Optimal metabolic regulation along resource stoichiometry gradients

Manzoni, Stefano; Čapek, Petr; Mooshammer, Maria; Lindahl, Björn D.; Richter, Andreas; Šantrůčková, Hana

doi:10.1111/ele.12815

Cited by 152 publications

(127 citation statements)

References 51 publications

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“…C:N, C:P, and C:K values of leaf fall and reproductive fall litter are indeed well within the range of observed variability (Holland et al, ), but the 20 analyzed locations span a much lower range than observations (Figure S4). The C:N and C:P ratios are also well within the observed range of woody and leaf litter chemistry composition (data summarized in Manzoni et al, ) and not surprisingly of soil microbial biomass (Figure a). However, in such a case the C:N and C:P ratios are prescribed for each microbial community and the limited differences among sites are only dictated by variability in the proportion among bacteria, saprotrophic, and mycorrhizal fungi.…”

Section: Resultssupporting

confidence: 81%

A Mechanistic Model of Microbially Mediated Soil Biogeochemical Processes: A Reality Check

Fatichi

Manzoni

et al. 2019

Global Biogeochemical Cycles

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Present gaps in the representation of key soil biogeochemical processes such as the partitioning of soil organic carbon among functional components, microbial biomass and diversity, and the coupling of carbon and nutrient cycles present a challenge to improving the reliability of projected soil carbon dynamics. We introduce a new soil biogeochemistry module linked with a well‐tested terrestrial biosphere model T&C. The module explicitly distinguishes functional soil organic carbon components. Extracellular enzymes and microbial pools are differentiated based on the functional roles of bacteria, saprotrophic, and mycorrhizal fungi. Soil macrofauna is also represented. The model resolves the cycles of nitrogen, phosphorus, and potassium. Model simulations for 20 sites compared favorably with global patterns of litter and soil stoichiometry, microbial and macrofaunal biomass relations with soil organic carbon, soil respiration, and nutrient mineralization rates. Long‐term responses to bare fallow and nitrogen addition experiments were also in agreement with observations. Some discrepancies between predictions and observations are appreciable in the response to litter manipulation. Upon successful model reproduction of observed general trends, we assessed patterns associated with the carbon cycle that were challenging to address empirically. Despite large site‐to‐site variability, fine root, fungal, bacteria, and macrofaunal respiration account for 33%, 40%, 24%, and 3% on average of total belowground respiration, respectively. Simulated root exudation and carbon export to mycorrhizal fungi represent on average about 13% of plant net primary productivity. These results offer mechanistic and general estimates of microbial biomass and its contribution to respiration fluxes and to soil organic matter dynamics.

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Section: Resultssupporting

confidence: 81%

A Mechanistic Model of Microbially Mediated Soil Biogeochemical Processes: A Reality Check

Fatichi

Manzoni

et al. 2019

Global Biogeochemical Cycles

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“…15) as a first step towards linking the physicochemical soil properties to our model parameters, although the correlation was weak and some important parameters (Al or Fe in soils) are still missing. A similar exercise should be done for biological model parameters like CUE DOC , which for the moment do not reflect their known changes with vegetation or soil properties (Manzoni et al, 2017;Sinsabaugh et al, 2016). Also, the SOC diffusion coefficient was kept constant along the soil profile, although it is known that diffusion is higher in the upper soil layers and that biotic activity is controlled by the pH, among other factors (Jagercikova et al, 2014).…”

Section: Model Limitations and Further Workmentioning

confidence: 99%

ORCHIDEE-SOM: modeling soil organic carbon (SOC) and dissolved organic carbon (DOC) dynamics along vertical soil profiles in Europe

