Regulation of priming effect by soil organic matter stability over a broad geographic scale

Abstract

The modification of soil organic matter (SOM) decomposition by plant carbon (C) input (priming effect) represents a critical biogeochemical process that controls soil C dynamics. However, the patterns and drivers of the priming effect remain hidden, especially over broad geographic scales under various climate and soil conditions. By combining systematic field and laboratory analyses based on multiple analytical and statistical approaches, we explore the determinants of priming intensity along a 2200 km grassland transect on the Tibetan Plateau. Our results show that SOM stability characterized by chemical recalcitrance and physico-chemical protection explains more variance in the priming effect than plant, soil and microbial properties. High priming intensity (up to 137% of basal respiration) is associated with complex SOM chemical structures and low mineral-organic associations. The dependence of priming effect on SOM stabilization mechanisms should be considered in Earth System Models to accurately predict soil C dynamics under changing environments.

Geomorphology as a Driver of Heavy Metal Accumulation Patterns in a Floodplain

“…The organic matter was the significant explanatory variable which determined the concentration of macro elements (RC1) in the GLM. This connection is confirmed by several authors [86,87] as Fe, Al, and Ca are key regulators of soil organic matter stability. However, pairwise correlations only varied between 0.3 and 0.5: it is known that floodplains do not provide favorable locations for humus formation; usually the first stages of transformation, the fulvic acids, are dominant, and these have an acidic character, few binding locations and poor binding capacity [88].…”

Deciphering Linkages Between Microbial Communities and Priming Effects in Lake Sediments With Different Salinity

“…(2) salinity affects the interactions (e.g., cooperation) among microbial species (Egamberdieva et al, 2017;Ji et al, 2019;Liu et al, 2017) and thus influences the PE intensity through microbial co-metabolisms; and (3) salinity affects the composition of microbial communities that are involved OM mineralization, thereby influencing the PE intensity. In addition, nutritionrelated factors (e.g., TDN, TDP, and C:N ratios) have previously been shown to regulate positive PE in soils (Chen et al, 2018(Chen et al, , 2019Perveen et al, 2019;Razanamalala et al, 2017Razanamalala et al, , 2018. Therefore, it is reasonable to observe the influence of nutrition-related factors on the PE intensity in the studied lake sediments.…”

“…The influence of salinity on the PE intensity could be ascribed to the following reasons: (1) salinity is one key factor limiting the production of microbial enzymes that take effects in decomposition of OMs (Oren, 2011; Reed & Martiny, 2013) and thus affects the PE intensity through microbial stoichiometric decomposition; (2) salinity affects the interactions (e.g., cooperation) among microbial species (Egamberdieva et al, 2017; Ji et al, 2019; Liu et al, 2017) and thus influences the PE intensity through microbial co‐metabolisms; and (3) salinity affects the composition of microbial communities that are involved OM mineralization, thereby influencing the PE intensity. In addition, nutrition‐related factors (e.g., TDN, TDP, and C:N ratios) have previously been shown to regulate positive PE in soils (Chen et al, 2018, 2019; Perveen et al, 2019; Razanamalala et al, 2017, 2018). Therefore, it is reasonable to observe the influence of nutrition‐related factors on the PE intensity in the studied lake sediments.…”

Asymmetric response of soil methane uptake rate to land degradation and restoration: Data synthesis

“…Dry and wet regions were divided into four groups based on different levels of MAP with the semi-arid and arid regions (MAP < 400 mm), semi-humid region (MAP: 400-800 mm), humid region (MAP: 800-1600 mm), and extremely humid region (MAP > 1,600 mm; Zheng, Yin, & Li, 2010). We recorded the soil pH of the control and treatment sites and assigned alkaline (pH > 8), neutral (pH 6-8), acidic (pH 4-6), and strongly acidic The first component (PC1), which explained 46.5%-84.2% of the total variance for these three groups, was then introduced as a new variable into the subsequent analysis (Chen et al, 2016(Chen et al, , 2019. The maximum-likelihood estimation method was applied to fit the model.…”