The Time Machine framework: monitoring and prediction of biodiversity loss

Niamh Eastwood¹, William A Stubbings², Mohamed A Abou-Elwafa Abdallah², Isabelle Durance³, Jouni Paavola⁴, Martin Dallimer⁴, Jelena H Pantel⁵, Samuel Johnson⁶, Jiarui Zhou¹, J Scott Hosking⁷, James B Brown⁸, Sami Ullah⁹, Stephan Krause², David M Hannah², Sarah E Crawford¹⁰, Martin Widmann², Luisa Orsini¹¹

Affiliations

¹ Environmental Genomics Group, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK.
² School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
³ School of Biosciences and Water Research Institute, Cardiff University, Cardiff, CF10 3AX, UK.
⁴ Sustainability Research Institute, School of Earth & Environment, University of Leeds, Leeds, LS2 9JT, UK.
⁵ Department of Computer Science, Mathematics, and Environmental Science, The American University of Paris, 6 rue du Colonel Combes, 75007 Paris, France.
⁶ School of Mathematics, University of Birmingham, Birmingham, B15 2TT, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK.
⁷ British Antarctic Survey, Natural Environment Research Council, Cambridge, CB3 0ET, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK.
⁸ Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
⁹ School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK; Birmingham Institute of Forest Research, University of Birmingham, Birmingham, B15 2TT, UK.
¹⁰ Institute of Ecology, Evolution and Diversity, Department of Evolutionary Ecology and Environmental Toxicology, Goethe University Frankfurt, 60438, Germany.
¹¹ Environmental Genomics Group, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK. Electronic address: l.orsini@bham.ac.uk.

PMID: 34772522
DOI: 10.1016/j.tree.2021.09.008

Review

The Time Machine framework: monitoring and prediction of biodiversity loss

Niamh Eastwood et al. Trends Ecol Evol. 2022 Feb.

. 2022 Feb;37(2):138-146.

doi: 10.1016/j.tree.2021.09.008. Epub 2021 Nov 9.

Authors

Affiliations

¹ Environmental Genomics Group, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK.
² School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
³ School of Biosciences and Water Research Institute, Cardiff University, Cardiff, CF10 3AX, UK.
⁴ Sustainability Research Institute, School of Earth & Environment, University of Leeds, Leeds, LS2 9JT, UK.
⁵ Department of Computer Science, Mathematics, and Environmental Science, The American University of Paris, 6 rue du Colonel Combes, 75007 Paris, France.
⁶ School of Mathematics, University of Birmingham, Birmingham, B15 2TT, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK.
⁷ British Antarctic Survey, Natural Environment Research Council, Cambridge, CB3 0ET, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK.
⁸ Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
⁹ School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK; Birmingham Institute of Forest Research, University of Birmingham, Birmingham, B15 2TT, UK.
¹⁰ Institute of Ecology, Evolution and Diversity, Department of Evolutionary Ecology and Environmental Toxicology, Goethe University Frankfurt, 60438, Germany.
¹¹ Environmental Genomics Group, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK. Electronic address: l.orsini@bham.ac.uk.

PMID: 34772522
DOI: 10.1016/j.tree.2021.09.008

Abstract

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.

Keywords: artificial intelligence; climate; economic valuation; ecosystem function; pollution; time-series.

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Declaration of interests No interests are declared.

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The Time Machine framework: monitoring and prediction of biodiversity loss

Affiliations

The Time Machine framework: monitoring and prediction of biodiversity loss

Authors

Affiliations

Abstract

Conflict of interest statement

Publication types

MeSH terms