Regime shifts in ecological systems can occur with no warning

Hastings, Alan; Wysham, Derin B.

doi:10.1111/j.1461-0248.2010.01439.x

Cited by 305 publications

(261 citation statements)

References 30 publications

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“…CSD indicators can capture changes in these dynamics but only when it comes to stable equilibria in the presence of weak stochasticity [12]. Identifying transitions across chaotic attractors or, more generally, in systems with nonlinear dynamics may be difficult with CSD indicators [53,54]. By contrast, EDM-derived nonlinearity may be broader in its application as it can capture changes in dynamics beyond stable attractors typical of the dynamics encountered in natural systems.…”

Section: Discussionmentioning

confidence: 99%

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

Dakos

Glaser

Hsieh

et al. 2017

J. R. Soc. Interface.

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Populations occasionally experience abrupt changes, such as local extinctions, strong declines in abundance or transitions from stable dynamics to strongly irregular fluctuations. Although most of these changes have important ecological and at times economic implications, they remain notoriously difficult to detect in advance. Here, we study changes in the stability of populations under stress across a variety of transitions. Using a Ricker-type model, we simulate shifts from stable point equilibrium dynamics to cyclic and irregular boom-bust oscillations as well as abrupt shifts between alternative attractors. Our aim is to infer the loss of population stability before such shifts based on changes in nonlinearity of population dynamics. We measure nonlinearity by comparing forecast performance between linear and nonlinear models fitted on reconstructed attractors directly from observed time series. We compare nonlinearity to other suggested leading indicators of instability (variance and autocorrelation). We find that nonlinearity and variance increase in a similar way prior to the shifts. By contrast, autocorrelation is strongly affected by oscillations. Finally, we test these theoretical patterns in datasets of fisheries populations. Our results suggest that elevated nonlinearity could be used as an additional indicator to infer changes in the dynamics of populations under stress.

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Section: Discussionmentioning

confidence: 99%

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

Dakos

Glaser

Hsieh

et al. 2017

J. R. Soc. Interface.

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“…In their review of early warning signals to predict critical transitions, Scheffer et al (2009) refer to critical slowing down when a system becomes increasingly slow in recovering from small (Scheffer et al, 2009;Dakos et al, 2011). Such early warning signals have been shown to be accurate in some model simulations (van Nes and Scheffer, 2007;Scheffer et al, 2009;Dakos et al, 2011), but not in others (Hastings and Wysham, 2010;Boettiger and Hastings, 2013). Nonetheless, in practice it remains difficult to verify and use them.…”

Section: Best Estimate For Seagrass Health and Resilience?mentioning

confidence: 99%

“…This phenomenon is referred to as 'critical slowing down' (Dakos et al, 2011). It still remains largely unknown whether critical slowing down can be used in practice as an indicator or early warning signal across ecosystems (Hastings and Wysham, 2010). As a matter of fact, the main support for the existence of critical slowing down originates from theoretical models based on long-term data and on specific systems (Boettiger and Hastings, 2013).…”

Section: Introductionmentioning

confidence: 99%

Cover versus recovery: Contrasting responses of two indicators in seagrass beds

Soissons

Han

et al. 2014

Marine Pollution Bulletin

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a b s t r a c tDespite being a highly valuable key-stone ecosystem, seagrass meadows are threatened and declining worldwide, creating urgent need for indicators of their health status. We compared two indicators for seagrass health: standing leaf area index versus relative recovery from local disturbance. Disturbance was created by removing aboveground biomass and recording the rate of regrowth for Zostera marina meadows exposed to contrasting wave regimes and nutrient stress levels.Within the experimental period, relative regrowth in gaps was around 50% in most plots, except for the ambient nutrient treatment at the sheltered site, where it exceeded 100%. The two indicators showed an opposite response to disturbance: the higher the standing leaf area index, the lower the relative recovery from disturbance. This conflicting response raises the question on the proper interpretation of such indicators to estimate seagrass health and resilience, and how to ideally monitor seagrass ecosystems in order to predict collapse.

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“…Other work, both ecological [Doak et al, 2008;Hastings and Wysham, 2010] and more interdisciplinary [Taleb, 2007;Casti, 2012], suggests that extreme events and threshold crossings may, like earthquakes, be impossible to predict more precisely than specifying return times or probabilities for events of certain magnitudes [Ditlevsen and Johnsen, 2010;Parmesan et al, 2011;Loehman and Keane, 2012]. At best, the indicators may be present in only a subset of circumstances.…”

Section: Abrupt Changes and Extreme Events Thresholds And Tipping Pmentioning

confidence: 99%

Smoke consequences of new wildfire regimes driven by climate change

et al. 2014

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Smoke from wildfires has adverse biological and social consequences, and various lines of evidence suggest that smoke from wildfires in the future may be more intense and widespread, demanding that methods be developed to address its effects on people, ecosystems, and the atmosphere. In this paper, we present the essential ingredients of a modeling system for projecting smoke consequences in a rapidly warming climate that is expected to change wildfire regimes significantly. We describe each component of the system, offer suggestions for the elements of a modeling agenda, and provide some general guidelines for making choices among potential components. We address a prospective audience of researchers whom we expect to be fluent already in building some or many of these components, so we neither prescribe nor advocate particular models or software. Instead, our intent is to highlight fruitful ways of thinking about the task as a whole and its components, while providing substantial, if not exhaustive, documentation from the primary literature as reference. This paper provides a guide to the complexities of smoke modeling under climate change, and a research agenda for developing a modeling system that is equal to the task while being feasible with current resources.

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Regime shifts in ecological systems can occur with no warning

Cited by 305 publications

References 30 publications

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

Cover versus recovery: Contrasting responses of two indicators in seagrass beds

Smoke consequences of new wildfire regimes driven by climate change

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