A resilience sensing system for the biosphere

Early warning signs for tipping points in systems with non-Gaussian α-stable noise

Forecasting rapid, non-linear change or so-called tipping points is a major concern in ecology and environmental science. Statistical early warning signs, based on the theory of stochastic dynamical systems, are now regularly applied to observational data streams. However, the reliability of these early warning signs relies on a number of key mathematical assumptions, most notably the presence of Gaussian noise, while many ecological systems exhibit non-Gaussianity. We here show that for systems driven by non-Gaussian, α-stable noise, the classical early warning signs of rising variance and autocorrelation are not supported by mathematical theory, and their use poses the danger of spurious, false-positive results. To address this, we provide a generalized approach by introducing the scaling factor [Formula: see text] as an alternative early warning sign. We show that in the case of the linear Ornstein-Uhlenbeck process, there exists a direct inverse relationship between [Formula: see text] and the bifurcation parameter, telling us that [Formula: see text] will increase as we approach the bifurcation. Our numerical simulations confirm theoretical results and show that our findings generalize well to non-linear, non-equilibrium systems often employed in ecological systems. We thus provide a generalized, robust, and broadly applicable statistical early warning sign for systems driven by Gaussian and non-Gaussian α-stable noise.

Ecosystem condition emerges from an ecological equation of state applied to North American tree communities

Ecosystems represent the largest scales of biological organization and shape the ecology and evolution of genes, populations, species, and communities. Yet we lack an understanding of the key properties of ecosystems-the state variables-that must be tracked to predict changes in ecosystem condition through time,1,2,3 instead commonly relying on reference states.4,5 A recently published ecological equation of state demonstrated a strong relationship between biomass, species richness, organism abundance, and productivity, suggesting the untested possibility that this relationship may systematically vary under ecological disturbance (i.e., vary with ecosystem condition).6 To test this idea, we investigate how the performance of the ecological equation of state relates to expected ecosystem condition (derived from protected area data) using satellite-derived proxies for the forests of the conterminous USA. We found that, despite the noise introduced by the use of satellite-derived proxies, the performance of the ecological equation of state in predicting biomass varied systematically with expected ecosystem condition. Moreover, differences in equation performance could be used to identify areas with different expected ecosystem condition. This differential performance was stronger in the equation of state than in correlative models fit to the same data, though similar to patterns seen in the relationship between productivity and biomass. These findings suggest deeper underlying regularities linking ecosystem condition and state variables and the potential to break ecology's dependence on reference states. Further investigation of these relationships may reveal new principles of ecosystem dynamics, which are vital to informing global biodiversity conservation efforts.

Monitoring Terrestrial Ecosystem Resilience Using Earth Observation Data: Identifying Consensus and Limitations Across Metrics

Resilience is a key feature of ecosystem dynamics reflecting a system's ability to resist and recover from environmental perturbations. Slowing down in the rate of recovery has been used as an early-warning signal for abrupt transitions. Recent advances in Earth observation (EO) vegetation data provide the capability to capture broad-scale resilience patterns and identify regions experiencing resilience loss. However, the proliferation of methods for evaluating resilience using EO data has introduced significant uncertainty, leading to contradictory resilience estimates across approximately 73% of the Earth's land surface. To reconcile these perspectives, we review the range of methods and associated metrics that capture aspects of ecosystem resilience using EO data. Using a principal component analysis, we empirically test the relationships between the most widely used resilience metrics and explore emergent patterns within and among the world's biomes. Our analysis reveals that the 10 resilience metrics aggregate into four core components of ecosystem dynamics, highlighting the multidimensional nature of ecosystem resilience. We also find that ecosystems with slower recovery are more resistant to drought extremes. Furthermore, the relationships between resilience metrics vary across the world's biomes and vegetation types. These results illustrate the inherent differences in the dynamics of natural systems and highlight the need for careful consideration when evaluating broad-scale resilience patterns across biomes. Our findings provide valuable insights for identifying global resilience patterns, which are critically needed to inform policy decisions and guide conservation efforts globally.

Quantifying stability and resilience of eco-social keystone species complexes for coastal marine ecosystems of the Caribbean Sea and eastern Pacific: applications in conservation and monitoring programmes

The local stability and resilience of 13 eco-social keystone species complexes (eco-social KSCs)-considered as conservation and monitoring units-were quantified in coastal marine ecosystems located in the Caribbean and eastern Pacific. Based on Routh-Hurwitz's criterion and Levins' criteria, the eco-social KSCs corresponding to Islas Marietas National Park (Mexico) emerged as the most locally stable and resilient ecosystem. To the contrary, the eco-social KSCs determined for Guala Guala Bay (Chile) and Xcalak Reef National Park (Caribbean) were the least stable and resilient, respectively. In terms of sensitivity, the eco-social KSCs corresponding to El Cobre Bay (Chile) presented the greatest number of sensitive components. The ecological section of the KSCs is formed by a tri-trophic network, dominating self-negative feedbacks. In the case of the socio-economic section, the fisher could exhibit the three types of self-feedbacks, and instead, the demand should be controlled. The identification of eco-social KSCs and the quantification of their stabilities and resiliences allow us to approach ecosystem-based fisheries management under a climate change context. Therefore, we suggest assessing and monitoring the persistence of the eco-social KSCs herein analysed over time, as a way to conserve the fundamental network structure of these ecosystems intervened by fishing.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Remotely sensing potential climate change tipping points across scales

Potential climate tipping points pose a growing risk for societies, and policy is calling for improved anticipation of them. Satellite remote sensing can play a unique role in identifying and anticipating tipping phenomena across scales. Where satellite records are too short for temporal early warning of tipping points, complementary spatial indicators can leverage the exceptional spatial-temporal coverage of remotely sensed data to detect changing resilience of vulnerable systems. Combining Earth observation with Earth system models can improve process-based understanding of tipping points, their interactions, and potential tipping cascades. Such fine-resolution sensing can support climate tipping point risk management across scales.

Ecological complexity and the biosphere: the next 30 years

Global warming, habitat loss and overexploitation of limited resources are leading to alarming biodiversity declines. Ecosystems are complex adaptive systems that display multiple alternative states and can shift from one to another in abrupt ways. Some of these tipping points have been identified and predicted by mathematical and computational models. Moreover, multiple scales are involved and potential mitigation or intervention scenarios are tied to particular levels of complexity, from cells to human–environment coupled systems. In dealing with a biosphere where humans are part of a complex, endangered ecological network, novel theoretical and engineering approaches need to be considered. At the centre of most research efforts is biodiversity, which is essential to maintain community resilience and ecosystem services. What can be done to mitigate, counterbalance or prevent tipping points? Using a 30-year window, we explore recent approaches to sense, preserve and restore ecosystem resilience as well as a number of proposed interventions (from afforestation to bioengineering) directed to mitigate or reverse ecosystem collapse. The year 2050 is taken as a representative future horizon that combines a time scale where deep ecological changes will occur and proposed solutions might be effective.

This article is part of the theme issue ‘Ecological complexity and the biosphere: the next 30 years’.