The Amazon forest is influenced by strong feedback loops between its biotic and abiotic components. Local forest loss increases CO2 emissions, which, in turn, drives climate change, raising temperatures and reducing rainfall, causing further forest loss. Additionally, forest loss disrupts important forest-rainfall cycles, threatening the overall forest stability. These feedbacks make the system vulnerable to tipping points, where parts of the forest could transition to a degraded state. Slower recovery to short-term disturbances, hereafter named reduced stability, is considered an early warning indicator of such tipping points. However, the role of tree species diversity in regulating this vulnerability remains poorly understood, especially across spatial scales. To examine how tree species diversity impacts tipping point likelihoods across multiple spatial scales, we used modelled tree species diversity data at the alpha (local), beta (asynchrony across local communities), and gamma (regional) scales. We quantified tipping likelihood on the same scales using temporal autocorrelation trends in monthly satellite-derived vegetation productivity time series over 2001–2019. Our findings reveal higher tipping likelihoods at the alpha level (25 km2) compared to the gamma level (209 903 km2), indicating that Amazonian tipping points are more likely to occur locally than regionally or basin-wide. We also observe significant but weak positive linear relationships between tree species diversity and stability at both alpha and beta scales. This emphasizes both the importance of biodiversity conservation at multiple spatial scales and the complexity of understanding the stability of the Amazon forest.

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.

Dynamic ecosystems, such as the Amazon forest, are expected to show critical slowing down behavior, or slower recovery from recurrent small perturbations, as they approach an ecological threshold to a different ecosystem state. Drought occurrences are becoming more prevalent across the Amazon, with known negative effects on forest health and functioning, but their actual role in the critical slowing down patterns still remains elusive. In this study, we evaluate the effect of trends in extreme drought occurrences on temporal autocorrelation (TAC) patterns of satellite-derived indices of vegetation activity, an indicator of slowing down, between 2001 and 2019. Differentiating between extreme drought frequency, intensity, and duration, we investigate their respective effects on the slowing down response. Our results indicate that the intensity of extreme droughts is a more important driver of slowing down than their duration, although their impacts vary across the different Amazon regions. In addition, areas with more variable precipitation are already less ecologically stable and need fewer droughts to induce slowing down. We present findings indicating that most of the Amazon region does not show an increasing trend in TAC. However, the predicted increase in extreme drought intensity and frequency could potentially transition significant portions of this ecosystem into a state with altered functionality.