Spatial early warning signals for impending regime shifts

A practical framework for application in real-world landscapes

Jelmer J. Nijp*, Arnaud J.A.M. Temme, George A.K. van Voorn, Lammert Kooistra, Geerten M. Hengeveld, Merel B. Soons, Adriaan J. Teuling, Jakob Wallinga

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1905-1921
JournalGlobal Change Biology
Volume25
Issue number6
DOIs
Publication statusPublished - Jun 2019

Fingerprint

Ecosystems
environmental change
ecosystem
ecosystem response
prediction
ecosystem resilience
environmental economics
literature review
spatial data
global change
Managers
Availability
Economics

Keywords

  • alternative stable states
  • critical slowing down
  • early warning signals
  • ecosystem resilience
  • environmental change
  • landscapes
  • regime shifts
  • remote sensing
  • spatial patterns
  • tipping points

Cite this

@article{22f0e6801d554a008c4fd4a73ba68089,
title = "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.",
keywords = "alternative stable states, critical slowing down, early warning signals, ecosystem resilience, environmental change, landscapes, regime shifts, remote sensing, spatial patterns, tipping points",
author = "Nijp, {Jelmer J.} and Temme, {Arnaud J.A.M.} and {van Voorn}, {George A.K.} and Lammert Kooistra and Hengeveld, {Geerten M.} and Soons, {Merel B.} and Teuling, {Adriaan J.} and Jakob Wallinga",
year = "2019",
month = "6",
doi = "10.1111/gcb.14591",
language = "English",
volume = "25",
pages = "1905--1921",
journal = "Global Change Biology",
issn = "1354-1013",
publisher = "Wiley",
number = "6",

}

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

In: Global Change Biology, Vol. 25, No. 6, 06.2019, p. 1905-1921.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Spatial early warning signals for impending regime shifts

T2 - A practical framework for application in real-world landscapes

AU - Nijp, Jelmer J.

AU - Temme, Arnaud J.A.M.

AU - van Voorn, George A.K.

AU - Kooistra, Lammert

AU - Hengeveld, Geerten M.

AU - Soons, Merel B.

AU - Teuling, Adriaan J.

AU - Wallinga, Jakob

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

KW - alternative stable states

KW - critical slowing down

KW - early warning signals

KW - ecosystem resilience

KW - environmental change

KW - landscapes

KW - regime shifts

KW - remote sensing

KW - spatial patterns

KW - tipping points

U2 - 10.1111/gcb.14591

DO - 10.1111/gcb.14591

M3 - Article

VL - 25

SP - 1905

EP - 1921

JO - Global Change Biology

JF - Global Change Biology

SN - 1354-1013

IS - 6

ER -