Biodiversity `time machine' uses artificial intelligence to learn from
the past

Date:
November 9, 2021
Source:
University of Birmingham
Summary:
Experts can make crucial decisions about future biodiversity
management by using artificial intelligence to learn from past
environmental change, according to new research.



FULL STORY ========================================================================== Experts can make crucial decisions about future biodiversity management
by using artificial intelligence to learn from past environmental change, according to research at the University of Birmingham.


==========================================================================
A team, led by the University's School of Biosciences, has 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.

In a 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. She explained: "Biodiversity sustains many ecosystem services. Yet these are declining at an alarming rate. 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 added: "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." Lead author, Niamh Eastwood, is a PhD student at the University of Birmingham.

She said: "We are working with stakeholders (e.g. UK Environment Agency)
to make this framework accessible to regulators and policy makers. This
will support decision-making in regulation and conservation practices."
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.

========================================================================== Story Source: Materials provided by University_of_Birmingham. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. 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 & Evolution,
2021; DOI: 10.1016/j.tree.2021.09.008 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211109120319.htm

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