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Lenton TM, Buxton JE, Armstrong McKay DI, Abrams JF, Boulton CA, Lees K, Powell TWR, Boers N, Cunliffe AM, Dakos V. A resilience sensing system for the biosphere. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210383. [PMID: 35757883 PMCID: PMC9234808 DOI: 10.1098/rstb.2021.0383] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/28/2022] [Indexed: 12/14/2022] Open
Abstract
We are in a climate and ecological emergency, where climate change and direct anthropogenic interference with the biosphere are risking abrupt and/or irreversible changes that threaten our life-support systems. Efforts are underway to increase the resilience of some ecosystems that are under threat, yet collective awareness and action are modest at best. Here, we highlight the potential for a biosphere resilience sensing system to make it easier to see where things are going wrong, and to see whether deliberate efforts to make things better are working. We focus on global resilience sensing of the terrestrial biosphere at high spatial and temporal resolution through satellite remote sensing, utilizing the generic mathematical behaviour of complex systems-loss of resilience corresponds to slower recovery from perturbations, gain of resilience equates to faster recovery. We consider what subset of biosphere resilience remote sensing can monitor, critically reviewing existing studies. Then we present illustrative, global results for vegetation resilience and trends in resilience over the last 20 years, from both satellite data and model simulations. We close by discussing how resilience sensing nested across global, biome-ecoregion, and local ecosystem scales could aid management and governance at these different scales, and identify priorities for further work. This article is part of the theme issue 'Ecological complexity and the biosphere: the next 30 years'.
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Affiliation(s)
| | - Joshua E. Buxton
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
| | - David I. Armstrong McKay
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
- Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
| | - Jesse F. Abrams
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
- Institute for Data Science and Artificial Intelligence, University of Exeter, Exeter EX4 4QF, UK
| | - Chris A. Boulton
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
| | - Kirsten Lees
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
- Environmental Sustainability Research Centre, University of Derby, Derby DE22 1GB, UK
| | | | - Niklas Boers
- Global Systems Institute, University of Exeter, Exeter EX4 4QE, UK
- School of Engineering and Design, Earth System Modelling, Technical University of Munich, Munich, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | | | - Vasilis Dakos
- ISEM, University of Montpellier, CNRS, EPHE, IRD, Montpellier, France
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Abstract
Studying ecosystem dynamics is critical to monitoring and managing linked systems of humans and nature. Due to the growth of tools and techniques for collecting data, information on the condition of these systems is more widely available. While there are a variety of approaches for mining and assessing data, there is a need for methods to detect latent characteristics in ecosystems linked to temporal and spatial patterns of change. Resilience-based approaches have been effective at not only identifying environmental change but also providing warning in advance of critical transitions in social-ecological systems (SES). In this study, we examine the usefulness of one such method, Fisher Information (FI) for spatiotemporal analysis. FI is used to assess patterns in data and has been established as an effective tool for capturing complex system dynamics to include regimes and regime shifts. We employed FI to assess the biophysical condition of eighty-five Swedish lakes from 1996–2018. Results showed that FI captured spatiotemporal changes in the Swedish lakes and identified distinct spatial patterns above and below the Limes Norrlandicus, a hard ecotone boundary which separates northern and southern ecoregions in Sweden. Further, it revealed that spatial variance changed approaching this boundary. Our results demonstrate the utility of this resilience-based approach for spatiotemporal and spatial regimes analyses linked to monitoring and managing critical watersheds and waterbodies impacted by accelerating environmental change.
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Abstract
Tipping points exist in social, ecological and climate systems and those systems are increasingly causally intertwined in the Anthropocene. Climate change and biosphere degradation have advanced to the point where we are already triggering damaging environmental tipping points, and to avoid worse ones ahead will require finding and triggering positive tipping points towards sustainability in coupled social, ecological and technological systems. To help with that I outline how tipping points can occur in continuous dynamical systems and in networks, the causal interactions that can occur between tipping events across different types and scales of system-including the conditions required to trigger tipping cascades, the potential for early warning signals of tipping points, and how they could inform deliberate tipping of positive change. In particular, the same methods that can provide early warning of damaging environmental tipping points can be used to detect when a socio-technical or socio-ecological system is most sensitive to being deliberately tipped in a desirable direction. I provide some example targets for such deliberate tipping of positive change. This article is part of the theme issue 'Climate change and ecosystems: threats, opportunities and solutions'.
