Spatial early warning signals for impending regime shifts: A practical framework for application in real‐world landscapes

Abstract

Prediction of ecosystem response to global environmental change is a pressing scientific challenge of major societal relevance. Many ecosystems display nonlinear responses to environmental change, and may even undergo practically irreversible ‘regime shifts’ that initiate ecosystem collapse. Recently, early warning signals based on spatiotemporal metrics have been proposed for the identification of impending regime shifts. The rapidly increasing availability of remotely sensed data provides excellent opportunities to apply such model‐based spatial early warning signals in the real world, to assess ecosystem resilience and identify impending regime shifts induced by global change. Such information would allow land‐managers and policy makers to interfere and avoid catastrophic shifts, but also to induce regime shifts that move ecosystems to a desired state. Here, we show that the application of spatial early warning signals in real‐world landscapes presents unique and unexpected challenges, and may result in misleading conclusions when employed without careful consideration of the spatial data and processes at hand. We identify key practical and theoretical issues and provide guidelines for applying spatial early warning signals in heterogeneous, real‐world landscapes based on literature review and examples from real‐world data. Major identified issues include (1) spatial heterogeneity in real‐world landscapes may enhance reversibility of regime shifts and boost landscape‐level resilience to environmental change (2) ecosystem states are often difficult to define, while these definitions have great impact on spatial early warning signals and (3) spatial environmental variability and socio‐economic factors may affect spatial patterns, spatial early warning signals and associated regime shift predictions. We propose a novel framework, shifting from an ecosystem perspective towards a landscape approach. The framework can be used to identify conditions under which resilience assessment with spatial remotely sensed data may be successful, to support well‐informed application of spatial early warning signals, and to improve predictions of ecosystem responses to global environmental change.

The decline of a hidden and expansive microhabitat: the subnivium

“…Under warming of 5°C, reductions in subnivium extent and duration were abrupt and widespread. Although there is a growing body of literature exploring tipping points and abrupt ecological change (Ratajczak et al 2018), real-world examples have lagged behind theory (Nijp et al 2019), with empirical examples for winter ecosystems particularly scarce (Turner et al 2020). Our study is among the first to highlight abrupt change in seasonally snow-covered environments.…”

Geometric analysis of regime shifts in coral reef communities

“…In the more general ecological case where species can be very different, more habitat connectivity or higher movement rates are still expected to create less relative clustering (approaching one with the highest connectivity or movement rates) (Bolker and Pacala 1999, Tarnita et al 2009, Tekwa et al 2019 b ). However, in some systems, a bifurcation is associated with dramatic changes in spatial clustering (Kéfi et al 2013, Nijp et al 2019), which may produce quantitatively different coupled dynamics. Thus, the constant clustering assumption is an approximation that roughly captures spatial effects on regime dynamics, but real spatially driven bifurcations may be more complex.…”

Spatio-temporal analysis of remote sensing images provides early warning signals of forest mortality

“…Although successful applications of critical transitions approaches on space and time have been mostly reported in fully controlled experimental studies or using simulation data (e.g., Carpenter et al, 2011; Carpenter and Brock, 2006), their applications in real ecosystems have been limited. Moreover, they are still subjected to false alarms (either false negative or false positive alarms of upcoming state change in an ecosystem) (Dakos et al, 2015; Nijp et al, 2019). For instance, the methods generating TEW are sensitive to pre-processing steps and their performance may be affected by the size of the data (Dakos et al, 2012a).…”