Predictability of abrupt shifts in dryland ecosystem functioning
Paulo N. Bernardino (Wageningen University & Research, Katholieke Universiteit Leuven)
Wanda De Keersmaecker (Wageningen University & Research, Vlaamse Instelling voor Technologisch Onderzoek)
Stéphanie Horion (University of Copenhagen)
Stefan Oehmcke (Universität Rostock)
Fabian Gieseke (Universität Münster)
Rasmus Fensholt (University of Copenhagen)
Ruben Van De Kerchove (Vlaamse Instelling voor Technologisch Onderzoek)
Stef Lhermitte (TU Delft - Mathematical Geodesy and Positioning, Katholieke Universiteit Leuven)
Christin Abel (University of Copenhagen)
Koenraad Van Meerbeek (Katholieke Universiteit Leuven)
Jan Verbesselt (Wageningen University & Research)
Ben Somers (Katholieke Universiteit Leuven)
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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.