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Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships

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  • Jakob Runge

    (Institut für Datenwissenschaften
    Institute of Computer Engineering and Microelectronics)

Abstract

Detecting and quantifying the causal relations of ecosystem functioning is a challenging endeavor. A global study on grasslands illustrates how reasoning about underlying assumptions, from confounding and nonlinearity to fundamental questions of determinism, is key to unlocking the potential of modern causal inference approaches in ecology.

Suggested Citation

  • Jakob Runge, 2023. "Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships," Nature Communications, Nature, vol. 14(1), pages 1-3, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37546-1
    DOI: 10.1038/s41467-023-37546-1
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    References listed on IDEAS

    as
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    2. Siyang Leng & Huanfei Ma & Jürgen Kurths & Ying-Cheng Lai & Wei Lin & Kazuyuki Aihara & Luonan Chen, 2020. "Partial cross mapping eliminates indirect causal influences," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
    6. Jakob Runge & Sebastian Bathiany & Erik Bollt & Gustau Camps-Valls & Dim Coumou & Ethan Deyle & Clark Glymour & Marlene Kretschmer & Miguel D. Mahecha & Jordi Muñoz-Marí & Egbert H. Nes & Jonas Peters, 2019. "Inferring causation from time series in Earth system sciences," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
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