess