Inferring the effects of partial defoliation on the carbon cycle from forest structure: challenges and opportunities
Inferring the effects of partial defoliation on the carbon cycle from forest structure: challenges and opportunities
复制标题
从森林结构推断部分落叶对碳循环的影响:挑战与机遇
DOI:
10.1088/1748-9326/ac46e9
复制
发表时间:
2022
影响因子:
6.7
通讯作者:
Tallant, Jason M
中科院分区:
文献类型:
--
作者:
Gough, Christopher M;Foster, Jane R;Bond-Lamberty, Ben;Tallant, Jason M
Disturbances are increasing in forests worldwide (McDowell et al 2015) and, in some regions, disturbance regimes are shifting to include a greater number of partial defoliation events that differ in timing, duration, distribution, and extent of leaf loss (Cohen et al 2016, figure 1). The global area already affected by defoliation is immense. For example, biotic disturbances impact 44 million hectares or 3% of the total forestland worldwide on an annual basis (Kautz et al 2017). Rather than causing complete and immediate tree mortality across entire landscapes, biotic disturbances from insects and pathogens and disturbances from drought and extreme heat are often spatially diffuse and slow-acting, making their effects on the carbon (C) cycle uncertain, variable, and difficult to predict (Amiro et al 2010). This uncertainty is important: the impacts of partial defoliation scale nonlinearly with C cycling processes (Medvigy et al 2012) and, in some regions, exert a fundamental control on landscape-level C balance (Clark et al 2010). As a result, partial defoliation-C cycling interactions represent a key knowledge gap relevant to ecological forecasting, remote sensing, and disturbance ecologists (Hicke et al 2012).In this perspective, we highlight current challenges and emerging opportunities for improving the characterization of partial defoliation and inferring its effects on ecosystem-to-landscape C cycling processes from observations of forest structure. As members of FLUXNET, a global network of eddy-covariance C flux towers, we emphasize the tower footprint scale, which typically encompasses tens of hectares. We focus on ground and remote sensing tools used to characterize disturbance, because field inventories and satellite data are commonly used to infer
登录
查看更多内容
影响因子:
13.5
作者:
Cho‐ying Huang;W. Anderegg;G. Asner
通讯作者:
Cho‐ying Huang;W. Anderegg;G. Asner
影响因子:
9.8
作者:
Pastorello, Gilberto;Trotta, Carlo;Papale, Dario
通讯作者:
Papale, Dario
DOI:
10.1098/rstb.2010.0102
发表时间:
2010-10-12
影响因子:
6.3
作者:
Richardson, Andrew D.;Black, T. Andy;Varlagin, Andrej
通讯作者:
Varlagin, Andrej
影响因子:
11.6
作者:
通讯作者:
--
影响因子:
2.7
作者:
Atkins, Jeff W.;Bond-Lamberty, Ben;Gough, Christopher M.
通讯作者:
Gough, Christopher M.