Biological and mineralogical controls over cycling of low molecular weight organic compounds along a soil chronosequence

Mcfarland, J. W.; Waldrop, Mark P.; Strawn, Daniel G.; Creamer, Courtney A.; Lawrence, C. R.; Haw, Monica

doi:10.1016/j.soilbio.2019.01.013

Cited by 21 publications

(5 citation statements)

References 63 publications

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“…Biochemical quality also regulates the incorporation of plant inputs into MAOM. Litter with a lower C:N ratio typically forms MAOM most efficiently, either through microbial transformation or through direct sorption to mineral surfaces (Cyle et al., 2016; McFarland et al., 2019). Thus, plants that produce litter with relatively lower C:N ratios—such as those that associate with N‐fixing bacteria, or those that invest less in structural compounds like lignin—are associated with the greatest MAOM formation efficiencies (Lavallee et al., 2018).…”

Section: Plant and Microbial Traits Influencing Mineral‐associated Or...mentioning

confidence: 99%

Global distribution, formation and fate of mineral‐associated soil organic matter under a changing climate: A trait‐based perspective

et al. 2022

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Soil organic matter (SOM) is the largest actively cycling reservoir of terrestrial carbon (C), and the majority of SOM in Earth's mineral soils (~65%) is mineral‐associated organic matter (MAOM). Thus, the formation and fate of MAOM can exert substantial influence on the global C cycle. To predict future changes to Earth's climate, it is critical to mechanistically understand the processes by which MAOM is formed and decomposed, and to accurately represent this process‐based understanding in biogeochemical and Earth system models. In this review, we use a trait‐based framework to synthesize the interacting roles of plants, soil micro‐organisms, and the mineral matrix in regulating MAOM formation and decomposition. Our proposed framework differentiates between plant and microbial traits that influence total OM inputs to the soil (‘feedstock traits’) versus traits that influence the proportion of OM inputs that are ultimately incorporated into MAOM (‘MAOM formation traits’). We discuss how these feedstock and MAOM formation traits may be altered by warming, altered precipitation and elevated carbon dioxide. At a planetary scale, these feedstock and MAOM formation traits help shape the distribution of MAOM across Earth's biomes, and modulate biome‐specific responses of MAOM to climate change. We leverage a global synthesis of MAOM measurements to provide estimates of the total amount of MAOM‐C globally (~840–1540 Pg C; 34%–51% of total terrestrial organic C), and its distribution across Earth's biomes. We show that MAOM‐C concentration is highest in temperate forests and grasslands, and lowest in shrublands and savannas. Grasslands and croplands have the highest proportion of soil organic carbon (SOC) in the MAOM fraction (i.e. the MAOM‐C:SOC ratio), while boreal forests and tundra have the lowest MAOM‐C:SOC ratio. Drawing on our trait framework, we then review experimental data and posit the effects of climate change on MAOM pools in different biomes. We conclude by discussing how MAOM is integrated into soil C models, and how feedstock and MAOM formation traits may be included in these models. We also summarize the projected fate of MAOM under climate change scenarios (Representative Concentration Pathways 4.5 and 8.5) and discuss key model uncertainties. Read the free Plain Language Summary for this article on the Journal blog.

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Section: Plant and Microbial Traits Influencing Mineral‐associated Or...mentioning

confidence: 99%

Global distribution, formation and fate of mineral‐associated soil organic matter under a changing climate: A trait‐based perspective

et al. 2022

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“…Different superscript letters indicate significant differences among sites using a two‐way ANOVA followed by a Tukey HSD test. Flux data were fitted to the equation: C resp = A o (1−e −kt ) where C resp is the cumulative carbon respired up to time, t (d), A o is the potentially bioavailable (180‐days labile) pool (gCkg −1 dry) of soil C, and k is the instantaneous rate constant describing the daily release of C from that pool (McFarland et al., 2019).…”

Section: Resultsmentioning

confidence: 99%

“…where C resp is the cumulative carbon respired up to time, t (d), A o is the potentially bioavailable (180-days labile) pool (gCkg −1 dry) of soil C, and k is the instantaneous rate constant describing the daily release of C from that pool (McFarland et al, 2019). not translate to increased summer diffusive CH 4 fluxes however, likely due to enhanced CH 4 oxidation, or the dominance of ebullition over diffusive flux.…”

Section: Data Availability Statementmentioning

confidence: 99%

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Carbon Fluxes and Microbial Activities From Boreal Peatlands Experiencing Permafrost Thaw

