Spatial early warning signals for impending regime shifts: A practical framework for application in real‐world landscapes. (1st April 2019) - Nijp, Jelmer J.; Temme, Arnaud J.A.M.; van Voorn, George A.K.; Kooistra, Lammert; Hengeveld, Geerten M.; Soons, Merel B.; Teuling, Adriaan J.; Wallinga, Jakob
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. Abstract : Ongoing global change steadily increases pressure on ecosystems, to the point where a small additional change may cause a sudden ecosystem state shift and drastically modify ecosystem functioning. Predicting such generally undesired 'regime shifts' is crucial for timely management intervention. A promising prediction method is the translation of spatial vegetation patterns observed with, for example, satellite imagery into ecosystem resilience indicators. While these spatial indicators perform well in theory, we show how application to real‐world landscapes presents unexpected challenges and may even lead to misleading conclusions. Our study identifies the conditions for successful application of spatial indicators to real‐world landscapes.