松材线虫病的入侵:对中国森林碳储存的威胁

IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Bohai Hu , Wenjiang Huang , Zhuoqing Hao , Jing Guo , Yanru Huang , Xiangzhe Cheng , Jing Zhao , Quanjun Jiao , Biyao Zhang
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引用次数: 0

摘要

中国的森林通过光合作用平衡大气中的碳含量,在应对全球气候变化方面发挥着至关重要的作用。由松材线虫(PWN,Bursaphelenchus xylophilus)引起的松材线虫病(PWD)的出现对这些森林的稳定性提出了挑战,导致树木大量死亡,破坏了原有的生态平衡。然而,PWD对中国森林碳储存和碳恢复的影响仍不清楚。在这项研究中,我们整合了森林调查、遥感和气象观测等多种数据源,并应用一种方法对中国各地寄主松树的抗性进行了精细划分。利用 MaxEnt 模型、活碳风险模型和包含干扰机制的碳恢复 REGIME 模型,我们预测了 PWD 全面入侵造成的森林碳风险损失,并评估了受影响森林的碳恢复时间。我们估计,在当前气候条件下,中国森林因有害生物入侵造成的碳损失总风险为 483.23 Tg C,平均碳恢复时间为 13.95 年。破坏性干旱的主要风险区集中在中国南部沿海地区及邻近省份,呈现出从重点地区向外辐射的风险溢出模式。森林风险度(风险 C/区域总 C)最高的六个省份依次为福建(13.69%)、浙江(9.42%)、湖南(7.49%)、广西(7.40%)、江西(7.35%)和广东(7.05%)。我们的研究结果表明,PWD 入侵的严重后果已经使受影响的森林从碳汇转变为碳源。这凸显了采取有效措施阻止其引入和扩散,从而促进森林生态系统恢复和可持续发展的紧迫性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Invasion of Pine Wilt Disease: A threat to forest carbon storage in China

Invasion of Pine Wilt Disease: A threat to forest carbon storage in China
China’s forests, which balance atmospheric carbon (C) levels through photosynthesis, play a crucial role in combating global climate change. The emergence of Pine wilt disease (PWD), caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus), has challenged the stability of these forests, leading to significant tree mortality and disrupting the original ecological balance. However, the impact of PWD on carbon storage and recovery in Chinese forests remains unclear. In this study, we integrated multiple data sources, including forest surveys, remote sensing, and meteorological observations, and applied a method of finely partitioning the resistance of host pine trees across China. Using the MaxEnt model, a live carbon risk model, and a C recovery REGIME model that incorporates disturbance mechanisms, we predicted the forest C risk loss caused by the comprehensive invasion of PWD and assessed the C recovery time for affected forests. We estimate that the total risk of C loss due to PWD invasion under current climate conditions in Chinese forests is 483.23 Tg C, with an average C recovery time of 13.95 years. The main risk areas for PWD are concentrated in the southern coastal regions of China and adjacent provinces, presenting a risk spillover pattern that radiates from focal areas outward. The six provinces with the highest forest risk degree (risk C/total regional C) are, in order, Fujian (13.69%), Zhejiang (9.42%), Hunan (7.49%), Guangxi (7.40%), Jiangxi (7.35%), and Guangdong (7.05%). Our findings indicate that the severe consequences of PWD invasion have transformed affected forests from C sinks to sources. This underscores the urgency of implementing effective measures to block its introduction and spread, thereby promoting the recovery and sustainable development of forest ecosystems.
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来源期刊
Ecological Indicators
Ecological Indicators 环境科学-环境科学
CiteScore
11.80
自引率
8.70%
发文量
1163
审稿时长
78 days
期刊介绍: The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published. • All aspects of ecological and environmental indicators and indices. • New indicators, and new approaches and methods for indicator development, testing and use. • Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources. • Analysis and research of resource, system- and scale-specific indicators. • Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs. • How research indicators can be transformed into direct application for management purposes. • Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators. • Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.
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