Recurrent droughts increase risk of cascading tipping events by outpacing adaptive capacities in the Amazon rainforest

Amazon rainforest adjusts to long-term experimental drought

Drought-induced mortality is expected to cause substantial biomass loss in the Amazon basin. However, rainforest responses to prolonged drought are largely unknown. Here, we demonstrate that an Amazonian rainforest plot subjected to more than two decades of large-scale experimental drought reached eco-hydrological stability. After elevated tree mortality during the first 15 years, ecosystem-level structural changes resulted in the remaining trees no longer experiencing drought stress. The loss of the largest trees led to increasing water availability for the remaining trees, stabilizing biomass in the last 7 years of the experiment. Hydraulic variables linked to physiological stress, such as leaf water potential, sap flow and tissue water content, converged to the values observed in a corresponding non-droughted control forest, indicating hydraulic homeostasis. While it prevented drought-induced collapse, eco-hydrological stabilization resulted in a forest with reduced biomass and carbon accumulation in wood. These findings show how tropical rainforests may be resilient to persistent soil drought.

Applicability of spatial early warning signals to complex network dynamics

Early warning signals (EWSs) for complex dynamical systems aim to anticipate tipping points before they occur. While signals computed from time-series data, such as temporal variance, are useful for this task, they are costly to obtain in practice because they need many samples over time to calculate. Spatial EWSs use just a single sample per spatial location and aggregate the samples over space rather than time to try to mitigate this limitation. However, although many complex systems in nature and society form diverse networks, the performance of spatial EWSs is mostly unknown for general networks because the vast majority of studies of spatial EWSs have been on regular lattice networks. Therefore, we have carried out a comprehensive investigation of six major spatial EWSs on various networks. We find that the winning EWS depends on tipping scenarios, although the coefficient of variation and spatial skewness tend to outperform alternative EWSs. We also find that spatial EWSs behave in a drastically different manner between the square lattice and complex networks and tend to be more reliable for the latter than the former. The present results encourage further studies of spatial EWSs on complex networks.

Towards ecosystem-based techniques for tipping point detection

An ecosystem shifts to an alternative stable state when a threshold of accumulated pressure (i.e. direct impact of environmental change or human activities) is exceeded. Detecting this threshold in empirical data remains a challenge because ecosystems are governed by complex interlinkages and feedback loops between their components and pressures. In addition, multiple feedback mechanisms exist that can make an ecosystem resilient to state shifts. Therefore, unless a broad ecological perspective is used to detect state shifts, it remains questionable to what extent current detection methods really capture ecosystem state shifts and whether inferences made from smaller scale analyses can be implemented into ecosystem management. We reviewed the techniques currently used for retrospective detection of state shifts detection from empirical data. We show that most techniques are not suitable for taking a broad ecosystem perspective because approximately 85% do not combine intervariable non-linear relationships and high-dimensional data from multiple ecosystem variables, but rather tend to focus on one subsystem of the ecosystem. Thus, our perception of state shifts may be limited by methods that are often used on smaller data sets, unrepresentative of whole ecosystems. By reviewing the characteristics, advantages, and limitations of the current techniques, we identify methods that provide the potential to incorporate a broad ecosystem-based approach. We therefore provide perspectives into developing techniques better suited for detecting ecosystem state shifts that incorporate intervariable interactions and high-dimensionality data.

Land system changes of terrestrial tipping elements on Earth under global climate pledges: 2000-2100

Tipping elements on Earth are components that undergo rapid and irreversible changes when climate change reaches a tipping point. They are highly sensitive to climate variations and serve as early warning signs of global change. Human activities, including global climate pledges, significantly influence the climate and the state of tipping elements. Land changes serve as the external and intuitive response of tipping elements to climate change, making it essential to identify shifts in the land system. We produced a 1-km land system dataset for terrestrial tipping elements on Earth for the years 2000, 2010, 2020, and 2100 under global climate pledges by integrating the GCAM with a modified version of CLUMondo. Our dataset includes 30 thematic categories, combining three density types and ten land cover types. The dataset illustrates potential land system changes under global climate pledges, contrasting with common SSP and RCP scenarios. Our simulations demonstrate high accuracy, offering valuable insights into tipping elements and the assessment of the impacts of global climate pledges on Earth.

