Soil Geography and Landscape group, Department of Environmental Sciences, Wageningen University & Research, Wageningen, the Netherlands.
KWR Watercycle Research Institute, Ecohydrology Group, Nieuwegein, the Netherlands.
Glob Chang Biol. 2019 Jun;25(6):1905-1921. doi: 10.1111/gcb.14591. Epub 2019 Apr 1.
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.
预测生态系统对全球环境变化的响应是一个具有重大社会相关性的紧迫科学挑战。许多生态系统对环境变化表现出非线性响应，甚至可能经历实际上不可逆转的“状态转变”，从而引发生态系统崩溃。最近，基于时空度量的早期预警信号已被提出，用于识别即将发生的状态转变。快速增加的遥感数据可用性为在现实世界中应用基于模型的空间早期预警信号提供了极好的机会，以评估生态系统的恢复力并识别由全球变化引起的即将发生的状态转变。这些信息将使土地管理者和政策制定者能够进行干预并避免灾难性的转变，还可以促使生态系统发生状态转变，使其达到期望的状态。在这里，我们表明，在现实景观中应用空间早期预警信号会带来独特且意想不到的挑战，如果在不仔细考虑手头的空间数据和过程的情况下使用，可能会导致误导性的结论。我们确定了关键的实际和理论问题，并基于文献综述和真实数据示例，为在异构的现实景观中应用空间早期预警信号提供了指导方针。主要确定的问题包括：（1）现实景观中的空间异质性可能会增强状态转变的可逆性，并提高景观水平对环境变化的恢复力；（2）生态系统状态通常难以定义，而这些定义对空间早期预警信号有很大影响；（3）空间环境变异性和社会经济因素可能会影响空间格局、空间早期预警信号和相关的状态转变预测。我们提出了一个新的框架，从生态系统的角度转向景观方法。该框架可用于确定使用空间遥感数据进行恢复力评估可能成功的条件，支持明智地应用空间早期预警信号，并改善对生态系统对全球环境变化响应的预测。
