Spatial correlation as leading indicator of catastrophic shifts

By Vasilis Dakos, E. V. van Nes, R. Donangelo, H. Fort, Marten Scheffer. Theoretical Ecology, 2010

343 citations recorded by the source corpus.

Generic early-warning signals such as increased autocorrelation and variance have been demonstrated in time-series of systems with alternative stable states approaching a critical transition. However, lag times for the detection of such leading indicators are typically long. Here, we show that increased spatial correlation may serve as a more powerful early-warning signal in systems consisting of many coupled units. We first show why from the universal phenomenon of critical slowing down, spatial correlation should be expected to increase in the vicinity of bifurcations. Subsequently, we explore the applicability of this idea in spatially explicit ecosystem models that can have alternative attractors. The analysis reveals that as a control parameter slowly pushes the system towards the threshold, spatial correlation between neighboring cells tends to increase well before the transition. We show that such increase in spatial correlation represents a better early-warning signal than indicators derived from time-series provided that there is sufficient spatial heterogeneity and connectivity in the system.

Subfields: Economics and Econometrics, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/314938da2de86a7d561bff418c9988e1d1065134

How researchers see this paper

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

In Economics and Econometrics

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Framework for understanding marine ecosystem health (2013, background): “Connectivity : refers to spatial links allowing exchanges of individuals or genetic materials amongst meta-populations (Steneck & Wilson 2010) and of materials and energy amongst habitats or sub-regions within an ecosystem (Dakos et al. 2010)”
Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data (2012, background): “Some of these signals are in fact mathematical analogs of the signals in time series indicators (spatial variance [57], spatial skewness [58], spatial autocorrelation [59]), while others can be system-specific, such as patch shape [60] and patch size distribution [61,62].”
Resilience indicators: prospects and limitations for early warnings of regime shifts (2015, background): “— interference with spatial processes: spatial interactions in the form of disturbance, irregular movement of key species, underlying spatial heterogeneity, or dispersal may influence CSD indicators [23,56].”