TY - JOUR
T1 - Predictability of abrupt shifts in dryland ecosystem functioning
AU - Bernardino, Paulo N.
AU - De Keersmaecker, Wanda
AU - Horion, Stéphanie
AU - Oehmcke, Stefan
AU - Gieseke, Fabian
AU - Fensholt, Rasmus
AU - Van De Kerchove, Ruben
AU - Lhermitte, Stef
AU - Abel, Christin
AU - Van Meerbeek, Koenraad
AU - Verbesselt, Jan
AU - Somers, Ben
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature Limited 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
U2 - 10.1038/s41558-024-02201-0
DO - 10.1038/s41558-024-02201-0
M3 - Journal article
AN - SCOPUS:85213948827
VL - 15
SP - 86
EP - 91
JO - Nature Climate Change
JF - Nature Climate Change
SN - 1758-678X
ER -