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The Time Machine framework: monitoring and prediction of biodiversity loss

Eastwood, Niamh, Stubbings, William A., Abou-Elwafa Abdallah, Mohamed A., Durance, Isabelle ORCID:, Paavola, Jouni, Dallimer, Martin, Pantel, Jelena H., Johnson, Samuel, Zhou, Jiarui, Hosking, J. Scott, Brown, James B., Ullah, Sami, Krause, Stephan, Hannah, David M., Crawford, Sarah E., Widmann, Martin and Orsini, Luisa 2022. The Time Machine framework: monitoring and prediction of biodiversity loss. Trends in Ecology and Evolution 37 (2) , pp. 138-146. 10.1016/j.tree.2021.09.008

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Transdisciplinary solutions are needed to achieve the sustainability of ecosystem services for future generations. We propose a framework to identify the causes of ecosystem function loss and to forecast the future of ecosystem services under different climate and pollution scenarios. The framework (i) applies an artificial intelligence (AI) time-series analysis to identify relationships among environmental change, biodiversity dynamics and ecosystem functions; (ii) validates relationships between loss of biodiversity and environmental change in fabricated ecosystems; and (iii) forecasts the likely future of ecosystem services and their socioeconomic impact under different pollution and climate scenarios. We illustrate the framework by applying it to watersheds, and provide system-level approaches that enable natural capital restoration by associating multidecadal biodiversity changes to chemical pollution.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Biosciences
Publisher: Cell Press
ISSN: 0169-5347
Date of First Compliant Deposit: 15 November 2021
Date of Acceptance: 1 November 2021
Last Modified: 06 Nov 2023 13:18

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