scholarly journals Remotely-sensed slowing down in spatially patterned dryland ecosystems

2021 ◽  
Author(s):  
Michiel P Veldhuis ◽  
Ricardo Martinez-Garcia ◽  
Vincent Deblauwe ◽  
Vasilis Dakos
Keyword(s):  
Configuration Model ◽  
Vegetation Patterns ◽  
Rainfall Gradient ◽  
Slowing Down ◽  
In Situ Data ◽  
Critical Transitions ◽  
Non Linear ◽  
Dryland Ecosystems ◽  
Resilience Indicators ◽  
Future Work

Regular vegetation patterns have been predicted to indicate a system slowing down and possibly desertification of drylands. However, these predictions have not yet been observed in dryland vegetation due to the inherent logistic difficulty to gather longer-term in situ data. Here, we use recently developed methods using remote-sensing EVI time-series in combination with classified regular vegetation patterns along a rainfall gradient in Sudan to test these predictions. Overall, three temporal indicators (responsiveness, temporal autocorrelation, variance) show slowing down as vegetation patterns change from gaps to labyrinths to spots towards more arid conditions, confirming predictions. However, this transition exhibits non-linearities, specifically when patterns change configuration. Model simulations reveal that the transition between patterns temporarily slows down the system affecting the temporal indicators. These transient states when vegetation patterns reorganize thus affect the systems resilience indicators in a non-linear way. Our findings suggest that spatial self-organization of dryland vegetation is associated with critical slowing down, but this transition towards reduced resilience happens in a non-linear way. Future work should aim to better understand transient dynamics in regular vegetation patterns in dryland ecosystems, because long transients make regular vegetation patterns of limited use for management in anticipating critical transitions.

scholarly journals Resilience indicators: prospects and limitations for early warnings of regime shifts

2015 ◽  
Vol 370 (1659) ◽  
pp. 20130263 ◽  
Author(s):  
Vasilis Dakos ◽  
Stephen R. Carpenter ◽  
Egbert H. van Nes ◽  
Marten Scheffer
Keyword(s):  
Early Warning ◽  
Temporal Correlation ◽  
Warning Signal ◽  
Regime Shifts ◽  
Tipping Points ◽  
Small Perturbations ◽  
Slowing Down ◽  
Critical Transitions ◽  
Wide Range ◽  
Resilience Indicators

In the vicinity of tipping points—or more precisely bifurcation points—ecosystems recover slowly from small perturbations. Such slowness may be interpreted as a sign of low resilience in the sense that the ecosystem could easily be tipped through a critical transition into a contrasting state. Indicators of this phenomenon of ‘critical slowing down (CSD)’ include a rise in temporal correlation and variance. Such indicators of CSD can provide an early warning signal of a nearby tipping point. Or, they may offer a possibility to rank reefs, lakes or other ecosystems according to their resilience. The fact that CSD may happen across a wide range of complex ecosystems close to tipping points implies a powerful generality. However, indicators of CSD are not manifested in all cases where regime shifts occur. This is because not all regime shifts are associated with tipping points. Here, we review the exploding literature about this issue to provide guidance on what to expect and what not to expect when it comes to the CSD-based early warning signals for critical transitions.

scholarly journals No warning for slow transitions

2021 ◽  
Vol 18 (176) ◽  
Author(s):  
Bregje van der Bolt ◽  
Egbert H. van Nes ◽  
Marten Scheffer
Keyword(s):  
Time Series ◽  
Response Rate ◽  
Warning Signal ◽  
Response Rates ◽  
Tipping Point ◽  
Climate Forcing ◽  
Slowing Down ◽  
Critical Transitions ◽  
Resilience Indicators ◽  
Different Response