Water deficit and storm disturbances co-regulate Amazon rainforest seasonality

Canopy leaf abundance of Amazon rainforests increases in the dry season but decreases in the wet season, contrary to earlier expectations of water stress adversely affecting plant functions. Drivers of this seasonality, particularly the role of water availability, remain debated. We introduce satellite-based ecophysiological indicators to demonstrate that Amazon rainforests are constrained by water during dry seasons despite light-driven canopy greening. Evidence includes a shifted partitioning of photosynthetically active radiation toward more isoprene emissions and synchronized declines in leaf and xylem water potentials. In addition, we find that convective storms attenuate light-driven ecosystem greening in the late dry season and then reverse to net leaf loss in the wet season, improving rainforest leaf area predictability by 24 to 31%. These findings highlight the susceptibility of Amazon rainforests to increasing risks of drought and windthrow disturbances under warming.

Drought as an emergent driver of ecological transformation in the twenty-first century

Under climate change, ecosystems are experiencing novel drought regimes, often in combination with stressors that reduce resilience and amplify drought's impacts. Consequently, drought appears increasingly likely to push systems beyond important physiological and ecological thresholds, resulting in substantial changes in ecosystem characteristics persisting long after drought ends (i.e., ecological transformation). In the present article, we clarify how drought can lead to transformation across a wide variety of ecosystems including forests, woodlands, and grasslands. Specifically, we describe how climate change alters drought regimes and how this translates to impacts on plant population growth, either directly or through drought's interactions with factors such as land management, biotic interactions, and other disturbances. We emphasize how interactions among mechanisms can inhibit postdrought recovery and can shift trajectories toward alternate states. Providing a holistic picture of how drought initiates long-term change supports the development of risk assessments, predictive models, and management strategies, enhancing preparedness for a complex and growing challenge.

Amazon forest biogeography predicts resilience and vulnerability to drought

Amazonia contains the most extensive tropical forests on Earth, but Amazon carbon sinks of atmospheric CO2 are declining, as deforestation and climate-change-associated droughts1-4 threaten to push these forests past a tipping point towards collapse5-8. Forests exhibit complex drought responses, indicating both resilience (photosynthetic greening) and vulnerability (browning and tree mortality), that are difficult to explain by climate variation alone9-17. Here we combine remotely sensed photosynthetic indices with ground-measured tree demography to identify mechanisms underlying drought resilience/vulnerability in different intact forest ecotopes18,19 (defined by water-table depth, soil fertility and texture, and vegetation characteristics). In higher-fertility southern Amazonia, drought response was structured by water-table depth, with resilient greening in shallow-water-table forests (where greater water availability heightened response to excess sunlight), contrasting with vulnerability (browning and excess tree mortality) over deeper water tables. Notably, the resilience of shallow-water-table forest weakened as drought lengthened. By contrast, lower-fertility northern Amazonia, with slower-growing but hardier trees (or, alternatively, tall forests, with deep-rooted water access), supported more-drought-resilient forests independent of water-table depth. This functional biogeography of drought response provides a framework for conservation decisions and improved predictions of heterogeneous forest responses to future climate changes, warning that Amazonia's most productive forests are also at greatest risk, and that longer/more frequent droughts are undermining multiple ecohydrological strategies and capacities for Amazon forest resilience.

