Temperature Sensitivity of Soil Organic Carbon Mineralization along an Elevation Gradient in the Wuyi Mountains, China

Wang, Guobing; Zhou, Yan; Xu, Xia; Ruan, Honghua; JiaShe, Wang

doi:10.1371/journal.pone.0053914

Cited by 57 publications

(76 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Soil organic matter decomposition is affected by the climate (Schimel et al, 1994;Wang et al, 2013), the initial quality and quantity of SOM (Wild et al, 2014;Holden et al, 2015), and soil microbial characteristics (Fang et al, 2005b;Baumann et al, 2013). However, these factors might interact with each other, making it difficult to discern factors that covary in the process of interest (Colman & Schimel, 2013).…”

Section: Introductionmentioning

confidence: 99%

Regional variation in the temperature sensitivity of soil organic matter decomposition in China's forests and grasslands

Liu

Zhu

et al. 2017

Global Change Biology

112

View full text Add to dashboard Cite

How to assess the temperature sensitivity (Q ) of soil organic matter (SOM) decomposition and its regional variation with high accuracy is one of the largest uncertainties in determining the intensity and direction of the global carbon (C) cycle in response to climate change. In this study, we collected a series of soils from 22 forest sites and 30 grassland sites across China to explore regional variation in Q and its underlying mechanisms. We conducted a novel incubation experiment with periodically changing temperature (5-30 °C), while continuously measuring soil microbial respiration rates. The results showed that Q varied significantly across different ecosystems, ranging from 1.16 to 3.19 (mean 1.63). Q was ordered as follows: alpine grasslands (2.01) > temperate grasslands (1.81) > tropical forests (1.59) > temperate forests (1.55) > subtropical forests (1.52). The Q of grasslands (1.90) was significantly higher than that of forests (1.54). Furthermore, Q significantly increased with increasing altitude and decreased with increasing longitude. Environmental variables and substrate properties together explained 52% of total variation in Q across all sites. Overall, pH and soil electrical conductivity primarily explained spatial variation in Q . The general negative relationships between Q and substrate quality among all ecosystem types supported the C quality temperature (CQT) hypothesis at a large scale, which indicated that soils with low quality should have higher temperature sensitivity. Furthermore, alpine grasslands, which had the highest Q , were predicted to be more sensitive to climate change under the scenario of global warming.

show abstract

Section: Introductionmentioning

confidence: 99%

Regional variation in the temperature sensitivity of soil organic matter decomposition in China's forests and grasslands

Liu

Zhu

et al. 2017

Global Change Biology

112

View full text Add to dashboard Cite

show abstract

“…Only a subset of them is explicitly expressed, while the majority is implicitly embedded in the C cycle models. Empirical studies have suggested that temperature, moisture, litter quality, and soil texture are primary factors that control C transformation processes of decomposition and stabilization (Burke et al, 1989;Adair et al, 2008;Zhang et al, 2008;Xu et al, 2012;Wang et al, 2013). Nitrogen influences C cycle processes mainly through changes in photosynthetic C input, C partitioning, and decomposition.…”

Section: Assumptions Of the C Cycle Models And Validity Of This Analysismentioning

confidence: 99%

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

et al. 2017

View full text Add to dashboard Cite

Abstract. Terrestrial ecosystems have absorbed roughly 30 % of anthropogenic CO 2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is timedependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times.Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a threedimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationPublished by Copernicus Publications on behalf of the European Geosciences Union. 146Y. Luo et al.: Land carbon storage dynamics ally feasible so that both C flux-and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.

show abstract

“…Q 10 evaluation is usually based on instantaneous CO 2 flux rates (Chang et al, 2012;Fang and Moncrieff, 2001;Karhu et al, 2014;Sun et al, 2016;Waldrop et al, 2010), time spans needed for an identical amount of C mineralization (Bracho et al, 2016;Wang et al, 2013;Zhu and Cheng, 2011), cumulative C flux over a given time period (Conant et al, 2008;Hamdi et al, 2013;Podrebarac et al, 2016), or microbial activity as a function of temperature (Diakova et al, 2016;Karhu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…However, the Q 10 values derived from various experimental methods can be confounded by the different qualities and retention times of the co-existing labile and recalcitrant components (Conant et al, 2008;Hamdi et al, 2013;Nakajima et al, 2016;Wang et al, 2013). For instance, the "equal-time" approach always leads to great discrepancies in the degradation of fast-vs. slow-turnover C pools among samples along with increasing incubation temperature and time, while the "equal-C" approach can underestimate the contribution of more recalcitrant substrates (Hamdi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Q10 values vary with different kinetic properties of C mineralization

Bai

Lin

et al. 2017

Pedobiologia

View full text Add to dashboard Cite

A B S T R A C TTemperature response quotient (Q 10 ) is a critical parameter for evaluating global additional carbon (C) release with climate change. However, its value is usually derived from time span or instantaneous rate or cumulative amount of C flux, giving a very one-sided account of thermal sensitivity of C cycling. Through a 117-day laboratory incubation study, we estimated Q 10 values simultaneously with the labile (a 0 ) and recalcitrant C proportions and their rate constants, and then tested for any variances of these kinetic properties in different vegetation stands, soil horizons, aeration statuses, and thermal settings (i.e., diurnally-varying, constant low and constant high temperatures). A regularly varying temperature regime increased the exploitation of labile C resources (i.e., high a 0 ) and required longer time spans (i.e., low rate constants). The constant high temperature induced the exhaustive depletion of the labile C pool and motivated a very rapid and short-term C mineralization process. The constant low temperature treatment was characterized by the lowest a 0 but by medium rate constants because low temperature slowed the C mineralization processes but retained high level of the original C processing diversity. Therefore, a 0 and the rate constants showed discrepancies in their temperature sensitivities as revealed by pairwise comparisons of temperature regimes. Such discrepancies were also supported by pairwise comparisons of aeration statuses, forest stands and soil horizons. The Q 10 bias between C mineralization a 0 and rate constants in this laboratory experiment is attributed to the inherently distinct properties of these two parameters, as a 0 and its Q 10 are closely correlated with the sizes of the easily available C pool, while rate constants and their Q 10 variances explain the temporal scale of the same C mineralization process. Our findings suggest a combined application of a 0 and rate constants for exploring the temperature sensitivity of C mineralization in future studies.

show abstract

Temperature Sensitivity of Soil Organic Carbon Mineralization along an Elevation Gradient in the Wuyi Mountains, China

Cited by 57 publications

References 49 publications

Regional variation in the temperature sensitivity of soil organic matter decomposition in China's forests and grasslands

Regional variation in the temperature sensitivity of soil organic matter decomposition in China's forests and grasslands

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

Q10 values vary with different kinetic properties of C mineralization

Contact Info

Product

Resources

About