et al. 2021

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Permafrost in northern latitude ecosystems is becoming increasingly susceptible to climate warming, with strong implications for changes in carbon (C) cycling (Biskaborn et al., 2019; Bush and Lemmen 2019). Much of the surface permafrost temperature in interior Alaska is just below the freezing point of water (Romanovsky et al., 2010), such that it is nearing a phase change. Models predict that interior Alaska, southern Canada, and southern Siberia will experience widespread loss of surface permafrost (top 1-2 m) this century (Schaefer et al., 2011), vastly transforming ecosystems and disturbance regimes (Lara et al., 2016). This prediction is also significant because permafrost soils contain large quantities of C that could be released as CO 2 or CH 4 to the atmosphere, thereby creating a positive feedback to climate warming (McGuire Abstract Permafrost thaw in northern ecosystems may cause large quantities of carbon (C) to move from soil to atmospheric pools. Because soil microbial communities play a critical role in regulating C fluxes from soils, we examined microbial activity and greenhouse gas production soon after permafrost thaw and ground collapse (into collapse-scar bogs), relative to the permafrost plateau or older thaw features. Using multiple field and laboratory-based assays at a field site in interior Alaska, we show that the youngest collapse-scar bog had the highest CH 4 production potential from soil incubations, and, based upon temporal changes in porewater concentrations and 13 C-CH 4 and 13 C-CO 2 , had greater summer in situ rates of respiration, methanogenesis, and surface CH 4 oxidation. These patterns could be explained by greater C and N availability in the young bog, while alternative terminal electron accepting processes did not play a significant role. Field diffusive CH 4 fluxes from the young bog were 4.1 times greater in the shoulder season and 1.7-7.2 times greater in winter relative to older bogs, but not during summer. Greater relative CH 4 flux rates in the shoulder season and winter could be due to reduced CH 4 oxidation relative to summer, magnifying the importance of differences in production. Both the permafrost plateau and collapse-scar bogs were sources of C to the atmosphere due in large part to winter C fluxes. In collapse scar bogs, winter is a critical period when differences in thermokarst age translates to differences in surface fluxes. Plain Language Summary Permafrost thaw is occurring in Alaska which may result in a positive feedback to climate warming, due to the release of greenhouse gases such as CO 2 and CH 4 from soils. Here we examined greenhouse gas production along a gradient of "time since thaw," hypothesizing that fluxes and microbial activities would be highest soon after thaw, and then decline. We observed highest rates of microbial activities, particularly methanogenesis, soon after thaw, coinciding with less decomposed organic matter and higher concentrations of dissolved carbon and nitrogen in soil, possibly of permafrost origin. However, field flu...

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“…There are a number of Quaternary-based examples where chronosequence studies are useful, for example for estimating field weathering rates (element depletion and mineral transformation, e.g. Favilli et al 2009;Mavris et al 2011;Panichini et al 2017), carbon (C) sequestration rates (Kabala and Zapart 2012;Lawrence et al 2015;McFarland et al 2019), nitrogen mineralisation (Zhao et al 2013) or using soils for environmental reconstructions (Kruczkowska et al 2019). However, the inspection of the Quaternary investigation reveals that many nonlinear procedures in any chronosequence are described using component rates that are implicitly determined in its calculations, for example linear instead of nonlinear derivation.…”

Section: Introductionmentioning

confidence: 99%

Mathematical–Statistical Problem that Has a Significant Implication on Estimation of Interval-Specific Rates of Soil-Forming Processes

Matus

Egli

2019

J Soil Sci Plant Nutr

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Mathematical-statistical problem on estimation of soil-forming processes. This paper raises a mathematical-statistical problem that finally can be found in any chronosequence of a specific Quaternary-based process (e.g. soil mass, clay content accumulation). The problem arises when interval-specific rates of a component f(t) are intended by dividing the stock by the given age t. The procedure implies that only an average specific rate is calculated in a linear form, for example when soil organic carbon often tends to an asymptotic end-value. For any other parameters (e.g. sedimentation rates, soil horizons thickness), the same holds true. The mathematical-statistical problem arises if the rates are derived using a linear calculation (individual stocks, f(t) divided by time, t) and then plotted as a function of time, in circumstances where there is no correlation between t and the components f(t).To illustrate this problem, we used a random generator function where the plot t versus f(t) are not correlated. We also give examples of the chronosequence approaches of soil organic carbon and soil mass, where, at a given point, the real specific rate corresponds to the slope (first derivative) of the obtained non-linear regression function. The random generator function for the plot t versus f(t) showed no significant relationship (R 2 = 0.0), but f(t)t −1 and t showed highly significant correlation (R 2 = 0.62). The error in calculating the component rate by dividing by time instead of using the derivative function when the processes are not linear ranged between 22% and > 500%. We conclude that non-independent variables plotted in a chronosequence can infer correlations even when they might not exist. Carefully observation is needed on the dataset when the time is involved, particularly in Quaternary-based studies, since the mass component not necessarily is related with time. Avoid in calculating the change of the mass component simply dividing by time, because an over-or under-estimation from real rate (obtained by derivative function) occurs when the process under study is not linear.

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Biological and mineralogical controls over cycling of low molecular weight organic compounds along a soil chronosequence

Cited by 21 publications

References 63 publications

Global distribution, formation and fate of mineral‐associated soil organic matter under a changing climate: A trait‐based perspective

Global distribution, formation and fate of mineral‐associated soil organic matter under a changing climate: A trait‐based perspective

Carbon Fluxes and Microbial Activities From Boreal Peatlands Experiencing Permafrost Thaw

Mathematical–Statistical Problem that Has a Significant Implication on Estimation of Interval-Specific Rates of Soil-Forming Processes

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