Projecting Uncertainty in Ecosystem Persistence Under Climate Change

IF 12 1区 环境科学与生态学 Q1 BIODIVERSITY CONSERVATION
Christina A. Buelow, Dominic A. Andradi-Brown, Thomas A. Worthington, Maria F. Adame, Rod M. Connolly, Catherine E. Lovelock, Kerrylee Rogers, Jaramar Villarreal-Rosas, Christopher J. Brown
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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.

Abstract Image

气候变化下生态系统持续性的不确定性预测
需要对生态系统对日益增加的气候和人为压力的反应进行全球预估,以便为适应规划提供信息。然而,通常没有适当的时空分辨率数据来参数化全球尺度上的复杂环境过程。当参数不确定性很高时,能够预测生态系统持续存在的概率的建模方法可能提供了一条前进的道路。特别是,保护具有复杂动态的沿海生态系统,如红树林,可能受益于了解它们未来极有可能持续存在的地方,或者,如果没有适当分辨率的额外数据,就无法可靠地估计。本文通过模拟网络模型,对SSP5-8.5气候排放情景下2040-2060年全球红树林生态系统净变化方向进行了概率预测。77%的人认为向海净损失是最可能的结果[37%-78%;95%可信区间(CI)],而30% [15% ~ 59%;CI]预计将经历陆地净增益或稳定。在50%以上的森林单位中,预测是不明确的,因此是不可靠的,净损失或净收益的概率几乎相等。用局部精确数据参数化的定量模型可以解决在概率预测不可靠的地方红树林未来持续存在的不确定性。在保护情景下所作的预测也表明,如果采取行动进行管理或恢复,将来可能出现净增长或稳定的红树林单位的数量可能会增加近一倍。我们模拟生态系统对气候和人为压力的反应的方法,清楚地表明了生态系统持久性的确定性(或不确定性),从而可以为保护规划提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Global Change Biology
Global Change Biology 环境科学-环境科学
CiteScore
21.50
自引率
5.20%
发文量
497
审稿时长
3.3 months
期刊介绍: Global Change Biology is an environmental change journal committed to shaping the future and addressing the world's most pressing challenges, including sustainability, climate change, environmental protection, food and water safety, and global health. Dedicated to fostering a profound understanding of the impacts of global change on biological systems and offering innovative solutions, the journal publishes a diverse range of content, including primary research articles, technical advances, research reviews, reports, opinions, perspectives, commentaries, and letters. Starting with the 2024 volume, Global Change Biology will transition to an online-only format, enhancing accessibility and contributing to the evolution of scholarly communication.
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