The Time Machine framework: monitoring and prediction of biodiversity loss

Eastwood, Niamh; Stubbings, William A.; Abou-Elwafa Abdallah, Mohamed A.; Durance, Isabelle; Paavola, Jouni; Dallimer, Martin; Pantel, Jelena H.; Johnson, Samuel; Zhou, Jiarui; Hosking, J. Scott ORCID: https://orcid.org/0000-0002-3646-3504; Brown, James B.; Ullah, Sami; Krause, Stephan; Hannah, David M.; Crawford, Sarah E.; Widmann, Martin; Orsini, Luisa. 2022 The Time Machine framework: monitoring and prediction of biodiversity loss. Trends in Ecology & Evolution, 37 (2). 138-146. 10.1016/j.tree.2021.09.008

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Abstract/Summary

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:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.tree.2021.09.008
ISSN:	01695347
Additional Keywords:	ecosystem function, artificial intelligence, time-series climate, pollution, economic valuation
Date made live:	10 Nov 2021 09:53 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/531367

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