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Machine learning approaches offer the tools to process, analyse and interpret large data sets, giving insights into trends and guiding evidence\u2010based allocation of limited resources to maximise positive biodiversity outcomes. Here we describe how data acquired from remote sensing, citizen science and other monitoring approaches could feed in near\u2010real time into an early warning system for biodiversity that\u00a0integrates automated red\u2010listing of species with the identification of priority areas for conservation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Summary<\/jats:title><jats:p>Application of machine learning approaches is aiding biodiversity conservation and research at a time of rapid global change. Two emerging topics and their data requirements are presented. First, to identify areas of priority protection for preventing biodiversity loss, reinforcement learning is used by training models that take into account human disturbance and climate change under recurrent monitoring schemes. Second, neural networks are used to approximate classification of species into Red List categories of the International Union for Conservation of Nature, offering the possibility of real\u2010time re\u2010classification after events such as widespread fires and deforestation. We discuss how the identification of areas and species most at risk could be integrated into an \u2018early warning system\u2019 based on climatic monitoring, remotely sensed land\u2010use changes and near\u2010real time biological and threat data from citizen science initiatives. Such system would help guide actions to prevent biodiversity loss at the speed required for effective conservation.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ppp3.10337","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T02:46:42Z","timestamp":1667443602000},"page":"307-316","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity"],"prefix":"10.1002","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1842-9297","authenticated-orcid":false,"given":"Alexandre","family":"Antonelli","sequence":"first","affiliation":[{"name":"Science Directorate Royal Botanic Gardens Kew  Richmond UK"},{"name":"Gothenburg Global Biodiversity Centre, Department of Biological and Environmental Sciences University of Gothenburg  Gothenburg Sweden"},{"name":"Department of Biology University of Oxford  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0496-8428","authenticated-orcid":false,"given":"Kiran L.","family":"Dhanjal\u2010Adams","sequence":"additional","affiliation":[{"name":"Science Directorate Royal Botanic Gardens Kew  Richmond UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0100-0961","authenticated-orcid":false,"given":"Daniele","family":"Silvestro","sequence":"additional","affiliation":[{"name":"Gothenburg Global Biodiversity Centre, Department of Biological and Environmental Sciences University of Gothenburg  Gothenburg Sweden"},{"name":"Department of Biology University of Fribourg  Fribourg Switzerland"},{"name":"Swiss Institute of Bioinformatics  Fribourg Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143845"},{"key":"e_1_2_14_3_1","volume-title":"Living planet report 2020\u2014Bending the curve of biodiversity loss","author":"Almond R.","year":"2020"},{"key":"e_1_2_14_4_1","first-page":"173","volume-title":"Proceedings of The 33rd International Conference on Machine Learning. 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