et al. 2018

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Abstract. Current land surface models (LSMs) typically represent soils in a very simplistic way, assuming soil organic carbon (SOC) as a bulk, and thus impeding a correct representation of deep soil carbon dynamics. Moreover, LSMs generally neglect the production and export of dissolved organic carbon (DOC) from soils to rivers, leading to overestimations of the potential carbon sequestration on land. This common oversimplified processing of SOC in LSMs is partly responsible for the large uncertainty in the predictions of the soil carbon response to climate change. In this study, we present a new soil carbon module called ORCHIDEE-SOM, embedded within the land surface model ORCHIDEE, which is able to reproduce the DOC and SOC dynamics in a vertically discretized soil to 2 m. The model includes processes of biological production and consumption of SOC and DOC, DOC adsorption on and desorption from soil minerals, diffusion of SOC and DOC, and DOC transport with water through and out of the soils to rivers. We evaluated ORCHIDEE-SOM against observations of DOC concentrations and SOC stocks from four European sites with different vegetation covers: a coniferous forest, a deciduous forest, a grassland, and a cropland. The model was able to reproduce the SOC stocks along their vertical profiles at the four sites and the DOC concentrations within the range of measurements, with the exception of the DOC concentrations in the upper soil horizon at the coniferous forest. However, the Published by Copernicus Publications on behalf of the European Geosciences Union. 938M. Camino-Serrano et al.: ORCHIDEE-SOM model was not able to fully capture the temporal dynamics of DOC concentrations. Further model improvements should focus on a plant-and depth-dependent parameterization of the new input model parameters, such as the turnover times of DOC and the microbial carbon use efficiency. We suggest that this new soil module, when parameterized for global simulations, will improve the representation of the global carbon cycle in LSMs, thus helping to constrain the predictions of the future SOC response to global warming.

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“…Existing datasets or data collections shown in previous publications are used for CUE of heterotrophic organisms (McNaughton et al, 1989;Manzoni et al, 2017), leaves (Atkin et al, 2015), plant communities (Luyssaert et al, 2007), whole-terrestrial (Luyssaert et al, 2007) and aquatic ecosystems (Hoellein et al, 2013), and for lacustrine and marine sediments (Alin and Johnson, 2007;Canfield, 1994). New literature data collections are developed for CUE of microbial isolates, individual plants, non-vascular vegetation, food 235 chains, soils, and watersheds.…”

Section: Data Collection and Analysismentioning

confidence: 99%

“…Next, we analyse how these efficiencies vary across scales and levels of organization, and how at the whole-ecosystem level, physico-chemical processes that lead to stabilisation or incomplete turn-over of organic matter become relevant to evaluate C retention. While previous syntheses have investigated the drivers of CUE patterns in specific systems (Canfield, 1994;del Giorgio and Cole, 1998;DeLucia et al, 2007;Manzoni et al, 2017;Sinsabaugh et al, 2015;Sterner 100 and Elser, 2002), we focus here on scale-dependencies of CUE and CSE across systems, and discuss the limitations that arise in the interpretation of efficiency values due to these scaling issues. Finally, we discuss the relevance of the trends we find in relation to our understanding of the C cycle, for informing ecosystem model development, and for overcoming disciplinary boundaries that led to numerous conceptually similar CUE definitions.…”

mentioning

confidence: 99%

Reviews and syntheses: Carbon use efficiency from organisms to ecosystems – Definitions, theories, and empirical evidence

Manzoni¹,

Čapek²,

Porada³

et al. 2018

Preprint

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Abstract. The cycling of carbon (C) between the Earth surface and the atmosphere is controlled by biological and abiotic processes that regulate C storage in biogeochemical compartments and release to the atmosphere. This partitioning is quantified using various forms of C-use efficiency (CUE) -the ratio of C remaining in a system 25 over C entering that system. Biological CUE is the fraction of C taken up allocated to new biomass. In soils and sediments C storage depends also on abiotic processes, so the term C-storage efficiency (CSE) can be used. Here we first review and reconcile CUE and CSE definitions proposed for autotrophic and heterotrophic organisms and communities, food webs, whole ecosystems, and soils and sediments using a common mathematical framework.Second, we identify general CUE patterns, such as the CUE increase with improving growing conditions, and 30 apparent decrease due to turnover. We then synthesize >6000 CUE estimates showing that CUE decreases with increasing biological and ecological organization -from unicellular to multicellular organisms, and from individuals to ecosystems. We conclude that CUE is an emergent property of coupled biological-abiotic systems, and it should be regarded as a flexible and scale-dependent index of the capacity of a given system to effectively retain C. 35

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Optimal metabolic regulation along resource stoichiometry gradients

Cited by 152 publications

References 51 publications

A Mechanistic Model of Microbially Mediated Soil Biogeochemical Processes: A Reality Check

A Mechanistic Model of Microbially Mediated Soil Biogeochemical Processes: A Reality Check

ORCHIDEE-SOM: modeling soil organic carbon (SOC) and dissolved organic carbon (DOC) dynamics along vertical soil profiles in Europe

Reviews and syntheses: Carbon use efficiency from organisms to ecosystems – Definitions, theories, and empirical evidence

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