The Andes-Amazon-Atlantic pathway: A foundational hydroclimate system for social-ecological system sustainability

The Amazon River Basin's extraordinary social-ecological system is sustained by various water phases, fluxes, and stores that are interconnected across the tropical Andes mountains, Amazon lowlands, and Atlantic Ocean. This "Andes-Amazon-Atlantic" (AAA) pathway is a complex hydroclimatic system linked by the regional water cycle through atmospheric circulation and continental hydrology. Here, we aim to articulate the AAA hydroclimate pathway as a foundational system for research, management, conservation, and governance of aquatic systems of the Amazon Basin. We identify and describe the AAA pathway as an interdependent, multidirectional, and multiscale hydroclimate system. We then present an assessment of recent (1981 to 2020) changes in the AAA pathway, primarily reflecting an acceleration in the rates of hydrologic fluxes (i.e., water cycle intensification). We discuss how the changing AAA pathway orchestrates and impacts social-ecological systems. We conclude with four recommendations for the sustainability of the AAA pathway in ongoing research, management, conservation, and governance.

Critical slowing down of the Amazon forest after increased drought occurrence

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.

Trophic rewilding as a restoration approach under emerging novel biosphere conditions

Rewilding is a restoration approach that aims to promote self-regulating complex ecosystems by restoring non-human ecological processes while reducing human control and pressures. Rewilding is forward-looking in that it aims to enhance functionality for biodiversity, accepting and indeed promoting the dynamic nature of ecosystems, rather than fixating on static composition or structure. Rewilding is thus especially relevant in our epoch of increasingly novel biosphere conditions, driven by strong human-induced global change. Here, we explore this hypothesis in the context of trophic rewilding - the restoration of trophic complexity mediated by wild, large-bodied animals, known as 'megafauna'. This focus reflects the strong ecological impacts of large-bodied animals, their widespread loss during the last 50,000 years and their high diversity and ubiquity in the preceding 50 million years. Restoring abundant, diverse, wild-living megafauna is expected to promote vegetation heterogeneity, seed dispersal, nutrient cycling and biotic microhabitats. These are fundamental drivers of biodiversity and ecosystem function and are likely to gain importance for maintaining a biodiverse biosphere under increasingly novel ecological conditions. Non-native megafauna species may contribute to these effects as ecological surrogates of extinct species or by promoting ecological functionality within novel assemblages. Trophic rewilding has strong upscaling potential via population growth and expansion of wild fauna. It is likely to facilitate biotic adaptation to changing climatic conditions and resilience to ecosystem collapse, and to curb some negative impacts of globalization, notably the dominance of invasive alien plants. Finally, we discuss the complexities of realizing the biodiversity benefits that trophic rewilding offers under novel biosphere conditions in a heavily populated world.

Anticipating regime shifts by mixing early warning signals from different nodes

Real systems showing regime shifts, such as ecosystems, are often composed of many dynamical elements interacting on a network. Various early warning signals have been proposed for anticipating regime shifts from observed data. However, it is unclear how one should combine early warning signals from different nodes for better performance. Based on theory of stochastic differential equations, we propose a method to optimize the node set from which to construct an early warning signal. The proposed method takes into account that uncertainty as well as the magnitude of the signal affects its predictive performance, that a large magnitude or small uncertainty of the signal in one situation does not imply the signal's high performance, and that combining early warning signals from different nodes is often but not always beneficial. The method performs well particularly when different nodes are subjected to different amounts of dynamical noise and stress.

Targeted rainfall enhancement as an objective of forestation

Forestation efforts are accelerating across the globe in the fight against global climate change, in order to restore biodiversity, and to improve local livelihoods. Yet, so far the non-local effects of forestation on rainfall have largely remained a blind spot. Here we build upon emerging work to propose that targeted rainfall enhancement may also be considered in the prioritization of forestation. We show that the tools to achieve this are rapidly becoming available, but we also identify drawbacks and discuss which further developments are still needed to realize robust assessments of the rainfall effects of forestation in the face of climate change. Forestation programs may then mitigate not only global climate change itself but also its adverse effects in the form of drying.

