Abstract
A range of indicators have been proposed for identifying the elevated risk of critical transitions in ecosystems. Most indicators are based on the idea that critical slowing down can be inferred from changes in statistical properties of natural fluctuations and spatial patterns. However, identifying these signals in nature has remained challenging. An alternative approach is to infer changes in resilience from differences in standardized experimental perturbations. However, system-wide experimental perturbations are rarely feasible. Here we evaluate the potential to infer the risk of large-scale systemic transitions from local experimental or natural perturbations. We use models of spatially explicit landscapes to illustrate how recovery rates upon small-scale perturbations decrease as an ecosystem approaches a tipping point for a large-scale collapse. We show that the recovery trajectory depends on: (1) the resilience of the ecosystem at large scale, (2) the dispersal rate of organisms, and (3) the scale of the perturbation. In addition, we show that recovery of natural disturbances in a heterogeneous environment can potentially function as an indicator of resilience of a large-scale ecosystem. Our analyses reveal fundamental differences between large-scale weak and local-scale strong perturbations, leading to an overview of opportunities and limitations of the use of local disturbance-recovery experiments.
Highlights
The idea that we might detect loss of resilience as an early-warning signal for critical transitions in ecological systems has attracted much attention (Scheffer and others 2012; Dakos and others 2015)
Our analyses suggest that in spatially extended ecosystems, reduced recovery rates upon local perturbations may signal that the ecosystem is approaching a system-wide transition
Our results show that local recovery rates depend strongly on the size of the local perturbations and on the exchange of resources between the perturbed area and its surrounding
Summary
The idea that we might detect loss of resilience as an early-warning signal for critical transitions in ecological systems has attracted much attention (Scheffer and others 2012; Dakos and others 2015). A. van de Leemput and others this ‘critical slowing down’ is that, if we can measure the time it takes for a system to return to its original state after a small disturbance, we may indicate the proximity of the system to a catastrophic shift (van Nes and Scheffer 2007). A range of indicators has been proposed that may reflect such slowing down in natural fluctuations or in spatial patterns (Scheffer and others 2012; Dakos and others 2015). Rising temporal autocorrelation and variance have received much attention as indicators of loss of resilience before a transition. Other proposed indicators of resilience include increasing spatial correlation (Dakos and others 2010), increasing skewness (Guttal and Jayaprakash 2008), changing frequency spectra (Kleinen and others 2003), deviations in pattern formation (Rietkerk and others 2004), and truncated power law distributions (Kefi and others 2007)
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