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The wicked problem of biodiversity and ecosystem services in a changing world
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  • Niamh Eastwood,
  • William Stubbings,
  • Mohamed Abdallah,
  • Isabelle Durance,
  • Jouni Paavola,
  • Martin Dallimer,
  • Jelena Pantel,
  • Samuel Johnson,
  • Jiarui Zhou,
  • James Brown,
  • Sami Ullah,
  • Stefan Krause,
  • David Hannah,
  • Sarah Crawford,
  • Martin Widman,
  • Luisa Orsini
Niamh Eastwood
University of Birmingham
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William Stubbings
University of Birmingham
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Mohamed Abdallah
University of Birmingham
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Isabelle Durance
Cardiff University Cardiff School of Biosciences
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Jouni Paavola
University of Leeds
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Martin Dallimer
University of Leeds
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Jelena Pantel
The American University of Paris
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Samuel Johnson
University of Birmingham
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Jiarui Zhou
University of Birmingham, University of Birmingham
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James Brown
Lawrence Berkeley National Laboratory
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Sami Ullah
University of Birmingham
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Stefan Krause
University of Birmingham
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David Hannah
University of Birmingham
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Sarah Crawford
Goethe-Universitat Frankfurt am Main
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Martin Widman
University of Birmingham
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Luisa Orsini
University of Birmingham
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Abstract

The Earth’s biodiversity and the ecosystem services it provides are declining rapidly because of an increasing use of natural (finite) resources to meet human needs and environmental change affecting biodiversity dynamics. Truly trans-disciplinary solutions are needed for a sustainable development of ecosystem services. Collaboration and knowledge transfer across paleo biology, climatology, global ecology, evolution, biogeography, water sciences, computer science, statistics, and economics has to be mustered for scientists, resources managers and policy makers to counter ecosystem function collapse and ecosystem services loss. We propose a novel framework that closes the implementation gap, in which long-term dynamics of biodiversity, abiotic properties, and ecosystem functions are reconstructed using an unprecedented integration of biochemical and environmental fingerprinting of biological archives spanning centuries. The long-term dynamics obtained from this fingerprinting are then placed in a machine learning pipeline to identify cause-effect relations between environmental change and biodiversity dynamics. Predictive models are tested by hindcasting, and then used to accurately forecast the future of ecosystem services and their socio-economic impact under different climate change scenarios. The framework provides accessible tools to practitioners to translate cutting-edge research into practical solutions for environmental management and practice.