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Affiliation(s)
- Timothy M. Lenton
- Global Systems Institute, University of Exeter, Laver Building (Level 8), North Park Road, Exeter EX4 4QE, UK
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Nijp JJ, Temme AJ, van Voorn GA, Kooistra L, Hengeveld GM, Soons MB, Teuling AJ, Wallinga J. Spatial early warning signals for impending regime shifts: A practical framework for application in real-world landscapes. GLOBAL CHANGE BIOLOGY 2019; 25:1905-1921. [PMID: 30761695 PMCID: PMC6849843 DOI: 10.1111/gcb.14591] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 01/31/2019] [Indexed: 06/09/2023]
Abstract
Prediction of ecosystem response to global environmental change is a pressing scientific challenge of major societal relevance. Many ecosystems display nonlinear responses to environmental change, and may even undergo practically irreversible 'regime shifts' that initiate ecosystem collapse. Recently, early warning signals based on spatiotemporal metrics have been proposed for the identification of impending regime shifts. The rapidly increasing availability of remotely sensed data provides excellent opportunities to apply such model-based spatial early warning signals in the real world, to assess ecosystem resilience and identify impending regime shifts induced by global change. Such information would allow land-managers and policy makers to interfere and avoid catastrophic shifts, but also to induce regime shifts that move ecosystems to a desired state. Here, we show that the application of spatial early warning signals in real-world landscapes presents unique and unexpected challenges, and may result in misleading conclusions when employed without careful consideration of the spatial data and processes at hand. We identify key practical and theoretical issues and provide guidelines for applying spatial early warning signals in heterogeneous, real-world landscapes based on literature review and examples from real-world data. Major identified issues include (1) spatial heterogeneity in real-world landscapes may enhance reversibility of regime shifts and boost landscape-level resilience to environmental change (2) ecosystem states are often difficult to define, while these definitions have great impact on spatial early warning signals and (3) spatial environmental variability and socio-economic factors may affect spatial patterns, spatial early warning signals and associated regime shift predictions. We propose a novel framework, shifting from an ecosystem perspective towards a landscape approach. The framework can be used to identify conditions under which resilience assessment with spatial remotely sensed data may be successful, to support well-informed application of spatial early warning signals, and to improve predictions of ecosystem responses to global environmental change.
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Affiliation(s)
- Jelmer J. Nijp
- Soil Geography and Landscape group, Department of Environmental SciencesWageningen University & ResearchWageningenthe Netherlands
- KWR Watercycle Research Institute, Ecohydrology GroupNieuwegeinthe Netherlands
| | - Arnaud J.A.M. Temme
- Soil Geography and Landscape group, Department of Environmental SciencesWageningen University & ResearchWageningenthe Netherlands
- Geography DepartmentKansas State UniversityManhattanKansas
| | | | - Lammert Kooistra
- Laboratory of Geo‐information Science and Remote Sensing, Department of Environmental SciencesWageningen University & ResearchWageningenthe Netherlands
| | | | - Merel B. Soons
- Ecology and Biodiversity group, Institute of Environmental Biology, Biology DepartmentUtrecht UniversityUtrechtthe Netherlands
| | - Adriaan J. Teuling
- Hydrology and Quantitative Water Management Group, Department of Environmental SciencesWageningen University & ResearchWageningenthe Netherlands
| | - Jakob Wallinga
- Soil Geography and Landscape group, Department of Environmental SciencesWageningen University & ResearchWageningenthe Netherlands
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Eason T, Chuang WC, Sundstrom S, Cabezas H. An information theory-based approach to assessing spatial patterns in complex systems. ENTROPY (BASEL, SWITZERLAND) 2019; 21:182. [PMID: 31402835 PMCID: PMC6688651 DOI: 10.3390/e21020182] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 02/14/2019] [Indexed: 01/14/2023]
Abstract
Given the intensity and frequency of environmental change, the linked and cross-scale nature of social-ecological systems, and the proliferation of big data, methods that can help synthesize complex system behavior over a geographical area are of great value. Fisher information evaluates order in data and has been established as a robust and effective tool for capturing changes in system dynamics, including the detection of regimes and regime shifts. Methods developed to compute Fisher information can accommodate multivariate data of various types and requires no a priori decisions about system drivers, making it a unique and powerful tool. However, the approach has primarily been used to evaluate temporal patterns. In its sole application to spatial data, Fisher information successfully detected regimes in terrestrial and aquatic systems over transects. Although the selection of adjacently positioned sampling stations provided a natural means of ordering the data, such an approach limits the types of questions that can be answered in a spatial context. Here, we expand the approach to develop a method for more fully capturing spatial dynamics. Results reflect changes in the index that correspond with geographical patterns and demonstrate the utility of the method in uncovering hidden spatial trends in complex systems.
