The Time Machine framework: monitoring and prediction of biodiversity loss

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.

Details

Publication status:
Published Online
Author(s):
Authors: 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 ORCIDORCID record for J. Scott Hosking, Brown, James B., Ullah, Sami, Krause, Stephan, Hannah, David M., Crawford, Sarah E., Widmann, Martin, Orsini, Luisa

On this site: Scott Hosking
Date:
9 November, 2021
Journal/Source:
Trends in Ecology & Evolution
Page(s):
9pp
Link to published article:
https://doi.org/10.1016/j.tree.2021.09.008