Evaluating the responses of net primary productivity and carbon use efficiency of global grassland to climate variability along an aridity gradient

Liu, Yangyang; Yang, Young-Hoon; Wang, Qian; Du, Xingfan; Li, Jianlong; Gang, Chengcheng; Zhou, Wei; Wang, Zhaoqi

doi:10.1016/j.scitotenv.2018.10.295

Cited by 90 publications

(40 citation statements)

References 39 publications

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“…8B). This result is also consistent with the study of Liu et al (2019b), who found that global grassland NPP was positively correlated with the AP in arid and semi-arid regions. Our results are also in line with the finding of , who suggested that water is often considered the most important factor, and many vegetational limitations such as the low LAI and high root-to-shoot ratio might be induced result from the water scarcity, which could severely limit the grassland productivity in arid/ semiarid regions (Chen et al, 2016;Liu et al, 2019c).…”

Section: The Responses Of Grassland Npp To Climate Changesupporting

confidence: 93%

“…9), which can be attributed to decrease of photosynthetic activity in grassland and the increase in decomposition of soil organic matter that was caused by increased precipitation (Hilker et al, 2014;Xu et al, 2016). This study found that the significant positive correlations between NPP and AMT were mainly occurred in the most humid/semi-humid regions of the Northern Hemisphere (Liu et al, 2019b), where the cli-mate features are warm and wet, and most of regions experienced an increasing NPP and AP ( Fig. 5 and 8B), but grassland NPP was negatively correlated with the AMT in most of the arid/semi-arid regions (i.e.…”

Section: The Responses Of Grassland Npp To Climate Changementioning

confidence: 70%

“…The coordinate system and projection were the same as remote sensing data. The annual mean temperature (AMT) and annual precipitation (AP) were calculated from the monthly data over the study period by using ArcGIS V10.3 (ESRI, California, USA) (Gang et al, 2016b;Liu et al, 2019b).…”

Section: Meteorological Datamentioning

confidence: 99%

“…The spatial distribution of correlation coefficients between global grassland NPP and climate factors were calculated by using the following formula (Liu et al, 2019b):…”

Section: Correlation Analysis Between Grassland Npp and Climate Factorsmentioning

confidence: 99%

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Assessing the Dynamics of Grassland Net Primary Productivity in Response to Climate Change at the Global Scale

et al. 2019

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Understanding the net primary productivity (NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach (CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia (1737.23 × 10 4 km 2), while the grassland area in Europe was relatively small (202.83 × 10 4 km 2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas (560.10 g C/(m 2 •yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m 2 •yr). The relationship between grassland NPP and annual mean temperature and annual precipitation (AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.

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Section: The Responses Of Grassland Npp To Climate Changesupporting

confidence: 93%

Section: The Responses Of Grassland Npp To Climate Changementioning

confidence: 70%

Section: Meteorological Datamentioning

confidence: 99%

“…The spatial distribution of correlation coefficients between global grassland NPP and climate factors were calculated by using the following formula (Liu et al, 2019b):…”

Section: Correlation Analysis Between Grassland Npp and Climate Factorsmentioning

confidence: 99%

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Assessing the Dynamics of Grassland Net Primary Productivity in Response to Climate Change at the Global Scale

et al. 2019

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“…The possible reason lies in that the parameters applied to calculate the temperature sensitivity of R a in MODIS are relatively high (Ise et al., 2010). Numerous studies have reported linear and negative processes in warming experiments (Tucker et al., 2013), model estimations (Sinsabaugh et al., 2017) and MODIS observations (Y. Liu, Yang, et al., 2019; Y. Zhang et al., 2014). Additionally, field experiments found that the CUEc and CUEe were unaffected by warming (Drake et al., 2019; G. Li, Han, et al., 2017).…”

Section: Discussionmentioning

confidence: 99%

Multiple‐scale negative impacts of warming on ecosystem carbon use efficiency across the Tibetan Plateau grasslands

Chen

Zhang

Zhu

et al. 2020

Global Ecol. Biogeogr.