Estimating the spatial amplification of damage caused by degradation in the Amazon

The Amazon rainforests have been undergoing unprecedented levels of human-induced disturbances. In addition to local impacts, such changes are likely to cascade following the eastern-western atmospheric flow generated by trade winds. We propose a model of spatial and temporal interactions created by this flow to estimate the spread of effects from local disturbances to downwind locations along atmospheric trajectories. The spatial component captures cascading effects propagated by neighboring regions, while the temporal component captures the persistence of local disturbances. Importantly, all these network effects can be described by a single matrix, acting as a spatial multiplier that amplifies local forest disturbances. This matrix holds practical implications for policymakers as they can use it to easily map where the damage of an initial forest disturbance is amplified and propagated to. We identify regions that are likely to cause the largest impact throughout the basin and those that are the most vulnerable to shocks caused by remote deforestation. On average, the presence of cascading effects mediated by winds in the Amazon doubles the impact of an initial damage. However, there is heterogeneity in this impact. While damage in some regions does not propagate, in others, amplification can reach 250%. Since we only account for spillovers mediated by winds, our multiplier of 2 should be seen as a lower bound.

Early warnings for multi-stage transitions in dynamics on networks

Successfully anticipating sudden major changes in complex systems is a practical concern. Such complex systems often form a heterogeneous network, which may show multi-stage transitions in which some nodes experience a regime shift earlier than others as an environment gradually changes. Here we investigate early warning signals for networked systems undergoing a multi-stage transition. We found that knowledge of both the ongoing multi-stage transition and network structure enables us to calculate effective early warning signals for multi-stage transitions. Furthermore, we found that small subsets of nodes could anticipate transitions as well as or even better than using all the nodes. Even if we fix the network and dynamical system, no single best subset of nodes provides good early warning signals, and a good choice of sentinel nodes depends on the tipping direction and the current stage of the dynamics within a multi-stage transition, which we systematically characterize.

Tropical deforestation causes large reductions in observed precipitation

Tropical forests play a critical role in the hydrological cycle and can influence local and regional precipitation1. Previous work has assessed the impacts of tropical deforestation on precipitation, but these efforts have been largely limited to case studies2. A wider analysis of interactions between deforestation and precipitation-and especially how any such interactions might vary across spatial scales-is lacking. Here we show reduced precipitation over deforested regions across the tropics. Our results arise from a pan-tropical assessment of the impacts of 2003-2017 forest loss on precipitation using satellite, station-based and reanalysis datasets. The effect of deforestation on precipitation increased at larger scales, with satellite datasets showing that forest loss caused robust reductions in precipitation at scales greater than 50 km. The greatest declines in precipitation occurred at 200 km, the largest scale we explored, for which 1 percentage point of forest loss reduced precipitation by 0.25 ± 0.1 mm per month. Reanalysis and station-based products disagree on the direction of precipitation responses to forest loss, which we attribute to sparse in situ tropical measurements. We estimate that future deforestation in the Congo will reduce local precipitation by 8-10% in 2100. Our findings provide a compelling argument for tropical forest conservation to support regional climate resilience.

Network motifs shape distinct functioning of Earth's moisture recycling hubs

Earth's hydrological cycle critically depends on the atmospheric moisture flows connecting evaporation to precipitation. Here we convert a decade of reanalysis-based moisture simulations into a high-resolution global directed network of spatial moisture provisions. We reveal global and local network structures that offer a new view of the global hydrological cycle. We identify four terrestrial moisture recycling hubs: the Amazon Basin, the Congo Rainforest, South Asia and the Indonesian Archipelago. Network motifs reveal contrasting functioning of these regions, where the Amazon strongly relies on directed connections (feed-forward loops) for moisture redistribution and the other hubs on reciprocal moisture connections (zero loops and neighboring loops). We conclude that Earth's moisture recycling hubs are characterized by specific topologies shaping heterogeneous effects of land-use changes and climatic warming on precipitation patterns.