A rise in fragility as a system approaches a tipping point may be sometimes estimated using dynamical indicators of resilience (DIORs) that measure the characteristic slowing down of recovery rates before a tipping point. A change in DIORs could be interpreted as an early warning signal for an upcoming critical transition. However, in order to be able to estimate the DIORs, observational records need to be long enough to capture the response rate of the system. As we show here, the required length of the time series depends on the response rates of the system. For instance, the current rate of anthropogenic climate forcing is fast relative to the response rate of some parts of the climate system. Therefore, we may expect difficulties estimating the resilience from modern time series. So far, there have been no systematic studies of the effects of the response rates of the dynamical systems and the rates of forcing on the detectability trends in the DIORs prior to critical transitions. Here, we quantify the performance of the resilience indicators variance and temporal autocorrelation, in systems with different response rates and for different rates of forcing. Our results show that the rapid rise of anthropogenic forcing to the Earth may make it difficult to detect changes in the resilience of ecosystems and climate elements from time series. These findings suggest that in order to determine with models whether the use of the DIORs is appropriate, we need to use realistic models that incorporate the key processes with the appropriate time constants.

scholarly journals Evaluating the performance of multivariate indicators of resilience loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Els Weinans ◽  
Rick Quax ◽  
Egbert H. van Nes ◽  
Ingrid A. van de Leemput
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Multivariate Time Series ◽  
Tipping Points ◽  
Physical And Mental Health ◽  
One Dimensional ◽  
Slowing Down ◽  
The Past ◽  
Technological Developments ◽  
Resilience Indicators

AbstractVarious complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

Ghost Cats and the Specter of Self

2018 ◽  
pp. 75-94
Author(s):  
Sarah Hildebrand
Keyword(s):  
Bearing Witness ◽  
Slowing Down ◽  
Non Linear ◽  
Everyday Living

This chapter uses trauma as a point of access into Gabrielle Bell’s three most recent works—The Voyeurs (2012), Truth is Fragmentary (2014), and Everything is Flammable (2017). Highlighting how Bell develops a traumatic narrative by fragmenting it across these three texts, it analyzes the role speed plays in the ways we tell and receive traumatic accounts. By slowing down her own telling to the point of decentralizing trauma, Bell posits suffering not as spectacle, but as part of everyday living. While trauma consistently resurfaces throughout her oeuvre, its fractured and non linear format makes it easy to overlook. Building on Lee Gilmore's recent theorizing of female testimony and witness, this chapter traces the network of trauma that threads these books to suggest that ethically bearing witness often means slowing down.

Slowing down critical transitions via Gaussian white noise and periodic force

2019 ◽  
Vol 62 (12) ◽  
pp. 2144-2152 ◽  
Author(s):  
JinZhong Ma ◽  
Yong Xu ◽  
Wei Xu ◽  
YongGe Li ◽  
Jürgen Kurths
Keyword(s):  
White Noise ◽  
Gaussian White Noise ◽  
Periodic Force ◽  
Slowing Down ◽  
Critical Transitions

scholarly journals An ecohydrological approach to predicting hillslope-scale vegetation patterns in dryland ecosystems

2012 ◽  
Vol 48 (1) ◽  
Author(s):  
Trenton E. Franz ◽  
Kelly K. Caylor ◽  
Elizabeth G. King ◽  
Jan M. Nordbotten ◽  
Michael A. Celia ◽  
...  
Keyword(s):  
Vegetation Patterns ◽  
Dryland Ecosystems ◽  
Hillslope Scale

scholarly journals Transient indicators of tipping points in infectious diseases

2020 ◽  
Vol 17 (170) ◽  
pp. 20200094
Author(s):  
Suzanne M. O’Regan ◽  
Eamon B. O’Dea ◽  
Pejman Rohani ◽  
John M. Drake
Keyword(s):  
Infectious Diseases ◽  
Vaccine Uptake ◽  
Critical Slowing Down ◽  
Critical Threshold ◽  
Continuous Systems ◽  
Dominant Eigenvalue ◽  
Tipping Points ◽  
Critical Transition ◽  
Slowing Down ◽  
Critical Transitions

The majority of known early warning indicators of critical transitions rely on asymptotic resilience and critical slowing down. In continuous systems, critical slowing down is mathematically described by a decrease in magnitude of the dominant eigenvalue of the Jacobian matrix on the approach to a critical transition. Here, we show that measures of transient dynamics, specifically, reactivity and the maximum of the amplification envelope, also change systematically as a bifurcation is approached in an important class of models for epidemics of infectious diseases. Furthermore, we introduce indicators designed to detect trends in these measures and find that they reliably classify time series of case notifications simulated from stochastic models according to levels of vaccine uptake. Greater attention should be focused on the potential for systems to exhibit transient amplification of perturbations as a critical threshold is approached, and should be considered when searching for generic leading indicators of tipping points. Awareness of this phenomenon will enrich understanding of the dynamics of complex systems on the verge of a critical transition.

