Turning back from the brink: detecting an impending regime shift in time to avert it

Abstract

Ecological regime shifts are large, abrupt, long-lasting changes in ecosystems that often have considerable impacts on human economies and societies. Avoiding unintentional regime shifts is widely regarded as desirable, but prediction of ecological regime shifts is notoriously difficult. Recent research indicates that changes in ecological time series (e.g., increased variability and autocorrelation) could potentially serve as early warning indicators of impending shifts. A critical question, however, is whether such indicators provide sufficient warning to adapt management to avert regime shifts. We examine this question using a fisheries model, with regime shifts driven by angling (amenable to rapid reduction) or shoreline development (only gradual restoration is possible). The model represents key features of a broad class of ecological regime shifts. We find that if drivers can only be manipulated gradually management action is needed substantially before a regime shift to avert it; if drivers can be rapidly altered aversive action may be delayed until a shift is underway. Large increases in the indicators only occur once a regime shift is initiated, often too late for management to avert a shift. To improve usefulness in averting regime shifts, we suggest that research focus on defining critical indicator levels rather than detecting change in the indicators. Ideally, critical indicator levels should be related to switches in ecosystem attractors; we present a new spectral density ratio indicator to this end. Averting ecological regime shifts is also dependent on developing policy processes that enable society to respond more rapidly to information about impending regime shifts.

Fig. 1.

The fisheries food web model used to explore the use of regime shift indicators in averting ecological regime shifts. Regime shifts are driven by angling or shoreline development. Angling directly affects the populations of adult piscivores through harvesting (qE). Shoreline development affects the amount and quality of refuge habitat, and thus the rate at which juvenile piscivores hide from predators (h).

Fig. 2.

The attainable proximity to a regime shift is much greater for driving variables that can be rapidly manipulated (angling) than for variables that can only be manipulated gradually (shoreline development). Year 0 is defined as the year in which the switch from the lower (F = 0.34) to the upper (F = 76.92) planktivore attractor occurs. (A) In the absence of policy action, a harvest-driven shift occurs in years 81 to 95. The switch in attractors occurs at qE = 1.78. (B) The window for averting a regime shift by implementing harvest-reduction policy MS1 lasts to year 91, well within the regime shift. (C) However, instituting MS1 just a year later cannot avert a regime shift. (D) In the absence of policy action, a shoreline development-driven shift occurs between years 80 and 95, and the switch in attractors occurs at h = 3.7. (E) To avert a regime shift, shoreline restoration policy MS2 has to be implemented substantially before the shift, by year 35. (F) Taking action slightly later (year 37) cannot avert a regime shift, although it is substantially delayed.

Fig. 3.

Large annual increases in the regime shift indicators only occur once a regime shift is underway, which is often too late for management action to avert a shift. Gradual increases in variance (A and D) and AR1 (C and F) may occur before a regime shift, but specific thresholds need to be defined to indicate whether or when policy action to avert a shift is required. Indicators are based on the within-year planktivore data and correspond to the changes depicted in Fig. 2 A and D (vertical lines show the end of the policy windows).

Fig. 4.

For the angling-induced regime shift in Fig. 2A, clear signs of spectral “reddening” are evident after the switch from the lower to the upper planktivore attractor. The spectral density describes how variation in the within-year planktivore population data may be accounted for by cyclic components of different frequencies as determined by Fourier analysis. Each line gives the AR1-based spectral density for 1 year. Analogous spectra exist for the shoreline-driven regime shift.

Fig. 5.

The point at which the spectral density switches from domination by high to domination by low frequency processes (10-year running mean spectral density ratio exceeds 1 provided warning of a shift in the attractor in our model. We defined the spectral density ratio as the ratio of the spectral density at a frequency of 0.05 (low) to the density at a frequency of 0.5 (high) as given in Fig. 4.