Rising variance: a leading indicator of ecological transition.

By Stephen R. Carpenter, William A. Brock. Ecology Letters, 2006

937 citations recorded by the source corpus.

Regime shifts are substantial, long-lasting reorganizations of complex systems, such as ecosystems. Large ecosystem changes such as eutrophication, shifts among vegetation types, degradation of coral reefs and regional climate change often come as surprises because we lack leading indicators for regime shifts. Increases in variability of ecosystems have been suggested to foreshadow ecological regime shifts. However, it may be difficult to discern variability due to impending regime shift from that of exogenous drivers that affect the ecosystem. We addressed this problem using a model of lake eutrophication. Lakes are subject to fluctuations in recycling associated with regime shifts, as well as fluctuating nutrient inputs. Despite the complications of noisy inputs, increasing variability of lake-water phosphorus was discernible prior to the shift to eutrophic conditions. Simulations show that rising standard deviation (SD) could signal impending shifts about a decade in advance. The rising SD was detected by studying variability around predictions of a simple time-series model, and did not depend on detailed knowledge of the actual ecosystem dynamics.

Subfields: Environmental Chemistry, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/66ece56fad0abf74bc3d4299caff0399fc6ee926

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Chemistry

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Early warning signals also precede non-catastrophic transitions (2013, background): “This is reflected by the increase in the standard deviation of temporal fluctuations in the state variable just before a transition occurs (Oborny et al. 2005, Carpenter and Brock 2006) (Fig.”
Cross-scale Drivers of Natural Disturbances Prone to Anthropogenic Amplification: The Dynamics of Bark Beetle Eruptions (2008, background): “Fourth, regime-shift theory suggests that it may be difficult to restore the system to its original state following hysteresis (i.e., transition to a new stable state) (Scheffer and Carpenter 2003, Carpenter and Brock 2006 ).”
RESILIENCE: THE EMERGENCE OF A PERSPECTIVE FOR SOCIAL-ECOLOGICAL SYSTEMS ANALYSES (2006, background): “Recent work suggest that complex systems ‘‘stutter’’ or exhibit increased variance at multiple scales in advance of a regime shift (Kleinen et al., 2003; Carpenter and Brock, 2006).”
Ecological Stoichiometry, Biogeochemical Cycling, Invasive Species, and Aquatic Food Webs: San Francisco Estuary and Comparative Systems (2011, background): “Such regime shifts in species are considered difficult to predict and model, but clues about regime shifts are provided in the variance of biomass or chemical constituents of aquatic ecosystems (Carpenter and Brock, 2006).”
Global change and species interactions in terrestrial ecosystems. (2008, background): “However, a slow decrease in ecological resilience following seemingly minor changes in GEC drivers can push ecosystems to a critical threshold, beyond which normal stochastic events can trigger rapid ecosystem state change (Scheffer et al. 2001; Carpenter & Brock 2006).”
Ecological thresholds and regime shifts: approaches to identification. (2009, background): “This and other features have led to propose indicators that should in principle enable detection of an approaching threshold before crossing it: rising variance [55], spectrum reddening (i.”
Do early warning indicators consistently predict nonlinear change in long‐term ecological data? (2016, background): “…series show characteristic behaviours as a consequence of this process, notably an increase in variance and autocorrelation over time, and these behaviours have been suggested as potential EWIs (Wissel 1984; Carpenter & Brock 2006; van Nes & Scheffer 2007; Dakos et al. 2008; Scheffer et al. 2009).”
Interacting regime shifts in ecosystems: implication for early warnings (2010, background): “Theory and examples for this source of 924 variance, or squealing, have been discussed elsewhere (Brock and Carpenter 2006, Brock et al. 925 2008, Carpenter and Brock 2006, Biggs et al. 2009, Scheffer et al. 2009).”
Turning back from the brink: Detecting an impending regime shift in time to avert it (2009, background): “Specifically, time series data may show increased variability (25), changes in skewness (27), higher correlation through time (29, 33), and slower rates of recovery from disturbances (32) in advance of regime shifts.”
Unifying the concepts of stability and resilience in ecology (2020, background): “Additionally, both decreasing (i.e. slowing down) and increasing (i.e. flickering) constancy have been identified as early warning signals for critical transitions and thus the loss of stability (Carpenter & Brock, 2006; Scheffer et al., 2009).”
Fundamentals of Microbial Community Resistance and Resilience (2012, methodology): “Additionally, Bayesian approaches such as dynamic linear models (DLM) account for the autocorrelation of time series data, and estimate the sensitivity of data to disturbance (e.g., Carpenter and Brock, 2006).”
Critical natural capital revisited: Ecological resilience and sustainable development (2009, background): “Those are: (1) the extrapolation of empirically estimated return times of controlling variables well distant from the threshold (Wissel, 1984); (2) the examination of standard deviations of fast variables in the vicinity of thresholds ( Carpenter and Brock, 2006 );and(3) the repeatedcalculation ofth”