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预测气候变化下生态系统持久性的不确定性

Projecting Uncertainty in Ecosystem Persistence Under Climate Change.

作者信息

Buelow Christina A, Andradi-Brown Dominic A, Worthington Thomas A, Adame Maria F, Connolly Rod M, Lovelock Catherine E, Rogers Kerrylee, Villarreal-Rosas Jaramar, Brown Christopher J

机构信息

Coastal and Marine Research Centre, Australian Rivers Institute, School of Environment and Science, Griffith University, Gold Coast, Queensland, Australia.

Thriving Oceans Research Hub, School of Geosciences, The University of Sydney, Camperdown, New South Wales, Australia.

出版信息

Glob Chang Biol. 2025 Sep;31(9):e70468. doi: 10.1111/gcb.70468.

Abstract

Global projections of ecosystem responses to increasing climatic and anthropogenic pressures are needed to inform adaptation planning. However, data of appropriate spatiotemporal resolution are often not available to parameterize complex environmental processes at the global scale. Modeling approaches that can project the probability of ecosystem persistence when parameter uncertainty is high may offer a way forward. In particular, the conservation of coastal ecosystems with complex dynamics, like mangrove forests, may benefit from knowing where their future persistence is highly probable or, alternatively, cannot be reliably estimated without additional data of appropriate resolution. Here, we simulated network models to make probabilistic projections of the direction of net change in mangrove ecosystems worldwide under the SSP5-8.5 climate emissions scenario by the years 2040-2060. Seaward net loss was the most probable outcome in 77% [37%-78%; 95% confidence interval (CI)] of mangrove forest units, while 30% [15%-59%; CI] were projected to experience landward net gain or stability. In more than 50% of forest units, projections were ambiguous and therefore unreliable, with a near equal probability of net loss or gain. Quantitative models parameterized with locally accurate data could resolve uncertainty in the future persistence of mangroves in places with unreliable probabilistic projections. Projections made under conservation scenarios also showed that, with action to manage or restore, the number of mangrove forest units likely to experience net gain or stability in the future could nearly double. Our approach to simulating ecosystem responses to climatic and anthropogenic pressures provides a clear indication of how certain (or uncertain) ecosystem persistence is and thus can inform conservation planning.

摘要

为了为适应规划提供信息,需要对生态系统对不断增加的气候和人为压力的反应进行全球预测。然而,在全球尺度上,往往没有合适时空分辨率的数据来参数化复杂的环境过程。当参数不确定性很高时,能够预测生态系统持久性概率的建模方法可能提供一条前进的道路。特别是,对于像红树林这样具有复杂动态的沿海生态系统的保护,了解它们未来在何处极有可能持续存在,或者在没有适当分辨率的额外数据的情况下无法可靠估计,可能会有所帮助。在这里,我们模拟了网络模型,以对2040 - 2060年在SSP5 - 8.5气候排放情景下全球红树林生态系统净变化方向进行概率预测。在77% [37% - 78%;95%置信区间(CI)]的红树林单元中,向海净损失是最可能的结果,而预计有30% [15% - 59%;CI]会出现向陆净增加或稳定。在超过50%的森林单元中,预测是模糊的,因此不可靠,净损失或净增加的概率几乎相等。用局部准确数据参数化的定量模型可以解决概率预测不可靠地区红树林未来持久性的不确定性。在保护情景下所做的预测还表明,通过采取管理或恢复行动,未来可能经历净增加或稳定的红树林单元数量可能几乎翻倍。我们模拟生态系统对气候和人为压力反应的方法清楚地表明了生态系统持久性的确定程度(或不确定程度),从而可以为保护规划提供信息。

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