The Time Machine framework: monitoring and prediction of biodiversity loss. (February 2022)

• Record Type: Journal Article
• 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; Brown, James B.; Ullah, Sami; Krause, Stephan; Hannah, David M.; Crawford, Sarah E.; Widmann, Martin; Orsini, Luisa
• Is Part Of: Trends in ecology & evolution. Volume 37:Number 2(2022)
• Page Start: 138
• Page End: 146
• Publication Date: 2022-02
• Subjects: ecosystem function -- artificial intelligence -- time-series -- climate -- pollution -- economic valuation; Ecology -- Periodicals; Evolution (Biology) -- Periodicals; 576.8
• DOI: 10.1016/j.tree.2021.09.008
• Languages: English
• ISSNs: 0169-5347

Abstract

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. Highlights: Chemical pollution and climate change are recognised as the two main causes of Earth's ecosystem services deterioration and overuse, linked to the loss of biodiversity. Yet, preventive interventions that mitigate this loss and preserve natural resources are inadequate because the dynamics leading to biodiversity loss are context-dependent outcomes from processes operating over many years. We propose a framework that uses sedimentary archives from watersheds to establish causal links between abiotic change and systemic loss of biodiversity, ecosystem functions and services. Just like a time machine, we go back in time and reconstruct a library of biological, chemical, environmental and functional changes at a yearly resolution, enabling the understanding of the spatiotemporal impacts of abiotic change on ecosystems and their services.