Biodiversity ‘time machine’ uses AI to learn from the past

Sedimentary archives serve as base for the ‘time machine‘ framework (Photo: Rhododendrites/CC BY-SA 4.0)

Experts can make crucial decisions about future biodiversity management by using artificial intelligence to learn from past environmental change, according to an international team of researchers including Dr. Sarah Crawford from Goethe University Frankfurt.

The team, led by the University of Birmingham, has proposed a ‘time machine framework’ that uses sedimentary archives from watersheds to establish causal links between abiotic change and systemic loss of biodiversity, ecosystem functions and services.

The framework will help decision-makers effectively go back in time to observe the links between biodiversity, pollution events and environmental changes such as climate change as they occurred and examine the impacts they had on ecosystems.

In a new paper the team sets out how these insights can be used to forecast the future of ecosystem services such as climate change mitigation, food provisioning and clean water.

Managing biodiversity whilst ensuring the delivery of ecosystem services is a complex problem because of limited resources, competing objectives and the need for economic profitability. Protecting every species is impossible. The time machine framework offers a way to prioritize conservation approaches and mitigation interventions.

The framework draws on the expertise of biologists, ecologists, environmental scientists, computer scientists and economists. It is the result of a cross-disciplinary collaboration among the University of Birmingham, The Alan Turing Institute, The University of Leeds, the University of Cardiff, The University of California Berkeley, The American University of Paris and the Goethe University Frankfurt.

Publication: Niamh Eastwood, William A. Stubbings, Mohamed A.Abou-Elwafa Abdallah, Isabelle Durance, Jouni Paavola, Martin Dallimer, Jelena H. Pantel, Samuel Johnson, Jiarui Zhou, J. Scott Hosking, James B. Brown, Sami Ullah, Stephan Krause, David M. Hannah, Sarah E. Crawford, Martin Widmann, Luisa Orsini: The Time Machine framework: monitoring and prediction of biodiversity loss. Trends in Ecology and Evolution The Time Machine framework: monitoring and prediction of biodiversity loss – ScienceDirect

Source: Biodiversity ‘time machine’ uses artificial intelligence to learn from the past (

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