Ecosystem carbon use efficiency in ecologically vulnerable areas in China: Variation and influencing factors

Abstract

Ecologically vulnerable areas (EVAs) are regions with ecosystems that are fragile and vulnerable to degradation under external disturbances, e.g., environmental changes and human activities. A comprehensive understanding of the climate change characteristics of EVAs in China is of great guiding significance for ecological protection and economic development. The ecosystem carbon use efficiency (CUEe) can be defined as the ratio of the net ecosystem productivity (NEP) to gross primary productivity (GPP), one of the most important ecological indicators of ecosystems, representing the capacity for carbon transfer from the atmosphere to a potential ecosystem carbon sink. Understanding the variation in the CUEe and its controlling factors is paramount for regional carbon budget evaluation. Although many CUEe studies have been performed, the spatial variation characteristics and influencing factors of the CUEe are still unclear, especially in EVAs in China. In this study, we synthesized 55 field measurements (3 forestland sites, 37 grassland sites, 6 cropland sites, 9 wetland sites) of the CUEe to examine its variation and influencing factors in EVAs in China. The results showed that the CUEe in EVAs in China ranged from -0.39 to 0.67 with a mean value of 0.20. There were no significant differences in the CUEe among different vegetation types, but there were significant differences in CUEe among the different EVAs (agro-pastoral ecotones < Tibetan Plateau < arid and semiarid areas < Loess Plateau). The CUEe first decreased and then increased with increasing mean annual temperature (MAT), soil pH and soil organic carbon (SOC) and decreased with increasing mean annual precipitation (MAP). The most important factors affecting the CUEe were biotic factors (NEP, GPP, and leaf area index (LAI)). Biotic factors directly affected the CUEe, while climate (MAT and MAP) and soil factors (soil pH and SOC) exerted indirect effects. The results illustrated the comprehensive effect of environmental factors and ecosystem attributes on CUEe variation, which is of great value for the evaluation of regional ecosystem functions.

Variation in and Regulation of Carbon Use Efficiency of Grassland Ecosystem in Northern China

“…In contrast, the grassland ecosystems in those regions benefit from favorable hydrothermal conditions and are often intensively managed or artificially planted, contributing to their higher carbon sink capacity [73][74][75][76]. Additionally, the average CUE value in our study is lower than that reported for other ecosystem types in China, such as forests and farmlands [13,15,77]. Our study primarily concentrates on the grasslands situated in Northern China, where vegetation primarily thrives in cold or arid regions like the Qinghai-Tibet Plateau, northeastern regions, the Loess Plateau, and the Inner Mongolia Plateau.…”

“…The carbon flux (NEP and GPP) data were collected from peer-reviewed papers from ISI Web of Science (http://apps.webofknowledge.com (accessed on 19 May 2024)), the China National Knowledge Infrastructure (http://www.cnki.net (accessed on 19 May 2024)), and shared datasets from the National Tibetan Plateau Data Center (https://www.tpdc.ac.cn (accessed on 20 May 2024)), the Science Data Bank (https://www.scidb.cn (accessed on 19 May 2024)), and the Scientific Data Center of CAS (https://www.casdc.cn (accessed on 23 March 2024)), using "Eddy covariance", "Carbon flux", "productivity", "gross primary productivity", "net ecosystem productivity", "net ecosystem exchange (NEE)", "Grassland", and "China" as keywords. To avoid bias in the publication selection, we only selected the literature and datasets that satisfied the following three criteria: (1) the NEP and GPP values were measured using the eddy covariance technique; (2) the data were continuously observed for at least one year; (3) coordinate rotation, WPL correction, night flux calculation, and other adjustments were applied to calibrate the data [13].…”

“…A high CUE indicates that the ecosystem releases less carbon to the atmosphere, which can promote carbon stability and long-term sequestration, as opposed to low CUE ecosystems where more of the carbon fixed by photosynthesis is released in the form of CO2 or other forms and where the carbon cycle is more open [6][7][8]. Understanding the variation in CUE, particularly across different environmental conditions, is crucial for analyzing patterns of carbon fluxes and allocation [9] and key to gaining insights into the potential for carbon sequestration in terrestrial ecosystem and its feedback to climate change [8,[10][11][12][13][14].…”

“…Some previous studies have suggested that CUE values remain constant [9,15]. Recently, more and more studies have shown significant spatial differences in CUE across various ecosystems and hydrothermal conditions [12][13][14][15][16][17][18]. Studies have compared CUE across different ecosystem types, with some suggesting higher CUE in grassland and farmland compared to forest and shrub [6,17], while others indicate similar or even lower CUE in grassland compared to forest, cropland, and wetland [2,18].…”

“…On global and regional scales, CUE tends to decrease with increasing temperature [6,19,20], but CUE in eastern U.S. forests increases with increasing temperature [21]. The results of some studies indicate that CUE is positively correlated with precipitation [6,20,21], while others suggest that CUE is negatively correlated with precipitation [13]. In addition, CUE is also influenced by biological factors such as leaf area index, leaf habit, NEP, and GPP [22][23][24].…”