Emergent vulnerability to climate-driven disturbances in European forests.
Emergent vulnerability to climate-driven disturbances in European forests.
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DOI：
10.1038/s41467-021-21399-7
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发表时间：
2021-02-23
影响因子：
16.6
通讯作者：
Cescatti A
中科院分区：
文献类型：
--
作者：
Forzieri G;Girardello M;Ceccherini G;Spinoni J;Feyen L;Hartmann H;Beck PSA;Camps-Valls G;Chirici G;Mauri A;Cescatti A
随着地球气候的变化，森林干扰状况预计将加剧。量化森林对干扰的脆弱性并了解其内在机制对于制定减缓和适应战略至关重要。然而，在区域到大陆的尺度上，观测证据基本上是缺失的。在这里，我们通过将机器学习与干扰数据和卫星产品相结合，量化了1979-2018年期间欧洲森林对火灾，风暴和昆虫爆发的脆弱性。我们发现，大约334亿吨森林生物量可能受到这些干扰的严重影响，与昆虫爆发（26%）相比，暴露于风暴（40%）和火灾（34%）时的相对损失更高。脆弱性的空间格局受森林特征和背景气候之间相互作用的强烈控制。脆弱性的热点地区位于欧洲南部和北方气候包络线的边界。总体森林脆弱性有一个明显的趋势，其驱动因素是气候变暖导致植物对虫害爆发的防御机制减少，特别是在高纬度地区。自然干扰危及健康和多产的森林，但在大规模上量化其影响具有挑战性。在本文中，作者将机器学习应用于干扰记录和卫星数据，以量化和绘制1979-2018年欧洲森林对火灾、风暴和昆虫爆发的脆弱性。
Forest disturbance regimes are expected to intensify as Earth’s climate changes. Quantifying forest vulnerability to disturbances and understanding the underlying mechanisms is crucial to develop mitigation and adaptation strategies. However, observational evidence is largely missing at regional to continental scales. Here, we quantify the vulnerability of European forests to fires, windthrows and insect outbreaks during the period 1979–2018 by integrating machine learning with disturbance data and satellite products. We show that about 33.4 billion tonnes of forest biomass could be seriously affected by these disturbances, with higher relative losses when exposed to windthrows (40%) and fires (34%) compared to insect outbreaks (26%). The spatial pattern in vulnerability is strongly controlled by the interplay between forest characteristics and background climate. Hotspot regions for vulnerability are located at the borders of the climate envelope, in both southern and northern Europe. There is a clear trend in overall forest vulnerability that is driven by a warming-induced reduction in plant defence mechanisms to insect outbreaks, especially at high latitudes. Natural disturbances imperil healthy and productive forests, but quantifying their effects at large scales is challenging. Here the authors apply machine learning to disturbance records and satellite data to quantify and map European forest vulnerability to fires, windthrows, and insect outbreaks through 1979-2018.
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影响因子：
13.5
作者：
Gorelick, Noel;Hancher, Matt;Moore, Rebecca
通讯作者：
Moore, Rebecca
影响因子：
2.2
作者：
Boone, Celia K.;Aukema, Brian H.;Raffa, Kenneth F.
通讯作者：
Raffa, Kenneth F.
影响因子：
56.9
作者：
Hanson, Michael A.;Roth, Christopher B.;Stevens, Raymond C.
通讯作者：
Stevens, Raymond C.
影响因子：
3
作者：
Fernandes, Paulo M.
通讯作者：
Fernandes, Paulo M.
DOI：
10.1080/10618600.2014.907095
发表时间：
2015-01-02
影响因子：
2.4
作者：
Goldstein, Alex;Kapelner, Adam;Pitkin, Emil
通讯作者：
Pitkin, Emil