• Energy Research

Tipping elements are nonlinear subsystems of the Earth system that have the potential to abruptly shift to another state if environmental change occurs close to a critical threshold with large consequences for human societies and ecosystems. Among these tipping elements may be the Amazon rainforest, which has been undergoing intensive anthropogenic activities and increasingly frequent droughts. Here, we assess how extreme deviations from climatological rainfall regimes may cause local forest collapse that cascades through the coupled forest–climate system. We develop a conceptual dynamic network model to isolate and uncover the role of atmospheric moisture recycling in such tipping cascades. We account for heterogeneity in critical thresholds of the forest caused by adaptation to local climatic conditions. Our results reveal that, despite this adaptation, a future climate characterized by permanent drought conditions could trigger a transition to an open canopy state particularly in the southern Amazon. The loss of atmospheric moisture recycling contributes to one-third of the tipping events. Thus, by exceeding local thresholds in forest adaptive capacity, local climate change impacts may propagate to other regions of the Amazon basin, causing a risk of forest shifts even in regions where critical thresholds have not been crossed locally.

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.

The south-eastern Amazon rainforest is subject to ongoing deforestation and is expected to become drier due to climate change. Recent analyses of the distribution of tree cover in the tropics show three modes that have been interpreted as representing alternative stable states: forest, savanna and treeless states. This situation implies that a change in environmental conditions, such as in the climate, could cause critical transitions from a forest towards a savanna ecosystem. Shifts to savanna might also occur if perturbations such as deforestation exceed a critical threshold. Recovering the forest would be difficult as the savanna will be stabilized by a feedback between tree cover and fire. Here we explore how environmental changes and perturbations affect the forest by using a simple model with alternative tree-cover states. We focus on the synergistic effects of precipitation reduction and deforestation on the probability of regime shifts in the south-eastern Amazon rainforest. The analysis indicated that in a large part of the south-eastern Amazon basin rainforest and savanna could be two alternative states, although massive forest dieback caused by mean-precipitation reduction alone is unlikely. However, combinations of deforestation and climate change triggered up to 6.6 times as many local regime shifts than the two did separately, causing large permanent forest losses in the studied region. The results emphasize the importance of reducing deforestation rates in order to prevent a climate-induced dieback of the south-eastern Amazon rainforest.

AbstractThe possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern1–3. For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system1. Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

This dataset contains the scripts and data used for the paper "Double stress of waterlogging and drought drives forest–savanna coexistence", PNAS (2023). The work represents a collaborative effort from the Department of Earth and Planetary Sciences at Rutgers University, the Department of Physics and Graduate Program in Ecology at the Federal University of Santa Catarina, the Department of Plant Biology at the University of Campinas, the Non-Linear Physics Group at the University of Santiago de Compostela, and the Department of Civil and Environmental Engineering at Michigan State University. The dataset includes the raw data files for analyses. Scripts to analyze and plot the data are available on https://github.com/caiomattos/doublestress. Please refer to the paper for a complete description of the methods and results associated with this dataset. If the data presented here is central to your results, please consider inviting the data creators for a possible co-authorship.

Publicly available remote sensing products have boosted science in many ways. The openness of these data sources suggests high reproducibility. However, as we show here, results may be specific to versions of the data products that can become unavailable as new versions are posted. We focus on remotely-sensed tree cover. Recent studies have used this public resource to detect multi-modality in tree cover in the tropical and boreal biomes. Such patterns suggest alternative stable states separated by critical tipping points. This has important implications for the potential response of these ecosystems to global climate change. For the boreal region, four distinct ecosystem states (i.e., treeless, sparse and dense woodland, and boreal forest) were previously identified by using the Collection 3 data of MODIS Vegetation Continuous Fields (VCF). Since then, the MODIS VCF product has been updated to Collection 5; and a Landsat VCF product of global tree cover at a fine spatial resolution of 30 meters has been developed. Here we compare these different remote-sensing products of tree cover to show that identification of alternative stable states in the boreal biome partly depends on the data source used. The updated MODIS data and the newer Landsat data consistently demonstrate three distinct modes around similar tree-cover values. Our analysis suggests that the boreal region has three modes: one sparsely vegetated state (treeless), one distinct 'savanna-like' state and one forest state, which could be alternative stable states. Our analysis illustrates that qualitative outcomes of studies may change fundamentally as new versions of remote sensing products are used. Scientific reproducibility thus requires that old versions remain publicly available.