Variable silicon accumulation in plants affects terrestrial carbon cycling by controlling lignin synthesis

2017 ◽  
Vol 24 (1) ◽  
pp. e183-e189 ◽  
Author(s):  
Thimo Klotzbücher ◽  
Anika Klotzbücher ◽  
Klaus Kaiser ◽  
Doris Vetterlein ◽  
Reinhold Jahn ◽  
...  
2016 ◽  
Vol 413 (1-2) ◽  
pp. 1-25 ◽  
Author(s):  
Claire L. Phillips ◽  
Ben Bond-Lamberty ◽  
Ankur R. Desai ◽  
Martin Lavoie ◽  
Dave Risk ◽  
...  

2020 ◽  
Vol 13 (12) ◽  
pp. 787-793
Author(s):  
Peter B. Reich ◽  
Sarah E. Hobbie ◽  
Tali D. Lee ◽  
Roy Rich ◽  
Melissa A. Pastore ◽  
...  

2018 ◽  
Vol 31 (7) ◽  
pp. 2833-2851 ◽  
Author(s):  
Sha Zhou ◽  
Junyi Liang ◽  
Xingjie Lu ◽  
Qianyu Li ◽  
Lifen Jiang ◽  
...  

Terrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land–Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Jian Song ◽  
Jingyi Ru ◽  
Mengmei Zheng ◽  
Haidao Wang ◽  
Yongge Fan ◽  
...  

Abstract Numerous ecosystem manipulative experiments have been conducted since 1970/80 s to elucidate responses of terrestrial carbon cycling to the changing atmospheric composition (CO2 enrichment and nitrogen deposition) and climate (warming and changing precipitation regimes), which is crucial for model projection and mitigation of future global change effects. Here, we extract data from 2,242 publications that report global change manipulative experiments and build a comprehensive global database with 5,213 pairs of samples for plant production (productivity, biomass, and litter mass) and ecosystem carbon exchange (gross and net ecosystem productivity as well as ecosystem and soil respiration). Information on climate characteristics and vegetation types of experimental sites as well as experimental facilities and manipulation magnitudes subjected to manipulative experiments are also included in this database. This global database can facilitate the estimation of response and sensitivity of key terrestrial carbon-cycling variables under future global change scenarios, and improve the robust projection of global change‒terrestrial carbon feedbacks imposed by Earth System Models.


2015 ◽  
Vol 10 (8) ◽  
pp. 089501 ◽  
Author(s):  
Anders Ahlström ◽  
Jianyang Xia ◽  
Almut Arneth ◽  
Yiqi Luo ◽  
Benjamin Smith

2017 ◽  
Vol 1 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Yanchun Liu ◽  
Qing Shang ◽  
Zhongwei Wang ◽  
Kesheng Zhang

Water availability is one of the fundamental drivers for biological activities and terrestrial carbon cycling. Although the response of soil respiration to precipitation has been well documented in arid and semiarid ecosystems, our understanding of its pattern in forests is rather limited. This study was conducted to examine the difference of precipitation effect on soil respiration under different canopy conditions in a temperate coniferous (Pinus armandii Franch) and broadleaved (Quercus aliena var. acuteserrata) mixed forest in Central China. The results showed that precipitation significantly reduced soil temperature, but increased soil volumetric water content and soil respiration (6.0%-35.3%). Precipitation caused a greater increment in soil respiration beneath the canopy of broadleaved trees (24.2%) than that beneath coniferous ones (13.5%). Precipitation-induced increase in soil respiration was consistently lower beneath the canopy of small size classes (7.1%-32.6%) than large size classes (9.5%-33.3%). Mean soil respiration of forest gaps increased 22.4% following precipitations. Our study highlights the positive response of soil respiration to precipitation pulses in water-unlimited ecosystems. The findings suggest that the spatial heterogeneity of soil respiration to precipitation pulse under different canopy conditions should be emphasized while assessing terrestrial carbon cycling and its feedback to climate change.


2020 ◽  
Author(s):  
Andreas Krause ◽  
Almut Arneth ◽  
Anja Rammig

<p>The carbon balance of terrestrial ecosystems is determined by environmental drivers (chiefly related to climate and land use) which interact with each other and change over time. In particular, ecosystems are presently still affected by past environmental changes because they have not yet reached equilibrium with their environment. However, the magnitude and drivers of this legacy effect for the upcoming decades are still unclear. Here, we use the dynamic global vegetation model LPJ-GUESS to calculate the effects of historical (1850-2015) and future (2015-2099, exemplarily for the high emission/moderate deforestation scenario SSP5-8.5) environmental changes on historical and future terrestrial carbon cycling and to quantify the contributions of the following environmental drivers: climate change, CO<sub>2 </sub>fertilization, agricultural expansion, shifting cultivation frequency, wood harvest, nitrogen deposition, and nitrogen fertilization.</p><p>According to our simulations, the land represented a cumulative net carbon source (-154 GtC) over the historical period mainly due to deforestation, wood harvest, and negative climate change impacts partly offset by carbon uptake via increased CO<sub>2</sub> levels and nitrogen input. In contrast, the land is simulated to act as a net carbon sink (+118 GtC) over the 21<sup>st</sup> century. This is mostly a result of historical environmental changes as ecosystems still adapt to present-day CO<sub>2</sub> and nitrogen availability as well as long-term vegetation regrowth following agricultural abandonment and wood harvest. The net impact of future environmental changes on future carbon cycling is much smaller because effects from individual environmental drivers largely compensate. Historical environmental changes dominate future terrestrial carbon cycling at least until mid-century when legacy effects gradually diminish and future environmental changes start to trigger carbon accumulation. Our results suggest that legacy effects persist even many decades after environmental changes occurred and need to be considered when interpreting alterations of the terrestrial carbon cycle.</p>


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