{"id":53822,"date":"2021-12-03T11:27:57","date_gmt":"2021-12-03T10:27:57","guid":{"rendered":"https:\/\/aktuelles.uni-frankfurt.de\/?p=53822"},"modified":"2023-02-17T13:45:27","modified_gmt":"2023-02-17T12:45:27","slug":"biodiversity-time-machine-uses-ai-to-learn-from-the-past","status":"publish","type":"post","link":"https:\/\/aktuelles.uni-frankfurt.de\/en\/english\/biodiversity-time-machine-uses-ai-to-learn-from-the-past\/","title":{"rendered":"Biodiversity \u2018time machine\u2019 uses AI to learn from the past"},"content":{"rendered":"<div class=\"wp-block-image\"><figure class=\"aligncenter size-full\"><a href=\"https:\/\/aktuelles.uni-frankfurt.de\/wp-content\/uploads\/2021\/12\/Beitragsbild_Biodiversitaet-Zeitmaschine.jpg\"><img fetchpriority=\"high\" decoding=\"async\" width=\"650\" height=\"450\" src=\"https:\/\/aktuelles.uni-frankfurt.de\/wp-content\/uploads\/2021\/12\/Beitragsbild_Biodiversitaet-Zeitmaschine.jpg\" alt=\"\" class=\"wp-image-53820\" srcset=\"https:\/\/aktuelles.uni-frankfurt.de\/wp-content\/uploads\/2021\/12\/Beitragsbild_Biodiversitaet-Zeitmaschine.jpg 650w, https:\/\/aktuelles.uni-frankfurt.de\/wp-content\/uploads\/2021\/12\/Beitragsbild_Biodiversitaet-Zeitmaschine-300x208.jpg 300w\" sizes=\"(max-width: 650px) 100vw, 650px\" \/><\/a><figcaption>Sedimentary archives serve as base for the \u2018time machine\u2018 framework (Photo: Rhododendrites\/<a href=\"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0\/deed.en\" target=\"_blank\" rel=\"noreferrer noopener\">CC BY-SA 4.0<\/a>)<\/figcaption><\/figure><\/div>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Experts can make crucial decisions about future biodiversity management by using artificial intelligence to learn from past environmental change, according to an international team of researchers including Dr. Sarah Crawford from Goethe University Frankfurt.<\/p>\n\n\n\n<p>The team, led by the University of Birmingham, has proposed a \u2018time machine framework\u2019 that uses sedimentary archives from watersheds to establish causal links between abiotic change and systemic loss of biodiversity, ecosystem functions and services.<\/p>\n\n\n\n<p>The framework will help decision-makers effectively go back in time to observe the links between biodiversity, pollution events and environmental changes such as climate change as they occurred and examine the impacts they had on ecosystems.<\/p>\n\n\n\n<p>In a new paper the team sets out how these insights can be used to forecast the future of ecosystem services such as climate change mitigation, food provisioning and clean water.<\/p>\n\n\n\n<p>Managing biodiversity whilst ensuring the delivery of ecosystem services is a complex problem because of limited resources, competing objectives and the need for economic profitability. Protecting every species is impossible. The time machine framework offers a way to prioritize conservation approaches and mitigation interventions.<\/p>\n\n\n\n<p>The framework draws on the expertise of biologists, ecologists, environmental scientists, computer scientists and economists. It is the result of a cross-disciplinary collaboration among the University of Birmingham, The Alan Turing Institute, The University of Leeds, the University of Cardiff, The University of California Berkeley, The American University of Paris and the Goethe University Frankfurt.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-background\" style=\"background-color:#eeeeee\"><strong>Publication:<\/strong> Niamh Eastwood, William A. Stubbings, Mohamed A.Abou-Elwafa Abdallah, Isabelle Durance, Jouni Paavola, Martin Dallimer, Jelena H. Pantel, Samuel Johnson, Jiarui Zhou, J. Scott Hosking, James B. Brown, Sami Ullah, Stephan Krause, David M. Hannah, Sarah E. Crawford, Martin Widmann, Luisa Orsini: <strong>The Time Machine framework: monitoring and prediction of biodiversity loss. <\/strong>Trends in Ecology and Evolution <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0169534721002597?dgcid=author\" target=\"_blank\" rel=\"noreferrer noopener\">The Time Machine framework: monitoring and prediction of biodiversity loss &#8211; ScienceDirect<\/a><\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><em>Source: <a href=\"https:\/\/www.birmingham.ac.uk\/news\/latest\/2021\/11\/biodiversity-time-machine-uses-artificial-intelligence-to-learn-from-the-past.aspx\" target=\"_blank\" rel=\"noreferrer noopener\">Biodiversity &#8218;time machine&#8216; uses artificial intelligence to learn from the past (birmingham.ac.uk)<\/a><\/em><\/p>","protected":false},"excerpt":{"rendered":"<p>Experts can make crucial decisions about future biodiversity management by using artificial intelligence to learn from past environmental change, according to an international team of researchers including Dr. Sarah Crawford [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":53820,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"footnotes":""},"categories":[126,254],"tags":[246,241,230],"post_folder":[],"class_list":["post-53822","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-english","category-research","tag-biological-sciences","tag-computer-science","tag-economics-and-business"],"yoast_head":"<!-- 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