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Aim Ecosystem carbon use efficiency (CUEe) is a core parameter of ecosystem process models, but its relationships with climate are still uncertain, especially for ecosystems with harsh environments. Large inconsistencies in climate impacts on the CUEe have been reported among various spatial scales. The goal of this study was to examine whether warming promotes or restricts the CUEe and whether the CUEe responds to a warming gradient in a linear or nonlinear manner. Location Tibetan Plateau. Time period 2000–2018. Major taxa studied Alpine grassland ecosystem. Methods We integrated multiple‐source data of carbon fluxes and CUEe, including warming experiments at a site scale, eddy covariance observations at a landscape scale and synthesized warming experiments and ecosystem process models at a regional scale. Next, we deployed a statistical model to examine the warming impacts on the CUEe across scales; the effects of biotic and abiotic factors on the CUEe and its components were summarized based on the results of standardized major axis tests and routines, structural equation modelling and nonlinear models. Results This study reported a suppressive warming impact on the CUEe, which followed a nonlinear curve with severe inhibition in the high‐level warming treatment. With a warming threshold of 1.5–2.0°C, CUEe response patterns transitioned from no change to a significant decrease. The restriction effects can be ascribed to the joint adverse and asymmetric effects of warming on CUEe components under multiple‐level warming. Warming‐modified relationships among CUEe components and the nonlinear effects of biotic and abiotic factors led to the nonlinear responses of CUEe to warming. Main conclusions This study revealed suppressive and nonlinear effects of warming on the CUEe, including especially dramatic CUEe decreases with high‐level warming. These findings are critical for optimizing model parameters and improving predictions of the carbon sequestration capacity of alpine grasslands.

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Untangling the effects of management measures, climate and land use cover change on grassland dynamics in the Qinghai–Tibet Plateau, China

et al. 2021

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Multiple large-scale ecological conservation programmes have been launched by the Chinese Government to combat grassland degradation across the Qinghai-Tibet Plateau (QTP) since 1999. However, the mechanisms by which these ecological initiatives might affect grassland changes remains unknown. Here, we examined the land use cover change (LUCC) and net primary productivity (NPP) of the grassland in the QTP in 2001-2016. The effects of LUCC, management measures, and climate variation on grassland NPP were quantitatively evaluated. The grassland area had a net increase of 96,397 km 2 , which could be attributed to the conversion of desert and forests. The total NPP of grassland increased by 154,073.7 GgCÁyr À1 in 2001-2016, of which the contribution of climate and human factors occupied 35.58% and 64.42%, respectively. Specifically, climate variation caused an overall increase of 54,823.24 GgCÁyr À1 in grassland total NPP. The climate-dominated NPP decrease was widely found in the eastern and southern QTP. The contribution of LUCC and management measures to the increase of grassland NPP was 62.78% and 37.22%, respectively. This increase was mostly found in the mid-east region of the QTP, which could sufficiently counteract the negative effect of climate variation on NPP.Overall, these findings seem to demonstrate that these ecological restoration programs have substantially promote the increase of grassland NPP and contribute to understanding the degree to which the grassland ecosystem may respond to the LUCC and management measures under the ecological restoration projects, which are essential to developing more effective ecological measures for future grassland restoration in the QTP.

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Evaluating the responses of net primary productivity and carbon use efficiency of global grassland to climate variability along an aridity gradient

Cited by 90 publications

References 39 publications

Assessing the Dynamics of Grassland Net Primary Productivity in Response to Climate Change at the Global Scale

Assessing the Dynamics of Grassland Net Primary Productivity in Response to Climate Change at the Global Scale

Multiple‐scale negative impacts of warming on ecosystem carbon use efficiency across the Tibetan Plateau grasslands

Untangling the effects of management measures, climate and land use cover change on grassland dynamics in the Qinghai–Tibet Plateau, China

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