scholarly journals Cascading regime shifts within and across scales

Science ◽  
2018 ◽  
Vol 362 (6421) ◽  
pp. 1379-1383 ◽  
Author(s):  
Juan C. Rocha ◽  
Garry Peterson ◽  
Örjan Bodin ◽  
Simon Levin
Keyword(s):  
Regime Shift ◽  
Regime Shifts ◽  
Control Case ◽  
Scale Interactions ◽  
Cascading Effects ◽  
Critical Transitions ◽  
Structural Interdependence

Regime shifts are large, abrupt, and persistent critical transitions in the function and structure of ecosystems. Yet, it is unknown how these transitions will interact, whether the occurrence of one will increase the likelihood of another or simply correlate at distant places. We explored two types of cascading effects: Domino effects create one-way dependencies, whereas hidden feedbacks produce two-way interactions. We compare them with the control case of driver sharing, which can induce correlations. Using 30 regime shifts described as networks, we show that 45% of regime shift pairwise combinations present at least one plausible structural interdependence. The likelihood of cascading effects depends on cross-scale interactions but differs for each type. Management of regime shifts should account for potential connections.

scholarly journals Cascading regime shifts within and across scales

2018 ◽  
Author(s):  
Juan C. Rocha ◽  
Garry Peterson ◽  
Örjan Bodin ◽  
Simon A. Levin
Keyword(s):  
Data Collection ◽  
Regime Shifts ◽  
Aquatic Systems ◽  
Tipping Points ◽  
Control Case ◽  
Cascading Effects ◽  
Cascading Effect ◽  
Earth Systems ◽  
Critical Transitions ◽  
Structural Interdependence

AbstractRegime shifts are large, abrupt and persistent critical transitions in the function and structure of systems (1, 2). Yet it is largely unknown how these transitions will interact, whether the occurrence of one will increase the likelihood of another, or simply correlate at distant places. Here we explore two types of cascading effects: domino effects create one-way dependencies, while hidden feedbacks produce two-way interactions; and compare them with the control case of driver sharing which can induce correlations. Using 30 regime shifts described as networks, we show that 45% of the pair-wise combinations of regime shifts present at least one plausible structural interdependence. Driver sharing is more common in aquatic systems, while hidden feedbacks are more commonly found in terrestrial and Earth systems tipping points. The likelihood of cascading effects depends on cross-scale interactions, but differs for each cascading effect type. Regime shifts should not be studied in isolation: instead, methods and data collection should account for potential teleconnections.

scholarly journals Approximating the Cost-of-Living Index for a Storable Good

2018 ◽  
Vol 10 (2) ◽  
pp. 286-314 ◽  
Author(s):  
Matthew Osborne
Keyword(s):  
Regime Shift ◽  
Structural Model ◽  
Regime Shifts ◽  
Cost Of Living ◽  
Product Categories ◽  
Dynamic Structural Model ◽  
Fixed Base ◽  
Retail Prices ◽  
The Cost ◽  
Cost Of Living Index

This paper estimates a cost-of-living index using a dynamic structural model for two storable product categories. In each category, regime shifts to higher or lower retail prices are observed. Fixed-base indexes do a poor job of capturing changes in welfare after a regime shift, and deviate from the dynamic index by as much as 300 percent. I evaluate the extent to which two recently proposed indexes can approximate the model-derived index. These indexes improve welfare measurement and are straightforward to compute. The category’s competitive structure and features of the regime shift determine which of the two provides a better approximation. (JEL C43, C51, E31, L11)

scholarly journals Identification of Regime Shifts and Their Potential Drivers in the Shallow Eutrophic Lake Yilong, Southwest China

2020 ◽  
Vol 12 (9) ◽  
pp. 3704
Author(s):  
Lei Zhao ◽  
Mingguo Wang ◽  
Zhongyao Liang ◽  
Qichao Zhou
Keyword(s):  
Regime Shift ◽  
Eutrophic Lake ◽  
Regime Shifts ◽  
Nutrient Reduction ◽  
Eutrophic Lakes ◽  
Test Analysis ◽  
Key Drivers ◽  
High Possibility ◽  
Cage Fish

Regime shifts in shallow lakes can lead to great changes in ecosystem structures and functions, making ecosystem management more complicated. Lake Yilong, located in Yunnan Province, is one of the most eutrophic lakes in China. Although there is a high possibility that this lake has undergone regime shift one or more times, the presence of regime shifts and their drivers remain unknown. Here, we employed the sequential t-test analysis of regime shifts to detect the regime shifts based on the long-term (1989–2018) dataset of the lake. We further determined their potential drivers, and explored the nutrient thresholds of regime shifts and hysteresis. The results showed that during the testing period, three regime shifts occurred in 1996 (restorative type), 2009 (catastrophic type) and 2014 (restorative type). The potential key drivers for the first two regime shifts (1996 and 2009) were both related to aquaculture. The abolition of cage fish culture may have led to the restorative regime shift in 1996, and the stocking of crabs and excessive premature releasing of fry possibly caused the catastrophic regime shift in 2009. However, the third regime shift, which occurred in 2014, was possibly related to the drought and succedent hydration. These results indicate that adjustments of aquaculture strategy and hydrological conditions are critical for the lake ecosystem’s recovery. Moreover, the total phosphorus thresholds were identified to be lower than 0.046 mg/L (restorative type) and higher than 0.105 mg/L (catastrophic type), respectively. In addition, an obvious hysteresis was observed after 2014, suggesting that nutrient reduction is important for this lake’s management in the future.