Variability in decision-making speed and quality against exploiting and exploitative opponents as a function of trial outcome and credit interruption

2020 ◽  
Author(s):  
Ben Dyson
Keyword(s):  
Decision Making ◽  
Perceived Control ◽  
Credit System ◽  
Successful Performance ◽  
Slowing Down ◽  
Long Run ◽  
Zero Sum ◽  
Response Interruption ◽  
Future Work

A presumption in previous work has been that sub-optimality in competitive performance following loss is the result of a reduction in decision-making time (i.e., post-error speeding). Decision-making time can also be modulated via the use of a credit system, where sufficient credit must be present for the participant to continue playing. Across three experiments, the speed and quality of competitive decision-making was examined in a zero-sum game as a function of the nature of the opponent (unexploitable, Experiment 1; exploiting, Experiment 2; exploitable, Experiment 3) and the nature of the credit system (no credit, fixed credit, variable credit). The data a) identify the use of a variable credit system as enhancing the perceived control participants have against exploitable opponents, b) reinforce the inflexibility of lose-shift as a decision-making heuristic in competitive contexts, and, c) confirm that self-imposed reductions in processing time following losses (post-error speeding) are causal factors in determining poorer-quality behaviour. Since slowing down decision-making following loss increases the likelihood of future successful performance, future work should seek not only to disentangle the two features of any putative credit system (temporal lag and response interruption), but also explore the possibility that mandatory pauses introduce slower cycles of performance and hence could improve the quality of competitive decision-making in the long run.

scholarly journals Multistability of model and real dryland ecosystems through spatial self-organization

2018 ◽  
Vol 115 (44) ◽  
pp. 11256-11261 ◽  
Author(s):  
Robbin Bastiaansen ◽  
Olfa Jaïbi ◽  
Vincent Deblauwe ◽  
Maarten B. Eppinga ◽  
Koen Siteur ◽  
...  
Keyword(s):  
Early Warning Systems ◽  
Aerial Photographs ◽  
Self Organization ◽  
Optical Data ◽  
Small Scale ◽  
Vegetation Patterns ◽  
Total Biomass ◽  
Contrast Model ◽  
Model Predictions ◽  
Dryland Ecosystems

Spatial self-organization of dryland vegetation constitutes one of the most promising indicators for an ecosystem’s proximity to desertification. This insight is based on studies of reaction–diffusion models that reproduce visual characteristics of vegetation patterns observed on aerial photographs. However, until now, the development of reliable early warning systems has been hampered by the lack of more in-depth comparisons between model predictions and real ecosystem patterns. In this paper, we combined topographical data, (remotely sensed) optical data, and in situ biomass measurements from two sites in Somalia to generate a multilevel description of dryland vegetation patterns. We performed an in-depth comparison between these observed vegetation pattern characteristics and predictions made by the extended-Klausmeier model for dryland vegetation patterning. Consistent with model predictions, we found that for a given topography, there is multistability of ecosystem states with different pattern wavenumbers. Furthermore, observations corroborated model predictions regarding the relationships between pattern wavenumber, total biomass, and maximum biomass. In contrast, model predictions regarding the role of slope angles were not corroborated by the empirical data, suggesting that inclusion of small-scale topographical heterogeneity is a promising avenue for future model development. Our findings suggest that patterned dryland ecosystems may be more resilient to environmental change than previously anticipated, but this enhanced resilience crucially depends on the adaptive capacity of vegetation patterns.

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