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Affiliation(s)
- Tarsha Eason
- National Risk Management Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Wen-Ching Chuang
- National Research Council, U.S. Environmental Protection Agency, 26 W. Martin Luther King Drive, Cincinnati, OH 45268, USA
| | - Shana Sundstrom
- School of Natural Resources, University of Nebraska-Lincoln, 103 Hardin Hall, 3310 Holdrege St., Lincoln, NE 68583, USA
| | - Heriberto Cabezas
- National Risk Management Research Laboratory, U.S. Environmental Protection Agency, Cincinnati, OH 45268, USA
- Institute for Process Systems Engineering and Sustainability, Pazmany Peter Catholic University, Szentkiralyi utca 28, H-1088 Budapest, Hungary
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Clements CF, Ozgul A. Indicators of transitions in biological systems. Ecol Lett 2018; 21:905-919. [PMID: 29601665 DOI: 10.1111/ele.12948] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 11/22/2017] [Accepted: 02/22/2018] [Indexed: 12/13/2022]
Abstract
In the face of global biodiversity declines, predicting the fate of biological systems is a key goal in ecology. One popular approach is the search for early warning signals (EWSs) based on alternative stable states theory. In this review, we cover the theory behind nonlinearity in dynamic systems and techniques to detect the loss of resilience that can indicate state transitions. We describe the research done on generic abundance-based signals of instability that are derived from the phenomenon of critical slowing down, which represent the genesis of EWSs research. We highlight some of the issues facing the detection of such signals in biological systems - which are inherently complex and show low signal-to-noise ratios. We then document research on alternative signals of instability, including measuring shifts in spatial autocorrelation and trait dynamics, and discuss potential future directions for EWSs research based on detailed demographic and phenotypic data. We set EWSs research in the greater field of predictive ecology and weigh up the costs and benefits of simplicity vs. complexity in predictive models, and how the available data should steer the development of future methods. Finally, we identify some key unanswered questions that, if solved, could improve the applicability of these methods.
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Affiliation(s)
- Christopher F Clements
- School of Biosciences, The University of Melbourne, Parkville, Vic., 3010, Australia.,Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, 8057, Switzerland
| | - Arpat Ozgul
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, 8057, Switzerland
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Wen H, Ciamarra MP, Cheong SA. How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs. PLoS One 2018; 13:e0191439. [PMID: 29538373 PMCID: PMC5851542 DOI: 10.1371/journal.pone.0191439] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 01/04/2018] [Indexed: 11/18/2022] Open
Abstract
There is growing interest in the use of critical slowing down and critical fluctuations as early warning signals for critical transitions in different complex systems. However, while some studies found them effective, others found the opposite. In this paper, we investigated why this might be so, by testing three commonly used indicators: lag-1 autocorrelation, variance, and low-frequency power spectrum at anticipating critical transitions in the very-high-frequency time series data of the Australian Dollar-Japanese Yen and Swiss Franc-Japanese Yen exchange rates. Besides testing rising trends in these indicators at a strict level of confidence using the Kendall-tau test, we also required statistically significant early warning signals to be concurrent in the three indicators, which must rise to appreciable values. We then found for our data set the optimum parameters for discovering critical transitions, and showed that the set of critical transitions found is generally insensitive to variations in the parameters. Suspecting that negative results in the literature are the results of low data frequencies, we created time series with time intervals over three orders of magnitude from the raw data, and tested them for early warning signals. Early warning signals can be reliably found only if the time interval of the data is shorter than the time scale of critical transitions in our complex system of interest. Finally, we compared the set of time windows with statistically significant early warning signals with the set of time windows followed by large movements, to conclude that the early warning signals indeed provide reliable information on impending critical transitions. This reliability becomes more compelling statistically the more events we test.
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Affiliation(s)
- Haoyu Wen
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
- Complexity Institute, Nanyang Technological University, Singapore, Singapore
| | - Massimo Pica Ciamarra
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
- Complexity Institute, Nanyang Technological University, Singapore, Singapore
| | - Siew Ann Cheong
- Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
- Complexity Institute, Nanyang Technological University, Singapore, Singapore
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