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 Regime shifts in marine communities: a complex systems perspective on food web dynamics

2016 ◽  
Vol 283 (1825) ◽  
pp. 20152569 ◽  
Author(s):  
Johanna Yletyinen ◽  
Örjan Bodin ◽  
Benjamin Weigel ◽  
Marie C. Nordström ◽  
Erik Bonsdorff ◽  
...  
Keyword(s):  
Food Web ◽  
Food Webs ◽  
Regime Shift ◽  
Relative Strength ◽  
Regime Shifts ◽  
Exponential Random Graph ◽  
Novel Approach ◽  
Before And After ◽  
Dynamic Perspective ◽  
The Baltic

Species composition and habitats are changing at unprecedented rates in the world's oceans, potentially causing entire food webs to shift to structurally and functionally different regimes. Despite the severity of these regime shifts, elucidating the precise nature of their underlying processes has remained difficult. We address this challenge with a new analytic approach to detect and assess the relative strength of different driving processes in food webs. Our study draws on complexity theory, and integrates the network-centric exponential random graph modelling (ERGM) framework developed within the social sciences with community ecology. In contrast to previous research, this approach makes clear assumptions of direction of causality and accommodates a dynamic perspective on the emergence of food webs. We apply our approach to analysing food webs of the Baltic Sea before and after a previously reported regime shift. Our results show that the dominant food web processes have remained largely the same, although we detect changes in their magnitudes. The results indicate that the reported regime shift may not be a system-wide shift, but instead involve a limited number of species. Our study emphasizes the importance of community-wide analysis on marine regime shifts and introduces a novel approach to examine food webs.

Application of a sequential regime shift detection method to the Bering Sea ecosystem

2005 ◽  
Vol 62 (3) ◽  
pp. 328-332 ◽  
Author(s):  
Sergei Rodionov ◽  
James E. Overland
Keyword(s):  
Bering Sea ◽  
Regime Shift ◽  
Poor Performance ◽  
Regime Shifts ◽  
Test Analysis ◽  
Eastern Bering Sea ◽  
Time Signals ◽  
Shift Detection ◽  
Changes Over Time ◽  
The Bering Sea

Abstract A common problem of existing methods for regime shift detection is their poor performance at the ends of time-series. Consequently, shifts in environmental and biological indices are usually detected long after their actual appearance. A recently introduced method based on sequential t-test analysis of regime shifts (STARS) treats all incoming data in real time, signals the possibility of a regime shift as soon as possible, then monitors how perception of the magnitude of the shift changes over time. Results of a STARS application to the eastern Bering Sea ecosystem show how the 1989 and 1998 regime shifts manifest themselves in biotic and abiotic indices in comparison with the 1977 shift.

scholarly journals Reversal of a cyanobacterial bloom in response to early warnings

2016 ◽  
Vol 114 (2) ◽  
pp. 352-357 ◽  
Author(s):  
Michael L. Pace ◽  
Ryan D. Batt ◽  
Cal D. Buelo ◽  
Stephen R. Carpenter ◽  
Jonathan J. Cole ◽  
...  
Keyword(s):  
Nutrient Enrichment ◽  
Regime Shift ◽  
Cyanobacterial Bloom ◽  
Regime Shifts ◽  
Environmental Drivers ◽  
Reference Lake ◽  
Directional Change ◽  
Lakes And Reservoirs ◽  
Resilience Indicators ◽  
Theory And Experiments

Directional change in environmental drivers sometimes triggers regime shifts in ecosystems. Theory and experiments suggest that regime shifts can be detected in advance, and perhaps averted, by monitoring resilience indicators such as variance and autocorrelation of key ecosystem variables. However, it is uncertain whether management action prompted by a change in resilience indicators can prevent an impending regime shift. We caused a cyanobacterial bloom by gradually enriching an experimental lake while monitoring an unenriched reference lake and a continuously enriched reference lake. When resilience indicators exceeded preset boundaries, nutrient enrichment was stopped in the experimental lake. Concentrations of algal pigments, dissolved oxygen saturation, and pH rapidly declined following cessation of nutrient enrichment and became similar to the unenriched lake, whereas a large bloom occurred in the continuously enriched lake. This outcome suggests that resilience indicators may be useful in management to prevent unwanted regime shifts, at least in some situations. Nonetheless, a safer approach to ecosystem management would build and maintain the resilience of desirable ecosystem conditions, for example, by preventing excessive nutrient input to lakes and reservoirs.

