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

Tuesday 09 Nov 2021

Experts can make crucial decisions about future biodiversity management by using artificial intelligence (AI) to learn from past environmental change, according to a new research paper out today (Tuesday 9 November 2021).

The paper, ‘The Time Machine framework: monitoring and prediction of biodiversity loss’, 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.

The authors of the paper have proposed a ‘time machine framework’ that 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. The framework draws on the expertise of biologists, ecologists, environmental scientists, computer scientists and economists.

In the new paper, published in Trends in Ecology and Evolution, 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. Using this information, stakeholders can prioritise actions which will provide the greatest impact.

Principal investigator, Dr Luisa Orsini, is an Associate Professor at the University of Birmingham and Fellow of The Alan Turing Institute, the UK’s national institute for data science and AI. Luisa explains: “Biodiversity sustains many ecosystem services, but these are now   declining at an alarming rate with potentially damaging effects. As we discuss vital issues like these at the COP26 Summit in Glasgow, we might be more aware than ever that future generations may not be able to enjoy nature’s services if we fail to protect biodiversity.”

Biodiversity loss happens over many years and is often caused by the cumulative effect of multiple environmental threats. Only by quantifying biodiversity before, during and after pollution events, can the causes of biodiversity and ecosystem service loss be identified, say the researchers.

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.

Dr Orsini adds: “We have already seen how a lack of understanding of the interlinked processes underpinning ecosystem services has led to mismanagement, with negative impacts on the environment, the economy and on our wellbeing. We need a whole-system, evidence-based approach in order to make the right decisions in the future. Our time-machine framework is an important step towards that goal.”