A resilience sensing system for the biosphere

Lenton, Timothy M.; Buxton, Joshua; McKay, David I. Armstrong; Abrams, Jesse F.; Boulton, Chris A.; Lees, Kirsten; Powell, Thomas W.; Boers, Niklas; Cunliffe, Andrew M.; Dakos, Vasilis

doi:10.1098/rstb.2021.0383

Cited by 11 publications

(20 citation statements)

References 108 publications

(193 reference statements)

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“…With the literature on critical transitions and EWS growing, it is important to understand the overall context of some of these concepts and their impact in combating daunting issues such as climate change and socioecological systems that involve challenges present at different spatial and organisational scales [199]. These issues need immediate attention in terms of the development of estimation of risk and mitigation strategies.…”

Section: Outlook and Perspectivementioning

confidence: 99%

Early warning signals for critical transitions in complex systems

2023

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In this topical review, we present a brief overview of the different methods and measures to detect the occurrence of critical transitions in complex systems. We start by introducing the mechanisms that trigger critical transitions, and how they relate to early warning signals (EWS) and briefly mention the conventional measures based on critical slowing down, as computed from data and applied to real systems. We then present in detail the approaches for multivariate data, including those defined for complex networks. More recent techniques like the warning signals derived from the recurrence pattern underlying the data, are presented in detail as measures from recurrence plots and recurrence networks. This is followed by a discussion on how methods based on machine learning are used most recently, to detect critical transitionsin real and simulated data. Towards the end, we summarise the challenges involved while computing the EWS from real-world data and conclude with our outlook and perspective on future trends in this area.

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Section: Outlook and Perspectivementioning

confidence: 99%

Early warning signals for critical transitions in complex systems

2023

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“…Large-scale modelling of the challenges associated with the biodiversity crisis requires sensing global, spatio-temporal complexity across scales [ 68 ], using meta community approaches [ 69 , 70 ], incorporating humans into ecology [ 71 ] and adopting a network perspective [ 72 , 73 ]. Concerning the first, our perception of the state of the planet has much improved over the last 30 years as remote sensing methods and machine learning-based data analysis have been developed.…”

Section: Resilience Network and Tipping Pointsmentioning

confidence: 99%

“…Using as a working definition of resilience, namely the capacity of a system to recover from perturbations, Lenton et al . [ 68 ] have suggested a practical implementation. This idea stems from the dynamical correlations displayed by the behaviour of a system after perturbation.…”

Section: Resilience Network and Tipping Pointsmentioning

confidence: 99%

“…Since ecosystem degradation or even collapse is connected to loss of resilience, an ambitious goal would be to define a reliable sensing method that provides a measurable index [77,78]. Using as a working definition of resilience, namely the capacity of a system to recover from perturbations, Lenton et al [68] have suggested a practical implementation. This idea stems from the dynamical correlations displayed by the behaviour of a system after perturbation.…”

Section: Resilience Network and Tipping Pointsmentioning

confidence: 99%

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Ecological complexity and the biosphere: the next 30 years

Solé

Levin

2022

Phil. Trans. R. Soc. B

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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’.

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“…If a forest is resilient and quickly recovers, the autocorrelation is lower compared to a higher autocorrelation of a more vulnerable forest that slowly recovers. Increased autocorrelation in tropical rainforests worldwide has been found using different remotely-sensed vegetation proxies such as above-ground biomass (accounting for water stress, deforestation and vapor-pressure deficit, [14]), vegetation optical-depth ( [7] only Amazon basin, and [30] globally), normalized difference vegetation index (NDVI) [31] and kernel NDVI [32].…”

Section: Introductionmentioning

confidence: 99%

Measuring tropical rainforest resilience under non-Gaussian disturbances

Benson,

Donges,

Boers

et al. 2024

Environ. Res. Lett.

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The Amazon rainforest is considered one of the Earth's tipping elements and may lose stability under ongoing climate change. Recently a decrease in tropical rainforest resilience has been identified globally from remotely sensed vegetation data. However, the underlying theory assumes a Gaussian distribution of forest disturbances, which is different from most observed forest stressors such as fires, deforestation, or windthrow. Those stressors often occur in power-law-like distributions and can be approximated by α-stable Lévy noise. Here, we show that classical critical slowing down indicators to measure changes in forest resilience are robust under such power-law disturbances. To assess the robustness of critical slowing down indicators, we simulate pulse-like perturbations in an adapted and conceptual model of a tropical rainforest. We find few missed early warnings and few false alarms are achievable simultaneously if the following steps are carried out carefully: First, the model must be known to resolve the timescales of the perturbation. Second, perturbations need to be filtered according to their absolute temporal autocorrelation. Third, critical slowing down has to be assessed using the non-parametric Kendall-τ slope. These prerequisites allow for an increase in the sensitivity of early warning signals. Hence, our findings imply improved reliability of the interpretation of empirically estimated rainforest resilience through critical slowing down indicators.

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A resilience sensing system for the biosphere

Cited by 11 publications

References 108 publications

Early warning signals for critical transitions in complex systems

Early warning signals for critical transitions in complex systems

Ecological complexity and the biosphere: the next 30 years

Measuring tropical rainforest resilience under non-Gaussian disturbances

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