scholarly journals Regime Shifts in Arctic Oscillation (AO) Variability 1951–2009

2010 ◽  
Vol 3 (1) ◽  
pp. 103-119
Author(s):  
Michał Marosz
Keyword(s):  
Arctic Oscillation ◽  
Regime Shift ◽  
Linear Trend ◽  
Regime Shifts ◽  
Atlantic Region ◽  
Mean Values ◽  
Field Response ◽  
The Mean ◽  
Trend Identification ◽  
Identification Techniques

Abstract Arctic Oscillation (AO) reflects the dominating mode of SLP (or 1000hPa) variability in hemispheric scale and seems to bear an effect on the weather and, in longer time scales, on climate. The aim of the research was to identify the possible regime shifts in the multiannual course of AO index which in turn might be utilised in the identification and quantification of the regional/local meteorological field response. The data (AO monthly and daily index values) were acquired from the NOAA Climate Prediction Centre. The AO index is constructed by projecting monthly/daily anomalies of 1000hPa heights poleward of 20°N onto the loading pattern of the AO - 1st (leading) EOF of the 1000hPa height field for the same spatial domain. The temporal scope of analysis was 1951-2009. The analysis comprised the regime shift identification techniques that track the shifts in the mean values and in the magnitude of fluctuations as well as the usage of the classical linear trend identification in the annual and seasonal scale. The regime shift recognition was followed by the compositing method that allowed the analysis of spatial anomalies in SLP field in Euro-Atlantic region that reflect the response of the regional airflow to identified circulation regimes.

scholarly journals Microbial Shift in the Enteric Bacteriome of Coral Reef Fish Following Climate-Driven Regime Shifts

2021 ◽  
Vol 9 (8) ◽  
pp. 1711
Author(s):  
Marie-Charlotte Cheutin ◽  
Sébastien Villéger ◽  
Christina C. Hicks ◽  
James P. W. Robinson ◽  
Nicholas A. J. Graham ◽  
...  
Keyword(s):  
Coral Reef ◽  
Reef Fish ◽  
Coral Reef Fish ◽  
Regime Shift ◽  
Taxonomic Composition ◽  
Regime Shifts ◽  
Ecosystem Change ◽  
Fermentative Bacteria ◽  
Seychelles Islands ◽  
Fish Gut

Replacement of coral by macroalgae in post-disturbance reefs, also called a “coral-macroalgal regime shift”, is increasing in response to climate-driven ocean warming. Such ecosystem change is known to impact planktonic and benthic reef microbial communities but few studies have examined the effect on animal microbiota. In order to understand the consequence of coral-macroalgal shifts on the coral reef fish enteric bacteriome, we used a metabarcoding approach to examine the gut bacteriomes of 99 individual fish representing 36 species collected on reefs of the Inner Seychelles islands that, following bleaching, had either recovered to coral domination, or shifted to macroalgae. While the coral-macroalgal shift did not influence the diversity, richness or variability of fish gut bacteriomes, we observed a significant effect on the composition (R2 = 0.02; p = 0.001), especially in herbivorous fishes (R2 = 0.07; p = 0.001). This change is accompanied by a significant increase in the proportion of fermentative bacteria (Rikenella, Akkermensia, Desulfovibrio, Brachyspira) and associated metabolisms (carbohydrates metabolism, DNA replication, and nitrogen metabolism) in relation to the strong turnover of Scarinae and Siganidae fishes. Predominance of fermentative metabolisms in fish found on macroalgal dominated reefs indicates that regime shifts not only affect the taxonomic composition of fish bacteriomes, but also have the potential to affect ecosystem functioning through microbial functions.

scholarly journals Data-driven identification of reliable sensor species to predict regime shifts in ecological networks

2020 ◽  
Vol 7 (8) ◽  
pp. 200896 ◽  
Author(s):  
Amin Ghadami ◽  
Shiyang Chen ◽  
Bogdan I. Epureanu
Keyword(s):  
Indicator Species ◽  
Early Warning ◽  
Regime Shifts ◽  
Data Driven ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Spatially Distributed ◽  
Data Driven Approach ◽  
Interacting Components

Signals of critical slowing down are useful for predicting impending transitions in ecosystems. However, in a system with complex interacting components not all components provide the same quality of information to detect system-wide transitions. Identifying the best indicator species in complex ecosystems is a challenging task when a model of the system is not available. In this paper, we propose a data-driven approach to rank the elements of a spatially distributed ecosystem based on their reliability in providing early-warning signals of critical transitions. The proposed method is rooted in experimental modal analysis techniques traditionally used to identify structural dynamical systems. We show that one could use natural system fluctuations and the system responses to small perturbations to reveal the slowest direction of the system dynamics and identify indicator regions that are best suited for detecting abrupt transitions in a network of interacting components. The approach is applied to several ecosystems to demonstrate how it successfully ranks regions based on their reliability to provide early-warning signals of regime shifts. The significance of identifying the indicator species and the challenges associated with ranking nodes in networks of interacting components are also discussed.

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