Predictability of abrupt shifts in dryland ecosystem functioning

Paulo N. Bernardino (), Wanda De Keersmaecker, Stéphanie Horion, Stefan Oehmcke, Fabian Gieseke, Rasmus Fensholt, Ruben Van De Kerchove, Stef Lhermitte, Christin Abel, Koenraad Van Meerbeek, Jan Verbesselt and Ben Somers
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Paulo N. Bernardino: KU Leuven
Wanda De Keersmaecker: Wageningen University
Stéphanie Horion: University of Copenhagen
Stefan Oehmcke: University of Rostock
Fabian Gieseke: University of Münster
Rasmus Fensholt: University of Copenhagen
Ruben Van De Kerchove: Flemish Institute for Technological Research (VITO)
Stef Lhermitte: KU Leuven
Christin Abel: University of Copenhagen
Koenraad Van Meerbeek: KU Leuven
Jan Verbesselt: Wageningen University
Ben Somers: KU Leuven

Nature Climate Change, 2025, vol. 15, issue 1, 86-91

Abstract: Abstract Climate change and human-induced land degradation threaten dryland ecosystems, vital to one-third of the global population and pivotal to inter-annual global carbon fluxes. Early warning systems are essential for guiding conservation, climate change mitigation and alleviating food insecurity in drylands. However, contemporary methods fail to provide large-scale early warnings effectively. Here we show that a machine learning-based approach can predict the probability of abrupt shifts in Sudano–Sahelian dryland vegetation functioning (75.1% accuracy; 76.6% precision) particularly where measures of resilience (temporal autocorrelation) are supplemented with proxies for vegetation and rainfall dynamics and other environmental factors. Regional-scale predictions for 2025 highlight a belt in the south of the study region with high probabilities of future shifts, largely linked to long-term rainfall trends. Our approach can provide valuable support for the conservation and sustainable use of dryland ecosystem services, particularly in the context of climate change projected drying trends.

Date: 2025
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DOI: 10.1038/s41558-024-02201-0

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